Update model in opencode workflow to gpt-5.5

Merge branch 'dev' into lf-opencode-provider-workflow
Update opencode action to use latest version
2026-06-28 10:31:46 -05:00 · 2026-06-28 10:31:29 -05:00 · 2026-06-28 10:31:04 -05:00 · 2026-06-28 04:27:33 +00:00 · 2026-06-27 19:12:05 -05:00 · 2026-06-27 19:08:55 -05:00
2640 changed files with 8817 additions and 6570 deletions
@@ -20,8 +20,8 @@ jobs:
        uses: actions/checkout@v4

      - name: Run opencode
-        uses: sst/opencode/github@latest
+        uses: anomalyco/opencode/github@latest
        env:
-          ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
+          OPENCODE_API_KEY: ${{ secrets.OPENCODE_API_KEY }}
        with:
-          model: anthropic/claude-sonnet-4-20250514
+          model: opencode/gpt-5.5
@@ -64,8 +64,10 @@ jobs:
        run: bun models:sync ${{ matrix.provider }}
        env:
          BASETEN_API_KEY: ${{ secrets.BASETEN_API_KEY }}
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
          OPENROUTER_API_KEY: ${{ secrets.OPENROUTER_API_KEY }}
          VENICE_API_KEY: ${{ secrets.VENICE_API_KEY }}
+          LLMGATEWAY_API_KEY: ${{ secrets.LLMGATEWAY_API_KEY }}
          GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }}
          GEMINI_API_KEY: ${{ secrets.GEMINI_API_KEY }}
          GOOGLE_GENERATIVE_AI_API_KEY: ${{ secrets.GOOGLE_GENERATIVE_AI_API_KEY }}
@@ -5,6 +5,4 @@ dist
 .DS_Store
 .sync/
 node_modules
-data/tokenspeed-monitor.sqlite
-data/tokenspeed-monitor.sqlite-shm
-data/tokenspeed-monitor.sqlite-wal
+.opencode/package-lock.json
@@ -1,380 +0,0 @@
-{
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-      "optional": true,
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@@ -0,0 +1,164 @@
+---
+name: audit-reasoning-options
+description: Audit or write models.dev reasoning_options in provider TOML files and reasoning-option PRs. Use when verifying toggle, effort, budget_tokens, provider reasoning controls, or citations.
+---
+
+# Audit Reasoning Options
+
+Use this workflow to add or review `reasoning_options` for a specific provider. Treat these fields as provider capabilities, not provider-agnostic model facts.
+
+Provider capability means the inference service's accepted HTTP request surface. It does not mean the controls exposed by the repository's configured npm package, a preferred SDK, or a typed client wrapper.
+
+## Available Options
+
+The schema in `packages/core/src/schema.ts` supports:
+
+```toml
+[[reasoning_options]]
+type = "toggle"
+
+[[reasoning_options]]
+type = "effort"
+values = ["low", "medium", "high"]
+
+[[reasoning_options]]
+type = "budget_tokens"
+min = 1_024
+max = 32_000
+```
+
+- `toggle`: The provider offers an explicit way to switch reasoning on and off for the same model ID.
+- `effort`: The provider accepts one or more discrete effort values. Schema values are `null`, `none`, `minimal`, `low`, `medium`, `high`, `xhigh`, `max`, and `default`.
+- `budget_tokens`: The provider accepts a numeric reasoning-token budget. `min` and `max` are optional and must only be included when verified.
+- `reasoning_options = []`: The model reasons, but no user-selectable control was verified through this provider.
+- Omitted `reasoning_options`: No provider-specific claim has been authored. Do not treat omission as equivalent to an audited empty list.
+
+An option describes a control exposed to a caller. Do not add an option merely because a model reasons internally or another provider exposes that control.
+
+## Evidence Standard
+
+Use evidence in this order:
+
+1. The provider's current API reference or model documentation.
+2. The provider's raw OpenAPI schema, compatibility endpoint documentation, model endpoint metadata, or playground request payload.
+3. A reproducible request against the provider API, including a negative control with an invalid value where practical.
+4. The provider's official SDK source, but only as positive evidence for requests it emits.
+5. The upstream model developer's documentation.
+6. High-quality secondary sources only as supporting context.
+
+Provider documentation proves what the provider accepts. Upstream documentation proves what the model can support, but cannot by itself prove that a gateway forwards or exposes the control.
+
+An SDK can prove support when it emits a field. An SDK's omission, type restriction, or missing convenience option does not prove the inference API rejects that field. Before removing a control because an SDK cannot express it, inspect raw HTTP docs, compatibility base URLs, passthrough guarantees, migration guides, and direct API behavior.
+
+Prefer versioned or model-specific documentation over generic examples. Record the access date when a page is mutable or unversioned.
+
+## Audit Workflow
+
+1. Read the provider configuration to identify the API base URL and protocol. Record the SDK only as one possible client.
+2. Inspect the PR diff and list every changed model with its exact proposed options.
+3. Group models by API family or request adapter, not only by model developer.
+4. Locate provider documentation for reasoning request fields and model-specific restrictions.
+5. Check every raw compatibility endpoint the inference provider advertises, such as OpenAI-, Anthropic-, or provider-compatible base URLs. Existing calls working unchanged is positive evidence that native reasoning fields are accepted.
+6. Cross-check upstream model documentation for supported values and ranges after establishing provider passthrough or translation.
+7. Test the provider API when credentials are already available and documentation is incomplete. Never print credentials.
+8. Compare each TOML claim independently: toggle, each effort value, budget support, minimum, and maximum.
+9. Remove any claim that lacks inference-provider evidence. Do not remove it merely because one SDK lacks a type or helper.
+10. Run `bun validate` and `git diff --check`.
+11. Update the PR body with citations, request-field details, audit conclusions, and validation commands.
+
+## Toggle Verification
+
+Only add `toggle` if all of these are true:
+
+- The same provider model ID can run with reasoning enabled and disabled.
+- The caller controls the state through a documented or reproduced request.
+- The exact field and values are known.
+
+Examples of possible controls include `thinking.type = "enabled" | "disabled"`, `enable_thinking = true | false`, a documented `reasoning` object, or a provider-defined prompt switch such as `/think` and `/no_think`.
+
+The following do not prove a toggle:
+
+- Separate thinking and non-thinking model IDs.
+- Omitting a reasoning budget when omission selects an automatic budget.
+- Setting effort to `low` unless the provider says it disables reasoning.
+- A model card saying the model is hybrid without provider request documentation.
+- A provider UI switch when its API payload cannot be identified.
+
+For every proposed toggle, write this sentence before accepting it:
+
+> `<provider model ID>` toggles reasoning with `<request path>` set to `<enabled value>` or `<disabled value>`.
+
+If that sentence cannot be completed and cited or reproduced, do not claim `toggle`.
+
+## Effort Verification
+
+Verify every value separately. Do not copy the schema's full enum into a model.
+
+- For an OpenAI-compatible API, `low`, `medium`, and `high` are a useful investigation baseline, not proof.
+- Require explicit evidence for `null`, `none`, `minimal`, `xhigh`, `max`, and `default`.
+- Check model-specific differences. A generic gateway enum may be rejected or ignored by some routed models.
+- Distinguish accepted values from meaningful values. If the gateway silently ignores a field, it is not a supported control.
+- Preserve JSON `null` as TOML `null`, not the string `"null"`, when evidence requires a null value.
+
+When practical, send one valid request per claimed value and one invalid value. A structured `400` for the invalid value makes silent field dropping less likely.
+
+## Budget Verification
+
+`budget_tokens` is an abstract models.dev capability; providers may spell it `reasoning.max_tokens`, `thinking.budget_tokens`, `thinkingBudget`, or another field.
+
+- Cite the provider's actual request path.
+- Verify that the field controls reasoning tokens rather than total output tokens.
+- Do not infer `max` from `limit.output`, context length, or an upstream provider's limit.
+- Do not infer a provider minimum from an SDK default.
+- Omit unverified bounds while retaining verified budget support.
+- Check whether zero or a negative sentinel disables reasoning. If so, verify whether this also proves `toggle` for that model.
+- Check constraints relating budget to `max_tokens` or total output.
+
+## API Testing
+
+Use existing credentials only when permitted and necessary. Keep secrets out of commands, logs, files, PR bodies, and chat output.
+
+For each control, prefer this matrix:
+
+| Request | Expected evidence |
+| --- | --- |
+| No reasoning field | Establishes default behavior |
+| Each claimed valid value | Successful response or documented acceptance |
+| Explicit disabled value | Proves toggle-off behavior |
+| One invalid value | Structured rejection rather than silent dropping |
+| Boundary and adjacent value | Supports a claimed minimum or maximum |
+
+Acceptance alone is weak when an OpenAI-compatible gateway ignores unknown fields. Inspect returned metadata, reasoning content, usage fields, or error behavior where available.
+
+## Citations
+
+Put citations in the PR body, not TOML comments. TOML model files should remain data-only unless the repository establishes another convention.
+
+Use direct links to the narrowest authoritative section. For each link, state exactly what it proves:
+
+```markdown
+## Evidence
+
+- [Provider reasoning API](https://example.com/api/reasoning) documents
+  `reasoning_effort` values `low`, `medium`, and `high`.
+- [Provider model page](https://example.com/models/foo) documents that
+  `thinking.type = "disabled"` turns reasoning off for `foo`.
+- [Upstream model documentation](https://example.com/upstream/foo) confirms
+  the model-native budget range; provider requests at both boundaries succeeded.
+```
+
+Do not cite a search-results page, an AI-generated summary, or a generic upstream page for a provider-specific claim. If evidence comes from authenticated endpoint metadata or testing, describe the endpoint, date, request field, result, and negative control without including credentials or sensitive response data.
+
+## PR Audit Output
+
+For each audited PR, report:
+
+- Models and proposed options.
+- Verdict for every option: verified, corrected, or removed.
+- Exact toggle mechanism, when applicable.
+- Provider-level citations and what each proves.
+- Upstream citations used only for model-specific constraints.
+- Tests performed and their limitations.
+- Final validation result.
+
+If documentation is ambiguous, state the ambiguity and use the least permissive metadata supported by evidence.
@@ -1,7 +1,7 @@
-name = "Qwen3 32B FP8"
+name = "Qwen3.5 9B"
 family = "qwen"
-release_date = "2025-04-28"
-last_updated = "2025-04-28"
+release_date = "2026-02-23"
+last_updated = "2026-02-23"
 attachment = false
 reasoning = true
 temperature = true
@@ -9,14 +9,14 @@ tool_call = true
 structured_output = true
 open_weights = true

-[cost]
-input = 0.10
-output = 0.10
-
 [limit]
-context = 131_072
-output = 8_192
+context = 262_144
+output = 65_536

 [modalities]
 input = ["text"]
 output = ["text"]
+
+[[weights]]
+label = "Hugging Face"
+url = "https://huggingface.co/Qwen/Qwen3.5-9B"
@@ -1,7 +1,7 @@
 name = "Command R7B"
 family = "command-r"
-release_date = "2024-02-27"
-last_updated = "2024-02-27"
+release_date = "2024-12-02"
+last_updated = "2024-12-02"
 attachment = false
 reasoning = false
 temperature = true
@@ -0,0 +1,22 @@
+name = "Gemma 4 E2B IT"
+family = "gemma"
+release_date = "2026-04-02"
+last_updated = "2026-04-02"
+attachment = true
+reasoning = true
+temperature = true
+tool_call = true
+structured_output = true
+open_weights = true
+
+[limit]
+context = 131_072
+output = 8_192
+
+[modalities]
+input = ["text", "image", "audio"]
+output = ["text"]
+
+[[weights]]
+label = "Hugging Face"
+url = "https://huggingface.co/google/gemma-4-E2B-it"
@@ -0,0 +1,22 @@
+name = "Gemma 4 E4B IT"
+family = "gemma"
+release_date = "2026-04-02"
+last_updated = "2026-04-02"
+attachment = true
+reasoning = true
+temperature = true
+tool_call = true
+structured_output = true
+open_weights = true
+
+[limit]
+context = 131_072
+output = 8_192
+
+[modalities]
+input = ["text", "image", "audio"]
+output = ["text"]
+
+[[weights]]
+label = "Hugging Face"
+url = "https://huggingface.co/google/gemma-4-E4B-it"
@@ -6,7 +6,7 @@ attachment = true
 reasoning = true
 temperature = true
 tool_call = true
-open_weights = false
+open_weights = true

 [limit]
 context = 512_000
@@ -15,3 +15,7 @@ output = 128_000
 [modalities]
 input = ["text", "image", "video"]
 output = ["text"]
+
+[[weights]]
+label = "Hugging Face"
+url = "https://huggingface.co/MiniMaxAI/MiniMax-M3"
@@ -0,0 +1,23 @@
+name = "Kimi K2.7 Code Highspeed"
+family = "kimi-k2"
+release_date = "2026-06-12"
+last_updated = "2026-06-12"
+attachment = true
+reasoning = true
+temperature = false
+tool_call = true
+structured_output = true
+knowledge = "2025-01"
+open_weights = true
+
+[limit]
+context = 262_144
+output = 262_144
+
+[modalities]
+input = ["text", "image", "video"]
+output = ["text"]
+
+[[weights]]
+label = "Hugging Face"
+url = "https://huggingface.co/moonshotai/Kimi-K2.7-Code"
@@ -0,0 +1,17 @@
+name = "GPT-Image-1.5"
+family = "gpt-image"
+release_date = "2025-11-25"
+last_updated = "2025-11-25"
+attachment = true
+reasoning = false
+temperature = false
+tool_call = false
+open_weights = false
+
+[limit]
+context = 0
+output = 0
+
+[modalities]
+input = ["text", "image"]
+output = ["text", "image"]
@@ -0,0 +1,17 @@
+name = "GPT-Image-1"
+family = "gpt-image"
+release_date = "2025-04-24"
+last_updated = "2025-04-24"
+attachment = true
+reasoning = false
+temperature = false
+tool_call = false
+open_weights = false
+
+[limit]
+context = 0
+output = 0
+
+[modalities]
+input = ["text", "image"]
+output = ["image"]
@@ -0,0 +1,17 @@
+name = "GPT-Image-2"
+family = "gpt-image"
+release_date = "2026-04-21"
+last_updated = "2026-04-21"
+attachment = true
+reasoning = false
+temperature = false
+tool_call = false
+open_weights = false
+
+[limit]
+context = 0
+output = 0
+
+[modalities]
+input = ["text", "image"]
+output = ["image"]
@@ -1,22 +1,22 @@
-name = "GPT OSS 20B"
+name = "GPT OSS 120B"
 family = "gpt-oss"
 release_date = "2025-08-05"
 last_updated = "2025-08-05"
 attachment = false
 reasoning = true
-reasoning_options = [{ type = "effort", values = ["low", "medium", "high"] }]
 temperature = true
 tool_call = true
+structured_output = true
 open_weights = true

-[cost]
-input = 0.03
-output = 0.16
-
 [limit]
-context = 16_368
-output = 16_368
+context = 131_072
+output = 32_768

 [modalities]
 input = ["text"]
 output = ["text"]
+
+[[weights]]
+label = "Hugging Face"
+url = "https://huggingface.co/openai/gpt-oss-120b"
@@ -0,0 +1,22 @@
+name = "GPT OSS Safeguard 120B"
+family = "gpt-oss"
+release_date = "2025-10-29"
+last_updated = "2025-10-29"
+attachment = false
+reasoning = true
+temperature = true
+tool_call = true
+structured_output = true
+open_weights = true
+
+[limit]
+context = 131_072
+output = 32_768
+
+[modalities]
+input = ["text"]
+output = ["text"]
+
+[[weights]]
+label = "Hugging Face"
+url = "https://huggingface.co/openai/gpt-oss-safeguard-120b"
@@ -0,0 +1,16 @@
+name = "Whisper Large v3 Turbo"
+family = "whisper"
+release_date = "2024-10-01"
+last_updated = "2024-10-01"
+attachment = false
+reasoning = false
+tool_call = false
+open_weights = true
+
+[limit]
+context = 448
+output = 448
+
+[modalities]
+input = ["audio"]
+output = ["text"]
@@ -0,0 +1,16 @@
+name = "Whisper 3 Large"
+family = "whisper"
+release_date = "2024-10-01"
+last_updated = "2024-10-01"
+attachment = false
+reasoning = false
+tool_call = false
+open_weights = true
+
+[limit]
+context = 448
+output = 4_096
+
+[modalities]
+input = ["audio"]
+output = ["text"]
@@ -1,7 +1,7 @@
 name = "GLM-5.1"
 family = "glm"
-release_date = "2026-03-27"
-last_updated = "2026-03-27"
+release_date = "2026-04-07"
+last_updated = "2026-04-07"
 attachment = false
 reasoning = true
 temperature = true
@@ -7,7 +7,7 @@ reasoning = true
 temperature = true
 tool_call = true
 structured_output = true
-open_weights = false
+open_weights = true

 [limit]
 context = 1_000_000
@@ -16,3 +16,7 @@ output = 131_072
 [modalities]
 input = ["text"]
 output = ["text"]
+
+[[weights]]
+label = "Hugging Face"
+url = "https://huggingface.co/zai-org/GLM-5.2"
@@ -1,7 +1,7 @@
 name = "GLM-5"
 family = "glm"
-release_date = "2026-02-11"
-last_updated = "2026-02-11"
+release_date = "2026-02-12"
+last_updated = "2026-02-12"
 attachment = false
 reasoning = true
 temperature = true
@@ -20,9 +20,11 @@
    "compare:migrations": "bun ./packages/core/script/compare-model-migrations.ts",
    "baseten:sync": "bun ./packages/core/script/sync-models.ts baseten",
    "cloudflare:sync": "bun ./packages/core/script/sync-models.ts cloudflare-workers-ai",
-    "chutes:generate": "bun ./packages/core/script/generate-chutes.ts",
+    "chutes:sync": "bun ./packages/core/script/sync-models.ts chutes",
    "databricks:generate": "bun ./packages/core/script/generate-databricks.ts",
    "helicone:generate": "bun ./packages/core/script/generate-helicone.ts",
+    "huggingface:sync": "bun ./packages/core/script/sync-models.ts huggingface",
+    "llmgateway:sync": "bun ./packages/core/script/sync-models.ts llmgateway",
    "venice:sync": "bun ./packages/core/script/sync-models.ts venice",
    "vercel:generate": "bun ./packages/core/script/sync-models.ts vercel",
    "wandb:generate": "bun ./packages/core/script/generate-wandb.ts",
@@ -1,891 +0,0 @@
-#!/usr/bin/env bun
-
-/**
- * Generates Chutes model TOML files from the Chutes LLM API.
- *
- * Flags:
- * --dry-run: Preview changes without writing files
- * --new-only: Only create new models, skip updating existing ones
- * --keep-orphans: Don't delete TOML files for models no longer in the API
- */
-
-import { z } from "zod";
-import path from "node:path";
-import { existsSync, readFileSync } from "node:fs";
-import { mkdir } from "node:fs/promises";
-import { inferKimiFamily, ModelFamilyValues } from "../src/family.js";
-
-const API_ENDPOINT = "https://llm.chutes.ai/v1/models";
-const MODEL_METADATA_DIR = path.join(import.meta.dirname, "..", "..", "..", "models");
-
-const CHUTES_ORG_TO_MODEL_PROVIDER: Record<string, string | undefined> = {
-  "MiniMaxAI": "minimax",
-  "Qwen": "alibaba",
-  "XiaomiMiMo": "xiaomi",
-  "deepseek-ai": "deepseek",
-  "google": "google",
-  "moonshotai": "moonshotai",
-  "openai": "openai",
-  "zai-org": "zhipuai",
-};
-
-const BASE_MODEL_ALIASES: Record<string, string | undefined> = {
-  "google/gemma-4-31B-turbo-TEE": "google/gemma-4-31b-it",
-  "Qwen/Qwen3-235B-A22B-Instruct-2507-TEE": "alibaba/qwen3-235b-a22b",
-};
-
-const modelMetadataByID = new Map<string, Record<string, unknown>>();
-
-enum SkipZeroFields {
-  LimitContext = "limit.context",
-  LimitOutput = "limit.output",
-}
-
-const Pricing = z.object({
-  prompt: z.number().optional(),
-  completion: z.number().optional(),
-  input_cache_read: z.number().optional(),
-}).passthrough();
-
-const ChutesModel = z.object({
-  id: z.string(),
-  created: z.number(),
-  pricing: Pricing.optional(),
-  context_length: z.number().optional(),
-  max_output_length: z.number().optional(),
-  max_model_len: z.number().optional(),
-  input_modalities: z.array(z.string()).optional(),
-  output_modalities: z.array(z.string()).optional(),
-  supported_features: z.array(z.string()).optional(),
-  supported_sampling_parameters: z.array(z.string()).optional(),
-  quantization: z.string().optional(),
-}).passthrough();
-
-const ChutesResponse = z.object({
-  data: z.array(ChutesModel),
-}).passthrough();
-
-interface ExistingModel {
-  base_model?: string;
-  base_model_omit?: string[];
-  name?: string;
-  family?: string;
-  attachment?: boolean;
-  reasoning?: boolean;
-  tool_call?: boolean;
-  structured_output?: boolean;
-  temperature?: boolean;
-  knowledge?: string;
-  release_date?: string;
-  last_updated?: string;
-  open_weights?: boolean;
-  interleaved?: boolean | { field: string };
-  status?: string;
-  cost?: {
-    input?: number;
-    output?: number;
-    cache_read?: number;
-  };
-  limit?: {
-    context?: number;
-    output?: number;
-  };
-  modalities?: {
-    input?: string[];
-    output?: string[];
-  };
-}
-
-interface MergedModel {
-  base_model?: string;
-  base_model_omit?: string[];
-  name: string;
-  family?: string;
-  attachment: boolean;
-  reasoning: boolean;
-  tool_call: boolean;
-  structured_output: boolean;
-  temperature: boolean;
-  knowledge?: string;
-  release_date: string;
-  last_updated: string;
-  open_weights: boolean;
-  interleaved?: boolean | { field: string };
-  status?: string;
-  cost?: {
-    input: number;
-    output: number;
-    cache_read?: number;
-  };
-  limit: {
-    context: number;
-    output: number;
-  };
-  modalities: {
-    input: string[];
-    output: string[];
-  };
-}
-
-interface Changes {
-  field: string;
-  oldValue: string;
-  newValue: string;
-}
-
-// ── Utility functions ────────────────────────────────────────────────
-
-function timestampToDate(timestamp: number): string {
-  const date = new Date(timestamp * 1000);
-  return date.toISOString().slice(0, 10);
-}
-
-function getTodayDate(): string {
-  return new Date().toISOString().slice(0, 10);
-}
-
-function formatNumber(n: number): string {
-  if (n >= 1000) {
-    return n.toString().replace(/\B(?=(\d{3})+(?!\d))/g, "_");
-  }
-  return n.toString();
-}
-
-/**
- * Humanize a model ID into a readable name.
- * Strips the org prefix and replaces hyphens with spaces.
- * e.g. "Qwen/Qwen3-32B-TEE" → "Qwen3 32B TEE"
- */
-function humanizeModelName(modelId: string): string {
-  const parts = modelId.split("/");
-  const modelPart = parts.at(-1) ?? modelId;
-  return modelPart.replace(/-/g, " ");
-}
-
-function modelMetadataPath(modelId: string): string {
-  return path.join(MODEL_METADATA_DIR, `${modelId}.toml`);
-}
-
-function modelMetadataExists(modelId: string): boolean {
-  return existsSync(modelMetadataPath(modelId));
-}
-
-function modelMetadata(modelId: string): Record<string, unknown> {
-  let metadata = modelMetadataByID.get(modelId);
-  if (metadata === undefined) {
-    metadata = Bun.TOML.parse(
-      readFileSync(modelMetadataPath(modelId), "utf8"),
-    ) as Record<string, unknown>;
-    modelMetadataByID.set(modelId, metadata);
-  }
-  return metadata;
-}
-
-function baseModelCandidates(modelId: string): string[] {
-  const alias = BASE_MODEL_ALIASES[modelId];
-  const [org, ...modelParts] = modelId.split("/");
-  if (org === undefined || modelParts.length === 0) {
-    return alias === undefined ? [] : [alias];
-  }
-
-  const provider = CHUTES_ORG_TO_MODEL_PROVIDER[org];
-  if (provider === undefined) {
-    return alias === undefined ? [] : [alias];
-  }
-
-  const rawModel = modelParts.join("/");
-  if (!rawModel.endsWith("-TEE")) {
-    return alias === undefined ? [] : [alias];
-  }
-
-  const withoutTee = rawModel.slice(0, -"-TEE".length);
-  const lower = withoutTee.toLowerCase();
-  const normalized = [
-    withoutTee,
-    lower,
-    lower.replace(/-(?:instruct|thinking)-\d{4}$/, ""),
-    lower.replace(/-\d{4}$/, ""),
-    lower.replace(/-turbo$/, "-it"),
-    lower.replace(/-turbo$/, ""),
-  ];
-
-  return [...new Set([alias, ...normalized.map((candidate) => `${provider}/${candidate}`)])
-    .values()].filter((candidate): candidate is string => candidate !== undefined);
-}
-
-function resolveBaseModel(modelId: string, existing: ExistingModel | null): string | undefined {
-  const candidates = [
-    existing?.base_model,
-    ...baseModelCandidates(modelId),
-  ].filter((candidate): candidate is string => candidate !== undefined);
-
-  return candidates.find(modelMetadataExists);
-}
-
-function resolveBaseModelOmit(
-  baseModel: string | undefined,
-  existing: ExistingModel | null,
-): string[] | undefined {
-  const omit = new Set(existing?.base_model_omit ?? []);
-  if (baseModel !== undefined) {
-    const baseLimit = modelMetadata(baseModel).limit;
-    if (
-      isPlainObject(baseLimit) &&
-      baseLimit.input !== undefined
-    ) {
-      omit.add("limit.input");
-    }
-  }
-
-  return omit.size > 0 ? [...omit].sort() : undefined;
-}
-
-// ── Family inference ───────────
-
-function isSubstring(target: string, family: string): boolean {
-  return target.toLowerCase().includes(family.toLowerCase());
-}
-
-function matchesFamily(target: string, family: string): boolean {
-  const targetLower = target.toLowerCase();
-  const familyLower = family.toLowerCase();
-  let familyIdx = 0;
-
-  for (let i = 0; i < targetLower.length && familyIdx < familyLower.length; i++) {
-    if (targetLower[i] === familyLower[familyIdx]) {
-      familyIdx++;
-    }
-  }
-
-  return familyIdx === familyLower.length;
-}
-
-function inferFamily(modelId: string, modelName: string): string | undefined {
-  const kimiFamily = inferKimiFamily(modelId, modelName);
-  if (kimiFamily !== undefined) return kimiFamily;
-
-  const sortedFamilies = [...ModelFamilyValues].sort((a, b) => b.length - a.length);
-
-  // First pass: try exact substring matches
-  for (const family of sortedFamilies) {
-    if (isSubstring(modelId, family)) {
-      return family;
-    }
-  }
-
-  for (const family of sortedFamilies) {
-    if (isSubstring(modelName, family)) {
-      return family;
-    }
-  }
-
-  // Second pass: fall back to subsequence matching
-  for (const family of sortedFamilies) {
-    if (matchesFamily(modelId, family)) {
-      return family;
-    }
-  }
-
-  for (const family of sortedFamilies) {
-    if (matchesFamily(modelName, family)) {
-      return family;
-    }
-  }
-
-  return undefined;
-}
-
-// ── Load existing TOML ───────────────────────────────────────────────
-
-async function loadExistingModel(filePath: string): Promise<ExistingModel | null> {
-  try {
-    const file = Bun.file(filePath);
-    if (!(await file.exists())) {
-      return null;
-    }
-    const toml = await import(filePath, { with: { type: "toml" } }).then(
-      (mod) => mod.default,
-    );
-    return toml as ExistingModel;
-  } catch (e) {
-    console.warn(`Warning: Failed to parse existing file ${filePath}:`, e);
-    return null;
-  }
-}
-
-// ── Merge API data with existing TOML ────────────────────────────────
-
-function mergeModel(
-  apiModel: z.infer<typeof ChutesModel>,
-  existing: ExistingModel | null,
-): MergedModel {
-  const features = new Set(apiModel.supported_features ?? []);
-  const samplingParams = new Set(apiModel.supported_sampling_parameters ?? []);
-  const inputMods = apiModel.input_modalities ?? ["text"];
-  const outputMods = apiModel.output_modalities ?? ["text"];
-
-  // Capabilities from API features
-  const hasAttachment = inputMods.some((m) =>
-    m === "image" || m === "video" || m === "pdf",
-  );
-  const hasReasoning = features.has("reasoning");
-  const hasToolCall = features.has("tools");
-  const hasStructuredOutput = features.has("structured_outputs");
-  const hasTemperature = samplingParams.size > 0
-    ? samplingParams.has("temperature")
-    : true; // default true if no sampling params info
-
-  // Preserve existing values when available (manually specified)
-  const modelName = existing?.name ?? humanizeModelName(apiModel.id);
-  const family = existing?.family ?? inferFamily(apiModel.id, modelName);
-  const knowledge = existing?.knowledge;
-  const interleaved = existing?.interleaved;
-  const status = existing?.status;
-  const baseModel = resolveBaseModel(apiModel.id, existing);
-  const baseModelOmit = resolveBaseModelOmit(baseModel, existing);
-
-  // Release date: existing > API created timestamp > today
-  const releaseDate = existing?.release_date
-    ?? timestampToDate(apiModel.created)
-    ?? getTodayDate();
-
-  // Context limit: prefer context_length, fallback to max_model_len
-  const apiContext = apiModel.context_length ?? apiModel.max_model_len ?? 0;
-  const contextLimit = apiContext > 0
-    ? apiContext
-    : (existing?.limit?.context ?? 0);
-
-  // Output limit: prefer max_output_length, fallback to existing
-  const apiOutput = apiModel.max_output_length ?? 0;
-  const outputLimit = apiOutput > 0
-    ? apiOutput
-    : (existing?.limit?.output ?? 0);
-
-  const merged: MergedModel = {
-    ...(baseModel !== undefined && { base_model: baseModel }),
-    ...(baseModelOmit !== undefined && { base_model_omit: baseModelOmit }),
-    name: modelName,
-    family,
-    attachment: hasAttachment,
-    reasoning: hasReasoning,
-    tool_call: hasToolCall,
-    temperature: hasTemperature,
-    structured_output: hasStructuredOutput,
-    release_date: releaseDate,
-    last_updated: getTodayDate(),
-    open_weights: true, // Chutes hosts open-weight models
-    ...(knowledge && { knowledge }),
-    ...(interleaved !== undefined && { interleaved }),
-    ...(status && { status }),
-    limit: {
-      context: contextLimit,
-      output: outputLimit,
-    },
-    modalities: {
-      input: inputMods,
-      output: outputMods,
-    },
-  };
-
-  // Cost: API values are already in USD per 1M tokens — use directly
-  if (apiModel.pricing) {
-    const inputPrice = apiModel.pricing.prompt;
-    const outputPrice = apiModel.pricing.completion;
-    const cacheReadPrice = apiModel.pricing.input_cache_read;
-
-    if (inputPrice !== undefined && outputPrice !== undefined) {
-      merged.cost = {
-        input: inputPrice,
-        output: outputPrice,
-        ...(cacheReadPrice !== undefined && { cache_read: cacheReadPrice }),
-      };
-    }
-  }
-
-  return merged;
-}
-
-// ── TOML formatting ──────────────────────────────────────────────────
-
-function formatToml(model: MergedModel): string {
-  if (model.base_model !== undefined) {
-    return formatBaseModelToml(model);
-  }
-
-  return formatFullToml(model);
-}
-
-function formatFullToml(model: MergedModel): string {
-  const lines: string[] = [];
-
-  lines.push(`# Auto-generated by generate-chutes.ts — do not edit pricing, limits, or capabilities.`);
-  lines.push(`# Manual overrides preserved on re-run: name, family, knowledge, interleaved, status`);
-  lines.push(`name = "${model.name.replace(/"/g, '\\"')}"`);
-  if (model.family) {
-    lines.push(`family = "${model.family}"`);
-  }
-  lines.push(`release_date = "${model.release_date}"`);
-  lines.push(`last_updated = "${model.last_updated}"`);
-  lines.push(`attachment = ${model.attachment}`);
-  lines.push(`reasoning = ${model.reasoning}`);
-  lines.push(`temperature = ${model.temperature}`);
-  lines.push(`tool_call = ${model.tool_call}`);
-  if (model.structured_output) {
-    lines.push(`structured_output = ${model.structured_output}`);
-  }
-  lines.push(`open_weights = ${model.open_weights}`);
-  if (model.knowledge) {
-    lines.push(`knowledge = "${model.knowledge}"`);
-  }
-  if (model.status) {
-    lines.push(`status = "${model.status}"`);
-  }
-
-  if (model.cost) {
-    lines.push("");
-    lines.push(`[cost]`);
-    lines.push(`input = ${model.cost.input}`);
-    lines.push(`output = ${model.cost.output}`);
-    if (model.cost.cache_read !== undefined) {
-      lines.push(`cache_read = ${model.cost.cache_read}`);
-    }
-  }
-
-  lines.push("");
-  lines.push(`[limit]`);
-  lines.push(`context = ${formatNumber(model.limit.context)}`);
-  lines.push(`output = ${formatNumber(model.limit.output)}`);
-
-  lines.push("");
-  lines.push(`[modalities]`);
-  lines.push(`input = [${model.modalities.input.map((m) => `"${m}"`).join(", ")}]`);
-  lines.push(`output = [${model.modalities.output.map((m) => `"${m}"`).join(", ")}]`);
-
-  if (model.interleaved !== undefined) {
-    lines.push("");
-    if (model.interleaved === true) {
-      lines.push(`interleaved = true`);
-    } else if (typeof model.interleaved === "object") {
-      lines.push(`[interleaved]`);
-      lines.push(`field = "${model.interleaved.field}"`);
-    }
-  }
-
-  return lines.join("\n") + "\n";
-}
-
-function formatBaseModelToml(model: MergedModel): string {
-  const lines: string[] = [];
-  const overrides = baseModelOverrides(model);
-
-  lines.push(`# Auto-generated by generate-chutes.ts — do not edit pricing, limits, or capabilities.`);
-  lines.push(`# Manual overrides preserved on re-run: name, family, knowledge, interleaved, status`);
-  lines.push(`base_model = "${model.base_model}"`);
-  if (model.base_model_omit !== undefined) {
-    lines.push(
-      `base_model_omit = [${model.base_model_omit.map((item) => `"${item}"`).join(", ")}]`,
-    );
-  }
-  if (overrides.name !== undefined) {
-    lines.push(`name = "${String(overrides.name).replace(/"/g, '\\"')}"`);
-  }
-  for (const field of [
-    "attachment",
-    "reasoning",
-    "structured_output",
-    "temperature",
-    "tool_call",
-    "open_weights",
-  ] as const) {
-    const value = overrides[field];
-    if (value !== undefined) {
-      lines.push(`${field} = ${value}`);
-    }
-  }
-  if (overrides.knowledge !== undefined) {
-    lines.push(`knowledge = "${overrides.knowledge}"`);
-  }
-  if (overrides.status !== undefined) {
-    lines.push(`status = "${overrides.status}"`);
-  }
-
-  if (overrides.interleaved !== undefined) {
-    lines.push("");
-    if (overrides.interleaved === true) {
-      lines.push(`interleaved = true`);
-    } else if (isPlainObject(overrides.interleaved)) {
-      lines.push(`[interleaved]`);
-      lines.push(`field = "${overrides.interleaved.field}"`);
-    }
-  }
-
-  if (model.cost) {
-    lines.push("");
-    lines.push(`[cost]`);
-    lines.push(`input = ${model.cost.input}`);
-    lines.push(`output = ${model.cost.output}`);
-    if (model.cost.cache_read !== undefined) {
-      lines.push(`cache_read = ${model.cost.cache_read}`);
-    }
-  }
-
-  lines.push("");
-  lines.push(`[limit]`);
-  lines.push(`context = ${formatNumber(model.limit.context)}`);
-  lines.push(`output = ${formatNumber(model.limit.output)}`);
-
-  if (overrides.modalities !== undefined && isPlainObject(overrides.modalities)) {
-    const input = overrides.modalities.input;
-    const output = overrides.modalities.output;
-    if (Array.isArray(input) && Array.isArray(output)) {
-      lines.push("");
-      lines.push(`[modalities]`);
-      lines.push(`input = [${input.map((m) => `"${m}"`).join(", ")}]`);
-      lines.push(`output = [${output.map((m) => `"${m}"`).join(", ")}]`);
-    }
-  }
-
-  return lines.join("\n") + "\n";
-}
-
-function baseModelOverrides(model: MergedModel): Record<string, unknown> {
-  if (model.base_model === undefined) {
-    return {};
-  }
-
-  const metadata = modelMetadata(model.base_model);
-  const values: Record<string, unknown> = {
-    name: model.name,
-    attachment: model.attachment,
-    reasoning: model.reasoning,
-    structured_output:
-      model.structured_output || metadata.structured_output === true
-        ? model.structured_output
-        : undefined,
-    temperature: model.temperature,
-    tool_call: model.tool_call,
-    knowledge: model.knowledge,
-    open_weights: model.open_weights,
-    status: model.status,
-    interleaved: model.interleaved,
-    modalities: model.modalities,
-  };
-
-  return Object.fromEntries(
-    Object.entries(values)
-      .map(([key, value]) => [key, inheritedOverride(value, metadata[key])])
-      .filter(([, value]) => value !== undefined),
-  );
-}
-
-function inheritedOverride(value: unknown, inherited: unknown): unknown {
-  if (value === undefined) return undefined;
-  if (sameInheritedValue(value, inherited)) return undefined;
-  return stripUndefined(value);
-}
-
-function stripUndefined(value: unknown): unknown {
-  if (Array.isArray(value)) return value.map(stripUndefined);
-  if (isPlainObject(value)) {
-    return Object.fromEntries(
-      Object.entries(value)
-        .filter(([, item]) => item !== undefined)
-        .map(([key, item]) => [key, stripUndefined(item)]),
-    );
-  }
-  return value;
-}
-
-function sameInheritedValue(value: unknown, inherited: unknown): boolean {
-  return stableInheritedValue(value) === stableInheritedValue(inherited);
-}
-
-function stableInheritedValue(value: unknown): string {
-  if (Array.isArray(value)) {
-    const items = value.map(stableInheritedValue);
-    const ordered = value.every((item) => item === null || typeof item !== "object")
-      ? items.sort()
-      : items;
-    return `[${ordered.join(",")}]`;
-  }
-  if (isPlainObject(value)) {
-    return `{${Object.entries(value)
-      .filter(([, item]) => item !== undefined)
-      .sort(([a], [b]) => a.localeCompare(b))
-      .map(([key, item]) => `${JSON.stringify(key)}:${stableInheritedValue(item)}`)
-      .join(",")}}`;
-  }
-  return JSON.stringify(value);
-}
-
-function isPlainObject(value: unknown): value is Record<string, unknown> {
-  return value !== null && typeof value === "object" && !Array.isArray(value);
-}
-
-// ── Change detection ─────────────────────────────────────────────────
-
-function detectChanges(
-  existing: ExistingModel | null,
-  merged: MergedModel,
-): Changes[] {
-  if (!existing) return [];
-
-  const changes: Changes[] = [];
-  const EPSILON = 0.001;
-
-  const shouldSkipZero = (field: string, oldVal: unknown, newVal: unknown): boolean => {
-    if (!Object.values(SkipZeroFields).includes(field as SkipZeroFields)) {
-      return false;
-    }
-    return (typeof oldVal === "number" && oldVal === 0) || (typeof newVal === "number" && newVal === 0);
-  };
-
-  const formatValue = (val: unknown): string => {
-    if (typeof val === "number") return formatNumber(val);
-    if (Array.isArray(val)) return `[${val.join(", ")}]`;
-    if (val === undefined) return "(none)";
-    return String(val);
-  };
-
-  const isMaterialPriceDiff = (oldPrice: unknown, newPrice: unknown): boolean => {
-    if (oldPrice === 0 && newPrice === undefined) return false;
-    if (oldPrice !== undefined && newPrice !== undefined) {
-      return Math.abs((oldPrice as number) - (newPrice as number)) > EPSILON;
-    }
-    return oldPrice !== newPrice;
-  };
-
-  const compare = (field: string, oldVal: unknown, newVal: unknown) => {
-    if (shouldSkipZero(field, oldVal, newVal)) return;
-
-    const isDiff = field.startsWith("cost.")
-      ? isMaterialPriceDiff(oldVal, newVal)
-      : JSON.stringify(oldVal) !== JSON.stringify(newVal);
-
-    if (isDiff) {
-      changes.push({
-        field,
-        oldValue: formatValue(oldVal),
-        newValue: formatValue(newVal),
-      });
-    }
-  };
-
-  if (merged.base_model !== undefined) {
-    const overrides = baseModelOverrides(merged);
-
-    compare("base_model", existing.base_model, merged.base_model);
-    compare("base_model_omit", existing.base_model_omit, merged.base_model_omit);
-    compare("name", existing.name, overrides.name);
-    compare("attachment", existing.attachment, overrides.attachment);
-    compare("reasoning", existing.reasoning, overrides.reasoning);
-    compare("tool_call", existing.tool_call, overrides.tool_call);
-    compare(
-      "structured_output",
-      existing.structured_output ?? false,
-      overrides.structured_output ?? false,
-    );
-    compare("temperature", existing.temperature, overrides.temperature);
-    compare("open_weights", existing.open_weights, overrides.open_weights);
-    compare("knowledge", existing.knowledge, overrides.knowledge);
-    compare("status", existing.status, overrides.status);
-    compare("interleaved", existing.interleaved, overrides.interleaved);
-    compare("cost.input", existing.cost?.input, merged.cost?.input);
-    compare("cost.output", existing.cost?.output, merged.cost?.output);
-    compare("cost.cache_read", existing.cost?.cache_read, merged.cost?.cache_read);
-    compare("limit.context", existing.limit?.context, merged.limit.context);
-    compare("limit.output", existing.limit?.output, merged.limit.output);
-    if (isPlainObject(overrides.modalities)) {
-      compare("modalities.input", existing.modalities?.input, overrides.modalities.input);
-      compare("modalities.output", existing.modalities?.output, overrides.modalities.output);
-    } else {
-      compare("modalities.input", existing.modalities?.input, undefined);
-      compare("modalities.output", existing.modalities?.output, undefined);
-    }
-
-    return changes;
-  }
-
-  compare("name", existing.name, merged.name);
-  compare("base_model", existing.base_model, merged.base_model);
-  compare("base_model_omit", existing.base_model_omit, merged.base_model_omit);
-  compare("family", existing.family, merged.family);
-  compare("attachment", existing.attachment, merged.attachment);
-  compare("reasoning", existing.reasoning, merged.reasoning);
-  compare("tool_call", existing.tool_call, merged.tool_call);
-  compare("structured_output", existing.structured_output ?? false, merged.structured_output);
-  compare("open_weights", existing.open_weights, merged.open_weights);
-  compare("release_date", existing.release_date, merged.release_date);
-  compare("cost.input", existing.cost?.input, merged.cost?.input);
-  compare("cost.output", existing.cost?.output, merged.cost?.output);
-  compare("cost.cache_read", existing.cost?.cache_read, merged.cost?.cache_read);
-  compare("limit.context", existing.limit?.context, merged.limit.context);
-  compare("limit.output", existing.limit?.output, merged.limit.output);
-  compare("modalities.input", existing.modalities?.input, merged.modalities.input);
-  compare("modalities.output", existing.modalities?.output, merged.modalities.output);
-
-  return changes;
-}
-
-// ── Main ─────────────────────────────────────────────────────────────
-
-async function main() {
-  const args = process.argv.slice(2);
-  const dryRun = args.includes("--dry-run");
-  const newOnly = args.includes("--new-only");
-  const keepOrphans = args.includes("--keep-orphans");
-
-  const modelsDir = path.join(
-    import.meta.dirname,
-    "..",
-    "..",
-    "..",
-    "providers",
-    "chutes",
-    "models",
-  );
-
-  console.log(`${dryRun ? "[DRY RUN] " : ""}${newOnly ? "[NEW ONLY] " : ""}${keepOrphans ? "[KEEP ORPHANS] " : ""}Fetching Chutes models from API...`);
-
-  const res = await fetch(API_ENDPOINT);
-  if (!res.ok) {
-    console.error(`Failed to fetch API: ${res.status} ${res.statusText}`);
-    process.exit(1);
-  }
-
-  const json = await res.json();
-  const parsed = ChutesResponse.safeParse(json);
-  if (!parsed.success) {
-    console.error("Invalid API response:", parsed.error.errors);
-    process.exit(1);
-  }
-
-  const apiModels = parsed.data.data;
-
-  // Scan existing TOML files
-  const existingFiles = new Set<string>();
-  try {
-    for await (const file of new Bun.Glob("**/*.toml").scan({
-      cwd: modelsDir,
-      absolute: false,
-    })) {
-      existingFiles.add(file);
-    }
-  } catch {
-  }
-
-  console.log(`Found ${apiModels.length} models in API, ${existingFiles.size} existing files\n`);
-
-  const apiModelIds = new Set<string>();
-
-  let created = 0;
-  let updated = 0;
-  let unchanged = 0;
-
-  for (const apiModel of apiModels) {
-    const relativePath = `${apiModel.id}.toml`;
-    const filePath = path.join(modelsDir, relativePath);
-    const dirPath = path.dirname(filePath);
-
-    apiModelIds.add(relativePath);
-
-    const existing = await loadExistingModel(filePath);
-    const merged = mergeModel(apiModel, existing);
-    const tomlContent = formatToml(merged);
-
-    if (existing === null) {
-      created++;
-      if (dryRun) {
-        console.log(`[DRY RUN] Would create: ${relativePath}`);
-        console.log(`  name = "${merged.name}"`);
-        if (merged.family) {
-          console.log(`  family = "${merged.family}" (inferred)`);
-        }
-        console.log("");
-      } else {
-        await mkdir(dirPath, { recursive: true });
-        await Bun.write(filePath, tomlContent);
-        console.log(`Created: ${relativePath}`);
-      }
-    } else {
-      if (newOnly) {
-        unchanged++;
-        continue;
-      }
-
-      const changes = detectChanges(existing, merged);
-      const existingContent = await Bun.file(filePath).text();
-      const formatChanged = existingContent !== tomlContent;
-
-      if (changes.length > 0 || formatChanged) {
-        updated++;
-        if (dryRun) {
-          console.log(`[DRY RUN] Would update: ${relativePath}`);
-        } else {
-          await mkdir(dirPath, { recursive: true });
-          await Bun.write(filePath, tomlContent);
-          console.log(`Updated: ${relativePath}`);
-        }
-        for (const change of changes) {
-          console.log(`  ${change.field}: ${change.oldValue} → ${change.newValue}`);
-        }
-        if (changes.length === 0 && formatChanged) {
-          console.log(`  (format-only change)`);
-        }
-        console.log("");
-      } else {
-        unchanged++;
-      }
-    }
-  }
-
-  // Handle orphaned files (on disk but not in API)
-  const orphaned: string[] = [];
-  for (const file of existingFiles) {
-    if (!apiModelIds.has(file)) {
-      orphaned.push(file);
-      const orphanPath = path.join(modelsDir, file);
-      if (keepOrphans) {
-        console.log(`Orphaned (kept): ${file}`);
-      } else if (dryRun) {
-        console.log(`[DRY RUN] Would delete: ${file}`);
-      } else {
-        await Bun.file(orphanPath).delete();
-        console.log(`Deleted: ${file}`);
-
-        // Clean up empty parent directories
-        const parentDir = path.dirname(orphanPath);
-        try {
-          const remaining = [];
-          for await (const entry of new Bun.Glob("*").scan({ cwd: parentDir })) {
-            remaining.push(entry);
-          }
-          if (remaining.length === 0) {
-            const { rmdir } = await import("node:fs/promises");
-            await rmdir(parentDir);
-            console.log(`  Removed empty directory: ${path.basename(parentDir)}/`);
-          }
-        } catch {
-          // Directory not empty or other error, ignore
-        }
-      }
-    }
-  }
-
-  console.log("");
-  if (dryRun) {
-    console.log(
-      `Summary: ${created} would be created, ${updated} would be updated, ${unchanged} unchanged, ${orphaned.length} would be deleted`,
-    );
-  } else if (keepOrphans) {
-    console.log(
-      `Summary: ${created} created, ${updated} updated, ${unchanged} unchanged, ${orphaned.length} orphaned (kept)`,
-    );
-  } else {
-    console.log(
-      `Summary: ${created} created, ${updated} updated, ${unchanged} unchanged, ${orphaned.length} deleted`,
-    );
-  }
-}
-
-await main();
@@ -246,6 +246,9 @@ export const ModelFamilyValues = [
  // Lucid
  "lucid",

+  // LucidQuery
+  "agi",
+
  // Intellect
  "intellect",

@@ -39,7 +39,7 @@ export async function generateModels(directory: string) {
    absolute: true,
    followSymlinks: true,
  })) {
-    const modelID = path.relative(directory, modelPath).slice(0, -5);
+    const modelID = path.relative(directory, modelPath).split(path.sep).join("/").slice(0, -5);
    const toml = await import(modelPath, {
      with: {
        type: "toml",
@@ -94,7 +94,7 @@ async function generateProviders(
      absolute: true,
      followSymlinks: true,
    })) {
-      const modelID = path.relative(modelsPath, modelPath).slice(0, -5);
+      const modelID = path.relative(modelsPath, modelPath).split(path.sep).join("/").slice(0, -5);
      const toml = await import(modelPath, {
        with: {
          type: "toml",
@@ -5,8 +5,11 @@ import { z } from "zod";

 import { AuthoredModel, AuthoredModelShape, ModelMetadata } from "../schema.js";
 import { baseten } from "./providers/baseten.js";
+import { chutes } from "./providers/chutes.js";
 import { cloudflareWorkersAi } from "./providers/cloudflare-workers-ai.js";
 import { google } from "./providers/google.js";
+import { huggingface } from "./providers/huggingface.js";
+import { llmgateway } from "./providers/llmgateway.js";
 import { openrouter } from "./providers/openrouter.js";
 import { ovhcloud } from "./providers/ovhcloud.js";
 import { vercel } from "./providers/vercel.js";
@@ -78,8 +81,11 @@ export interface SyncResult {

 export const providers: {
  baseten: SyncProvider<any>;
+  chutes: SyncProvider<any>;
  "cloudflare-workers-ai": SyncProvider<any>;
  google: SyncProvider<any>;
+  huggingface: SyncProvider<any>;
+  llmgateway: SyncProvider<any>;
  openrouter: SyncProvider<any>;
  ovhcloud: SyncProvider<any>;
  vercel: SyncProvider<any>;
@@ -87,8 +93,11 @@ export const providers: {
  xai: SyncProvider<any>;
 } = {
  baseten,
+  chutes,
  "cloudflare-workers-ai": cloudflareWorkersAi,
  google,
+  huggingface,
+  llmgateway,
  openrouter,
  ovhcloud,
  vercel,
@@ -97,9 +106,9 @@ export const providers: {
 };

 export const groups = {
-  aggregators: ["openrouter", "vercel"],
+  aggregators: ["huggingface", "llmgateway", "openrouter", "vercel"],
  cloudflare: ["cloudflare-workers-ai"],
-  direct: ["baseten", "google", "ovhcloud", "venice", "xai"],
+  direct: ["baseten", "chutes", "google", "ovhcloud", "venice", "xai"],
 } as const;

 type ProviderID = keyof typeof providers;
@@ -229,7 +238,7 @@ export async function syncProvider<SourceModel>(
    }
    const namespaceDir = path.join(metadataDir, provider.metadataNamespace);
    for (const { file } of await tomlFiles(namespaceDir)) {
-      const relativePath = path.join(provider.metadataNamespace, file);
+      const relativePath = path.join(provider.metadataNamespace, file).split(path.sep).join("/");
      if (desiredMetadata.has(relativePath) || provider.deleteMissing === false) continue;
      if (options.newOnly) {
        console.log(`Skipping metadata removal in new-only mode: ${relativePath}`);
@@ -435,7 +444,7 @@ async function readModelMetadata(modelsDir: string) {
    absolute: true,
    followSymlinks: true,
  })) {
-    const modelID = path.relative(metadataDir, modelPath).slice(0, -5);
+    const modelID = path.relative(metadataDir, modelPath).split(path.sep).join("/").slice(0, -5);
    const toml = Bun.TOML.parse(
      await Bun.file(modelPath).text(),
    ) as Record<string, unknown>;
@@ -544,7 +553,7 @@ async function tomlFiles(root: string, dir = "") {
  const result: Array<{ file: string; symlink: boolean }> = [];

  for (const entry of await readdir(path.join(root, dir), { withFileTypes: true })) {
-    const file = path.join(dir, entry.name);
+    const file = path.join(dir, entry.name).split(path.sep).join("/");
    if (entry.isDirectory()) {
      result.push(...await tomlFiles(root, file));
    } else if (entry.name.endsWith(".toml") && (entry.isFile() || entry.isSymbolicLink())) {
@@ -673,7 +682,7 @@ function formatReasoningValue(value: string | null) {
  return value === null ? quote("null") : quote(value);
 }

-function formatToml(model: z.infer<typeof SyncedAuthoredModel>) {
+export function formatToml(model: z.infer<typeof SyncedAuthoredModel>) {
  const lines: string[] = [];

  if (model.base_model !== undefined) lines.push(`base_model = ${quote(model.base_model)}`);
@@ -694,22 +703,7 @@ function formatToml(model: z.infer<typeof SyncedAuthoredModel>) {
  if (model.knowledge !== undefined) lines.push(`knowledge = ${quote(model.knowledge)}`);
  if (model.open_weights !== undefined) lines.push(`open_weights = ${model.open_weights}`);
  if (model.status !== undefined) lines.push(`status = ${quote(model.status)}`);
-
-  if (model.reasoning_options?.length === 0) {
-    lines.push("reasoning_options = []");
-  } else {
-    for (const option of model.reasoning_options ?? []) {
-      lines.push("", "[[reasoning_options]]");
-      lines.push(`type = ${quote(option.type)}`);
-      if (option.type === "effort") {
-        lines.push(`values = [${option.values.map(formatReasoningValue).join(", ")}]`);
-      }
-      if (option.type === "budget_tokens") {
-        if (option.min !== undefined) lines.push(`min = ${formatInteger(option.min)}`);
-        if (option.max !== undefined) lines.push(`max = ${formatInteger(option.max)}`);
-      }
-    }
-  }
+  if (model.reasoning_options?.length === 0) lines.push("reasoning_options = []");

  if (model.interleaved !== undefined) {
    lines.push("");
@@ -721,6 +715,18 @@ function formatToml(model: z.infer<typeof SyncedAuthoredModel>) {
    }
  }

+  for (const option of model.reasoning_options ?? []) {
+    lines.push("", "[[reasoning_options]]");
+    lines.push(`type = ${quote(option.type)}`);
+    if (option.type === "effort") {
+      lines.push(`values = [${option.values.map(formatReasoningValue).join(", ")}]`);
+    }
+    if (option.type === "budget_tokens") {
+      if (option.min !== undefined) lines.push(`min = ${formatInteger(option.min)}`);
+      if (option.max !== undefined) lines.push(`max = ${formatInteger(option.max)}`);
+    }
+  }
+
  if (model.cost !== undefined) {
    lines.push("", "[cost]");
    lines.push(`input = ${formatNumber(model.cost.input)}`);
@@ -0,0 +1,219 @@
+import { existsSync, readdirSync } from "node:fs";
+import path from "node:path";
+import { z } from "zod";
+
+import { inferKimiFamily, ModelFamilyValues } from "../../family.js";
+import type { ExistingModel, SyncProvider, SyncedFullModel, SyncedModel } from "../index.js";
+import { factorBaseModel } from "./openrouter.js";
+
+const API_ENDPOINT = "https://llm.chutes.ai/v1/models";
+const MODELS_DIR = path.join(import.meta.dirname, "..", "..", "..", "..", "..", "models");
+
+const CHUTES_ORG_TO_MODEL_PROVIDER: Record<string, string | undefined> = {
+  MiniMaxAI: "minimax",
+  Qwen: "alibaba",
+  XiaomiMiMo: "xiaomi",
+  "deepseek-ai": "deepseek",
+  google: "google",
+  moonshotai: "moonshotai",
+  openai: "openai",
+  "zai-org": "zhipuai",
+};
+
+const BASE_MODEL_ALIASES: Record<string, string | undefined> = {
+  "google/gemma-4-31B-turbo-TEE": "google/gemma-4-31b-it",
+  // "unsloth" re-hosts models from many providers, so it has no org mapping; alias the
+  // ones whose canonical metadata lives under the original provider's namespace.
+  "unsloth/Mistral-Nemo-Instruct-2407-TEE": "mistral/mistral-nemo",
+};
+
+const Pricing = z.object({
+  prompt: z.number().optional(),
+  completion: z.number().optional(),
+  input_cache_read: z.number().optional(),
+}).passthrough();
+
+export const ChutesModel = z.object({
+  id: z.string(),
+  created: z.number(),
+  pricing: Pricing.optional(),
+  context_length: z.number().optional(),
+  max_output_length: z.number().optional(),
+  max_model_len: z.number().optional(),
+  input_modalities: z.array(z.string()).optional(),
+  output_modalities: z.array(z.string()).optional(),
+  supported_features: z.array(z.string()).optional(),
+  supported_sampling_parameters: z.array(z.string()).optional(),
+  quantization: z.string().optional(),
+}).passthrough();
+
+export const ChutesResponse = z.object({
+  data: z.array(ChutesModel),
+}).passthrough();
+
+export type ChutesModel = z.infer<typeof ChutesModel>;
+
+type Modality = "text" | "audio" | "image" | "video" | "pdf";
+
+export const chutes = {
+  id: "chutes",
+  name: "Chutes",
+  modelsDir: "providers/chutes/models",
+  preserveBaseModels: false,
+  async fetchModels() {
+    const response = await fetch(API_ENDPOINT);
+    if (!response.ok) {
+      throw new Error(`Chutes models request failed: ${response.status} ${response.statusText}`);
+    }
+    return response.json();
+  },
+  parseModels(raw) {
+    return ChutesResponse.parse(raw).data;
+  },
+  translateModel(model, context) {
+    return {
+      id: model.id,
+      model: buildChutesModel(model, context.existing(model.id)),
+    };
+  },
+} satisfies SyncProvider<ChutesModel>;
+
+export function buildChutesModel(
+  model: ChutesModel,
+  existing: ExistingModel | undefined,
+  today = new Date().toISOString().slice(0, 10),
+): SyncedModel {
+  const features = new Set(model.supported_features ?? []);
+  const samplingParams = new Set(model.supported_sampling_parameters ?? []);
+  const input = normalizeModalities(model.input_modalities ?? ["text"]);
+  const output = normalizeModalities(model.output_modalities ?? ["text"]);
+
+  const attachment = input.some((value) => value !== "text");
+  const reasoning = features.has("reasoning");
+  const toolCall = features.has("tools");
+  const structuredOutput = features.has("structured_outputs");
+  // Absent sampling-parameter info, assume temperature is tunable.
+  const temperature = samplingParams.size > 0 ? samplingParams.has("temperature") : true;
+
+  const name = existing?.name ?? humanizeModelName(model.id);
+  const baseModel = resolveBaseModel(model.id);
+
+  const apiContext = model.context_length ?? model.max_model_len ?? 0;
+  const context = apiContext > 0 ? apiContext : existing?.limit?.context ?? 0;
+  const apiOutput = model.max_output_length ?? 0;
+  const limit = {
+    context,
+    input: existing?.limit?.input,
+    output: apiOutput > 0 ? apiOutput : existing?.limit?.output ?? 0,
+  };
+
+  const cost = model.pricing?.prompt !== undefined && model.pricing?.completion !== undefined
+    ? {
+        input: model.pricing.prompt,
+        output: model.pricing.completion,
+        cache_read: model.pricing.input_cache_read,
+      }
+    : existing?.cost;
+
+  const values: SyncedFullModel = {
+    name,
+    family: baseModel == null ? (existing?.family ?? inferFamily(model.id, name)) : existing?.family,
+    release_date: existing?.release_date ?? dateFromTimestamp(model.created),
+    last_updated: existing?.last_updated ?? today,
+    attachment,
+    reasoning,
+    // Chutes' /v1/models advertises `reasoning` as a capability but exposes no parameter
+    // to toggle or set its effort, so there is no provider evidence for a reasoning option.
+    reasoning_options: [],
+    temperature,
+    tool_call: toolCall,
+    structured_output: structuredOutput ? true : undefined,
+    knowledge: existing?.knowledge,
+    open_weights: true,
+    status: existing?.status,
+    interleaved: existing?.interleaved,
+    cost,
+    limit,
+    modalities: { input, output },
+  };
+
+  return baseModel == null
+    ? values
+    : factorBaseModel(baseModel, values, limit, existing?.base_model_omit);
+}
+
+function resolveBaseModel(modelId: string): string | undefined {
+  return baseModelCandidates(modelId).find(canonicalExists);
+}
+
+// existsSync is case-insensitive on Windows/macOS; verify the real on-disk filename case
+// so the resolved base_model matches the canonical metadata exactly (and CI on Linux).
+function canonicalExists(candidate: string): boolean {
+  const file = path.join(MODELS_DIR, `${candidate}.toml`);
+  if (!existsSync(file)) return false;
+  try {
+    return readdirSync(path.dirname(file)).includes(path.basename(file));
+  } catch {
+    return false;
+  }
+}
+
+function baseModelCandidates(modelId: string): string[] {
+  const alias = BASE_MODEL_ALIASES[modelId];
+  const [org, ...modelParts] = modelId.split("/");
+  if (org === undefined || modelParts.length === 0 || modelParts.join("/").endsWith("-TEE") === false) {
+    return alias === undefined ? [] : [alias];
+  }
+
+  const provider = CHUTES_ORG_TO_MODEL_PROVIDER[org];
+  if (provider === undefined) {
+    return alias === undefined ? [] : [alias];
+  }
+
+  const withoutTee = modelParts.join("/").slice(0, -"-TEE".length);
+  const lower = withoutTee.toLowerCase();
+  // Distinct checkpoints (e.g. "-Thinking-2507") keep their own metadata — deliberately
+  // not collapsed onto the generic base, which would inherit the wrong capabilities.
+  const normalized = [
+    withoutTee,
+    lower,
+    lower.replace(/-turbo$/, "-it"),
+    lower.replace(/-turbo$/, ""),
+  ];
+
+  return [
+    ...new Set([alias, ...normalized.map((candidate) => `${provider}/${candidate}`)]).values(),
+  ].filter((candidate): candidate is string => candidate !== undefined);
+}
+
+function normalizeModalities(values: string[]): Modality[] {
+  const allowed = new Set<Modality>(["text", "audio", "image", "video", "pdf"]);
+  const result = values
+    .map((value) => value.toLowerCase())
+    .filter((value): value is Modality => allowed.has(value as Modality));
+  if (result.length === 0) return ["text"];
+  return [...new Set(result)];
+}
+
+function humanizeModelName(modelId: string): string {
+  const modelPart = modelId.split("/").at(-1) ?? modelId;
+  return modelPart.replace(/-/g, " ");
+}
+
+function dateFromTimestamp(timestamp: number): string {
+  return new Date(timestamp * 1000).toISOString().slice(0, 10);
+}
+
+function inferFamily(id: string, name: string) {
+  const kimiFamily = inferKimiFamily(id, name);
+  if (kimiFamily !== undefined) return kimiFamily;
+
+  const target = `${id} ${name}`.toLowerCase();
+  return [...ModelFamilyValues]
+    .sort((a, b) => b.length - a.length)
+    .find((family) => {
+      const value = family.toLowerCase().replace(/[.*+?^${}()|[\]\\]/g, "\\$&");
+      if (family === "o") return new RegExp(`(^|[^a-z0-9])${value}(?=\\d|$|[^a-z0-9])`).test(target);
+      return new RegExp(`(^|[^a-z0-9])${value}(?=$|[^a-z0-9])`).test(target);
+    });
+}
@@ -0,0 +1,245 @@
+import { z } from "zod";
+
+import type { ExistingModel, SyncProvider, SyncedFullModel, SyncedModel } from "../index.js";
+import { factorBaseModel, resolveCanonicalBaseModel } from "./openrouter.js";
+
+const API_ENDPOINT = "https://router.huggingface.co/v1/models";
+
+// Hugging Face org prefixes mapped to the canonical metadata prefixes understood
+// by resolveCanonicalBaseModel. Anything not listed falls back to a direct lookup.
+const CANONICAL_ORG_PREFIXES: Record<string, string> = {
+  CohereLabs: "cohere",
+  "deepseek-ai": "deepseek",
+  google: "google",
+  "meta-llama": "meta-llama",
+  MiniMaxAI: "minimax",
+  moonshotai: "moonshotai",
+  nvidia: "nvidia",
+  Qwen: "qwen",
+  "stepfun-ai": "stepfun",
+  XiaomiMiMo: "xiaomi",
+  "zai-org": "zai",
+};
+
+const HuggingFaceProvider = z.object({
+  provider: z.string(),
+  status: z.string(),
+  context_length: z.number().int().positive().optional(),
+  pricing: z.object({
+    input: z.number(),
+    output: z.number(),
+  }).passthrough().optional(),
+  throughput: z.number().nonnegative().optional(),
+  first_token_latency_ms: z.number().nonnegative().optional(),
+  is_free: z.boolean().optional(),
+  supports_tools: z.boolean().optional(),
+  supports_structured_output: z.boolean().optional(),
+  is_model_author: z.boolean().optional(),
+}).passthrough();
+
+export const HuggingFaceModel = z.object({
+  id: z.string().min(1),
+  created: z.number().optional(),
+  owned_by: z.string().optional(),
+  architecture: z.object({
+    input_modalities: z.array(z.string()),
+    output_modalities: z.array(z.string()),
+  }).passthrough(),
+  providers: z.array(HuggingFaceProvider),
+}).passthrough();
+
+export const HuggingFaceResponse = z.object({
+  data: z.array(HuggingFaceModel),
+}).passthrough();
+
+export type HuggingFaceModel = z.infer<typeof HuggingFaceModel>;
+export type HuggingFaceProvider = z.infer<typeof HuggingFaceProvider>;
+
+export const huggingface = {
+  id: "huggingface",
+  name: "Hugging Face",
+  modelsDir: "providers/huggingface/models",
+  deleteMissing: false,
+  sourceID(model) {
+    return model.id;
+  },
+  skippedNotice(ids) {
+    if (ids.length === 0) return [];
+    return [
+      `${ids.length} Hugging Face Inference Providers models were not created because their IDs could not be mapped to provider-agnostic metadata, had no live provider, or had no priced provider.`,
+      `Skipped remote IDs: ${ids.map((id) => `\`${id}\``).join(", ")}`,
+    ];
+  },
+  missingNotice(paths) {
+    if (paths.length === 0) return [];
+    return [
+      `${paths.length} local Hugging Face models were absent from the Inference Providers catalog and were retained for manual lifecycle review.`,
+      `Retained local paths: ${paths.map((item) => `\`${item}\``).join(", ")}`,
+    ];
+  },
+  async fetchModels() {
+    const headers = process.env.HF_TOKEN
+      ? { Authorization: `Bearer ${process.env.HF_TOKEN}` }
+      : undefined;
+    const response = await fetch(API_ENDPOINT, { headers });
+    if (!response.ok) {
+      throw new Error(`Hugging Face models request failed: ${response.status} ${response.statusText}`);
+    }
+    return response.json();
+  },
+  parseModels(raw) {
+    return HuggingFaceResponse.parse(raw).data;
+  },
+  translateModel(model, context) {
+    if (!model.providers.some((provider) => provider.status === "live")) return undefined;
+
+    const existing = context.existing(model.id);
+    const baseModel = existing === undefined
+      ? resolveHuggingFaceBaseModel(model.id)
+      : existing.base_model;
+    if (existing === undefined && baseModel === undefined) return undefined;
+
+    // The router only exposes pricing per inference provider, so a new model with
+    // no priced provider cannot be created with a meaningful cost.
+    const aggregate = aggregateProviders(model);
+    if (existing === undefined && aggregate.cost === undefined) return undefined;
+
+    return {
+      id: model.id,
+      model: buildHuggingFaceModel(model, existing, baseModel, aggregate),
+    };
+  },
+  sameModel() {
+    // For now the sync only creates new models; existing curated TOMLs are left
+    // untouched. Treating every existing model as already in sync skips updates
+    // while still allowing new files to be created.
+    return true;
+  },
+} satisfies SyncProvider<HuggingFaceModel>;
+
+interface Aggregate {
+  cost: { input: number; output: number } | undefined;
+  context: number | undefined;
+  tools: boolean;
+  structuredOutput: boolean;
+}
+
+function price(value: number) {
+  return Number.isFinite(value) && value >= 0
+    ? Math.round(value * 1_000_000) / 1_000_000
+    : undefined;
+}
+
+// The router aggregates several inference providers per model and sends traffic to
+// the fastest one, so this collapses them into the route a request would actually
+// take: pricing and context from the highest-throughput provider, plus capabilities
+// advertised by any provider (a caller can always pin a slower provider).
+function aggregateProviders(model: HuggingFaceModel): Aggregate {
+  const providers = model.providers.filter((provider) => provider.status === "live");
+
+  const byThroughput = (a: HuggingFaceProvider, b: HuggingFaceProvider) =>
+    (b.throughput ?? -Infinity) - (a.throughput ?? -Infinity);
+  // The provider the router routes to (fastest). Take its price when it reports one;
+  // otherwise fall back to the fastest provider that does, so a new model can still
+  // be costed.
+  const routed = [...providers].sort(byThroughput).at(0);
+  const costProvider = routed?.pricing !== undefined
+    ? routed
+    : [...providers]
+        .filter((provider): provider is HuggingFaceProvider & { pricing: { input: number; output: number } } =>
+          provider.pricing !== undefined)
+        .sort(byThroughput)
+        .at(0);
+  const input = costProvider?.pricing === undefined ? undefined : price(costProvider.pricing.input);
+  const output = costProvider?.pricing === undefined ? undefined : price(costProvider.pricing.output);
+
+  const contexts = providers
+    .map((provider) => provider.context_length)
+    .filter((value): value is number => value !== undefined);
+
+  return {
+    cost: input !== undefined && output !== undefined ? { input, output } : undefined,
+    context: routed?.context_length ?? (contexts.length > 0 ? Math.max(...contexts) : undefined),
+    tools: providers.some((provider) => provider.supports_tools === true),
+    structuredOutput: providers.some((provider) => provider.supports_structured_output === true),
+  };
+}
+
+type Modality = "text" | "audio" | "image" | "video" | "pdf";
+
+function modalities(values: string[], fallback: Modality[]): Modality[] {
+  const allowed = new Set<Modality>(["text", "audio", "image", "video", "pdf"]);
+  const result = values
+    .map((value) => value.toLowerCase())
+    .filter((value): value is Modality => allowed.has(value as Modality));
+  return [...new Set(result.length > 0 ? result : fallback)];
+}
+
+export function buildHuggingFaceModel(
+  model: HuggingFaceModel,
+  existing: ExistingModel | undefined,
+  baseModel = existing === undefined ? resolveHuggingFaceBaseModel(model.id) : existing.base_model,
+  aggregate: Aggregate = aggregateProviders(model),
+): SyncedModel {
+  const input = modalities(model.architecture.input_modalities, existing?.modalities?.input ?? ["text"]);
+  const output = modalities(model.architecture.output_modalities, existing?.modalities?.output ?? ["text"]);
+  // Pricing is curated: keep what was authored and only fall back to the router
+  // (fastest route) when the local model has no cost yet.
+  const cost = existing?.cost ?? aggregate.cost;
+  // context/output may be unset for a freshly created base_model entry, in which case
+  // factorBaseModel inherits them from the canonical metadata; the standalone-model
+  // path below validates their presence at runtime.
+  const limit = {
+    context: existing?.limit?.context ?? aggregate.context,
+    input: existing?.limit?.input,
+    output: existing?.limit?.output,
+  } as SyncedFullModel["limit"];
+  const values: Partial<SyncedFullModel> = {
+    name: existing?.name,
+    family: existing?.family,
+    release_date: existing?.release_date,
+    last_updated: existing?.last_updated,
+    attachment: input.some((value) => value !== "text"),
+    reasoning: existing?.reasoning,
+    reasoning_options: existing?.reasoning_options,
+    temperature: existing?.temperature,
+    tool_call: aggregate.tools || existing?.tool_call || undefined,
+    structured_output: aggregate.structuredOutput || existing?.structured_output || undefined,
+    knowledge: existing?.knowledge,
+    open_weights: existing?.open_weights ?? true,
+    status: existing?.status,
+    interleaved: existing?.interleaved,
+    cost,
+    limit,
+    modalities: { input, output },
+  };
+
+  if (baseModel !== undefined) {
+    return factorBaseModel(baseModel, values, limit, existing?.base_model_omit);
+  }
+
+  // Standalone (non base_model) models require concrete booleans the router does
+  // not always report; default the capability flags it leaves out.
+  const full = { ...values, tool_call: values.tool_call ?? false };
+  const required = z.object({
+    name: z.string(),
+    release_date: z.string(),
+    last_updated: z.string(),
+    reasoning: z.boolean(),
+    open_weights: z.boolean(),
+    cost: z.object({ input: z.number(), output: z.number() }),
+    limit: z.object({ context: z.number(), output: z.number() }),
+  }).safeParse(full);
+  if (!required.success) {
+    throw new Error(`Hugging Face model ${model.id} has incomplete local metadata required for sync`);
+  }
+  return full as SyncedFullModel;
+}
+
+export function resolveHuggingFaceBaseModel(id: string) {
+  const [prefix, ...parts] = id.split("/");
+  if (prefix === undefined || parts.length === 0) return undefined;
+  const canonicalPrefix = CANONICAL_ORG_PREFIXES[prefix];
+  if (canonicalPrefix === undefined) return resolveCanonicalBaseModel(id);
+  return resolveCanonicalBaseModel(`${canonicalPrefix}/${parts.join("/").toLowerCase()}`);
+}
@@ -0,0 +1,217 @@
+import { z } from "zod";
+
+import { inferKimiFamily, ModelFamilyValues } from "../../family.js";
+import type { ExistingModel, SyncProvider, SyncedFullModel, SyncedModel } from "../index.js";
+import { factorBaseModel } from "./openrouter.js";
+
+const API_ENDPOINT = "https://api.llmgateway.io/v1/models";
+
+const Pricing = z.object({
+  prompt: z.string().optional(),
+  completion: z.string().optional(),
+  internal_reasoning: z.string().optional(),
+  input_cache_read: z.string().optional(),
+  input_cache_write: z.string().optional(),
+});
+
+export const LLMGatewayModel = z.object({
+  id: z.string(),
+  name: z.string(),
+  created: z.number(),
+  family: z.string().optional(),
+  architecture: z.object({
+    input_modalities: z.array(z.string()),
+    output_modalities: z.array(z.string()),
+  }),
+  pricing: Pricing,
+  context_length: z.number(),
+  supported_parameters: z.array(z.string()),
+  structured_outputs: z.boolean().optional(),
+}).passthrough();
+
+export const LLMGatewayResponse = z.object({
+  data: z.array(LLMGatewayModel),
+}).passthrough();
+
+export type LLMGatewayModel = z.infer<typeof LLMGatewayModel>;
+
+export const llmgateway = {
+  id: "llmgateway",
+  name: "LLM Gateway",
+  modelsDir: "providers/llmgateway/models",
+  async fetchModels() {
+    const headers = process.env.LLMGATEWAY_API_KEY
+      ? { Authorization: `Bearer ${process.env.LLMGATEWAY_API_KEY}` }
+      : undefined;
+    const response = await fetch(API_ENDPOINT, { headers });
+    if (!response.ok) {
+      throw new Error(`LLM Gateway request failed: ${response.status} ${response.statusText}`);
+    }
+    return response.json();
+  },
+  parseModels(raw) {
+    return LLMGatewayResponse.parse(raw).data.filter((model) => {
+      const output = model.architecture.output_modalities;
+      return output.length === 1 && output[0] === "text";
+    });
+  },
+  translateModel(model, context) {
+    return {
+      id: model.id,
+      model: buildLLMGatewayModel(model, context.existing(model.id)),
+    };
+  },
+} satisfies SyncProvider<LLMGatewayModel>;
+
+function dateFromTimestamp(timestamp: number) {
+  return new Date(timestamp * 1000).toISOString().slice(0, 10);
+}
+
+function price(value: string | undefined) {
+  if (value === undefined) return undefined;
+  const number = Number(value);
+  return Number.isFinite(number) && number >= 0
+    ? Math.round(number * 1_000_000_000_000) / 1_000_000
+    : undefined;
+}
+
+// Cache/reasoning prices are reported as "0" when the gateway has no data; treat
+// those as unknown so we never downgrade a hand-authored value to zero.
+function nonZeroPrice(value: string | undefined) {
+  const result = price(value);
+  return result !== undefined && result > 0 ? result : undefined;
+}
+
+type Modality = "text" | "audio" | "image" | "video" | "pdf";
+
+function modalities(values: string[], fallback: Modality[]): Modality[] {
+  const allowed = new Set<Modality>(["text", "audio", "image", "video", "pdf"]);
+  const result = values
+    .map((value) => value.toLowerCase())
+    .map((value) => (value === "file" ? "pdf" : value))
+    .filter((value): value is Modality => allowed.has(value as Modality));
+  return [...new Set(result.length > 0 ? result : fallback)];
+}
+
+function inferFamily(model: LLMGatewayModel, name: string) {
+  const kimiFamily = inferKimiFamily(model.id, name);
+  if (kimiFamily !== undefined) return kimiFamily;
+
+  const target = `${model.id} ${name}`.toLowerCase();
+  return [...ModelFamilyValues]
+    .sort((a, b) => b.length - a.length)
+    .find((family) => {
+      const value = family.toLowerCase().replace(/[.*+?^${}()|[\]\\]/g, "\\$&");
+      if (family === "o") {
+        return new RegExp(`(^|[^a-z0-9])${value}(?=\\d|$|[^a-z0-9])`).test(target);
+      }
+      return new RegExp(`(^|[^a-z0-9])${value}(?=$|[^a-z0-9])`).test(target);
+    });
+}
+
+function buildLLMGatewayModel(
+  model: LLMGatewayModel,
+  existing: ExistingModel | undefined,
+): SyncedModel {
+  const prompt = price(model.pricing.prompt);
+  const completion = price(model.pricing.completion);
+  const reasoning = model.supported_parameters.includes("reasoning")
+    || model.supported_parameters.includes("include_reasoning");
+  const context = model.context_length > 0
+    ? model.context_length
+    : existing?.limit?.context ?? model.context_length;
+
+  // The gateway is authoritative for the volatile, gateway-specific data — cost
+  // and served limits. Its supported_parameters / modalities are too noisy to
+  // drive capability fields (it omits "tools" for flagship models yet lists
+  // "temperature" for ones the catalog deliberately marks temperature=false),
+  // so those stay curated: preserved from the existing entry (which, for a
+  // factored model, inherits its base when the field is absent).
+  const cost = prompt !== undefined && completion !== undefined
+    ? {
+        input: prompt,
+        output: completion,
+        reasoning: reasoning ? nonZeroPrice(model.pricing.internal_reasoning) ?? existing?.cost?.reasoning : existing?.cost?.reasoning,
+        cache_read: nonZeroPrice(model.pricing.input_cache_read) ?? existing?.cost?.cache_read,
+        cache_write: nonZeroPrice(model.pricing.input_cache_write) ?? existing?.cost?.cache_write,
+        tiers: existing?.cost?.tiers,
+      }
+    : existing?.cost;
+  const limit = {
+    context,
+    input: existing?.limit?.input,
+    output: existing?.limit?.output ?? context,
+  };
+
+  // Existing factored model: refresh cost + limit, keep every authored override
+  // as-is (undefined fields keep inheriting the base model).
+  if (existing?.base_model !== undefined) {
+    return factorBaseModel(
+      existing.base_model,
+      {
+        attachment: existing.attachment,
+        reasoning: existing.reasoning,
+        temperature: existing.temperature,
+        tool_call: existing.tool_call,
+        structured_output: existing.structured_output,
+        status: existing.status,
+        interleaved: existing.interleaved,
+        knowledge: existing.knowledge,
+        modalities: existing.modalities,
+        limit,
+        cost,
+      },
+      limit,
+      existing.base_model_omit,
+    );
+  }
+
+  // Existing full model: refresh cost + limit, preserve curated metadata.
+  if (existing !== undefined) {
+    return {
+      name: existing.name ?? model.name,
+      family: existing.family,
+      release_date: existing.release_date ?? dateFromTimestamp(model.created),
+      last_updated: existing.last_updated ?? dateFromTimestamp(model.created),
+      attachment: existing.attachment ?? false,
+      reasoning: existing.reasoning ?? false,
+      temperature: existing.temperature ?? false,
+      tool_call: existing.tool_call ?? false,
+      structured_output: existing.structured_output,
+      knowledge: existing.knowledge,
+      open_weights: existing.open_weights ?? false,
+      status: existing.status,
+      interleaved: existing.interleaved,
+      cost,
+      limit,
+      modalities: existing.modalities ?? defaultModalities(model),
+    } satisfies SyncedFullModel;
+  }
+
+  // Brand-new model: best-effort translation from the gateway. Capability and
+  // modality data are unreliable here and should be hand-reviewed.
+  const { input, output } = defaultModalities(model);
+  return {
+    name: model.name,
+    family: inferFamily(model, model.name),
+    release_date: dateFromTimestamp(model.created),
+    last_updated: dateFromTimestamp(model.created),
+    attachment: input.some((value) => value !== "text"),
+    reasoning,
+    temperature: model.supported_parameters.includes("temperature"),
+    tool_call: model.supported_parameters.includes("tools")
+      || model.supported_parameters.includes("tool_choice"),
+    structured_output: model.structured_outputs ?? false,
+    open_weights: false,
+    cost,
+    limit,
+    modalities: { input, output },
+  } satisfies SyncedFullModel;
+}
+
+function defaultModalities(model: LLMGatewayModel) {
+  return {
+    input: modalities(model.architecture.input_modalities, ["text"]),
+    output: modalities(model.architecture.output_modalities, ["text"]),
+  };
+}
@@ -7,6 +7,7 @@ import type { ExistingModel, SyncProvider, SyncedFullModel, SyncedModel } from "

 const API_ENDPOINT = "https://openrouter.ai/api/v1/models";
 const MODELS_DIR = path.join(import.meta.dirname, "..", "..", "..", "..", "..", "models");
+const MODEL_NAME_BLACKLIST = ["fable-5"];
 const modelMetadataByID = new Map<string, Record<string, unknown>>();
 const modelMetadataFilesByProvider = new Map<string, Set<string>>();

@@ -79,7 +80,10 @@ export const openrouter = {
    return response.json();
  },
  parseModels(raw) {
-    return OpenRouterResponse.parse(raw).data;
+    return OpenRouterResponse.parse(raw).data.filter((model) => {
+      const name = `${model.id} ${model.name}`.toLowerCase();
+      return MODEL_NAME_BLACKLIST.every((value) => !name.includes(value));
+    });
  },
  translateModel(model, context) {
    return {
@@ -6,7 +6,16 @@ import { factorBaseModel, resolveCanonicalBaseModel } from "./openrouter.js";

 const API_ENDPOINT = "https://ai-gateway.vercel.sh/v1/models";

-const ModelType = z.enum(["language", "embedding", "image", "video", "reranking"]);
+const ModelType = z.enum([
+  "language",
+  "embedding",
+  "image",
+  "video",
+  "reranking",
+  "transcription",
+  "speech",
+  "realtime",
+]);

 const PricingTier = z.object({
  cost: z.string(),
@@ -30,8 +39,8 @@ export const VercelModel = z.object({
  name: z.string(),
  created: z.number(),
  released: z.number().optional(),
-  context_window: z.number(),
-  max_tokens: z.number(),
+  context_window: z.number().optional().default(0),
+  max_tokens: z.number().optional().default(0),
  type: ModelType,
  tags: z.array(z.string()).optional().default([]),
  pricing: Pricing.optional(),
@@ -107,9 +116,17 @@ export function buildVercelModel(model: VercelModel, existing: ExistingModel | u
    cost,
    limit: { context, input, output },
    modalities: {
-      input: ["text", tags.has("vision") ? "image" : undefined, tags.has("file-input") ? "pdf" : undefined]
-        .filter((value): value is "text" | "image" | "pdf" => value !== undefined),
-      output: model.type === "image"
+      input: model.type === "transcription"
+        ? ["audio"]
+        : model.type === "realtime"
+        ? ["text", "audio"]
+        : ["text", tags.has("vision") ? "image" : undefined, tags.has("file-input") ? "pdf" : undefined]
+          .filter((value): value is "text" | "image" | "pdf" => value !== undefined),
+      output: model.type === "speech"
+        ? ["audio"]
+        : model.type === "realtime"
+        ? ["text", "audio"]
+        : model.type === "image"
        ? ["image"]
        : model.type === "video"
        ? ["video"]
@@ -1,150 +0,0 @@
-import { afterEach, expect, test } from "bun:test";
-import path from "node:path";
-import { mkdir, mkdtemp } from "node:fs/promises";
-import os from "node:os";
-
-import { syncProvider } from "../src/sync/index.js";
-import {
-  BasetenResponse,
-  baseten,
-  buildBasetenModel,
-  fetchBasetenModels,
-  type BasetenModel,
-} from "../src/sync/providers/baseten.js";
-
-const catalogModel: BasetenModel = {
-  id: "zai-org/GLM-5.1",
-  name: "GLM 5.1",
-  context_length: 128_000,
-  max_completion_tokens: 32_000,
-  input_modalities: ["text"],
-  output_modalities: ["text"],
-  pricing: {
-    prompt: "0.00000012",
-    completion: "0.0000005",
-  },
-  supported_features: ["reasoning", "reasoning_effort", "tools", "structured_outputs"],
-  supported_sampling_parameters: ["temperature", "top_p"],
-};
-
-const newCatalogModel: BasetenModel = {
-  ...catalogModel,
-};
-
-afterEach(() => {
-  baseten.modelsDir = "providers/baseten/models";
-  baseten.fetchModels = async () => {
-    const key = process.env.BASETEN_API_KEY;
-    if (key === undefined) throw new Error("Baseten sync requires BASETEN_API_KEY");
-    return fetchBasetenModels(key);
-  };
-});
-
-test("Baseten maps authoritative fields and preserves curated metadata", () => {
-  const synced = buildBasetenModel(catalogModel, {
-    name: "Old name",
-    release_date: "2025-08-05",
-    last_updated: "2025-09-01",
-    attachment: false,
-    reasoning: true,
-    reasoning_options: [{ type: "effort", values: ["low", "high"] }],
-    tool_call: true,
-    open_weights: true,
-    status: "deprecated",
-    interleaved: { field: "reasoning_content" },
-    base_model: "zhipuai/glm-5.1",
-    base_model_omit: ["limit.input"],
-    cost: { input: 0.1, output: 0.4, cache_write: 0.2 },
-    limit: { context: 64_000, output: 16_000 },
-    modalities: { input: ["text"], output: ["text"] },
-  });
-
-  expect(synced).toMatchObject({
-    base_model: "zhipuai/glm-5.1",
-    base_model_omit: ["limit.input"],
-    reasoning_options: [{ type: "effort", values: ["low", "high"] }],
-    status: "deprecated",
-    interleaved: { field: "reasoning_content" },
-    cost: { input: 0.12, output: 0.5, cache_write: 0.2 },
-    limit: { context: 128_000, output: 32_000 },
-  });
-});
-
-test("Baseten preserves curated reasoning when an opt-in capability is omitted", () => {
-  const synced = buildBasetenModel({
-    ...catalogModel,
-    supported_features: ["tools", "structured_outputs"],
-  }, {
-    name: "GLM 5.1",
-    release_date: "2026-05-20",
-    last_updated: "2026-05-20",
-    attachment: false,
-    reasoning: true,
-    reasoning_options: [{ type: "toggle" }],
-    tool_call: true,
-    open_weights: true,
-    cost: { input: 1, output: 4 },
-    limit: { context: 100_000, output: 50_000 },
-    modalities: { input: ["text"], output: ["text"] },
-  });
-
-  expect(synced).toMatchObject({
-    reasoning: true,
-    reasoning_options: [{ type: "toggle" }],
-  });
-});
-
-test("Baseten sync adds exact base models, retains missing entries, and is idempotent", async () => {
-  const root = await mkdtemp(path.join(os.tmpdir(), "models-dev-baseten-"));
-  const modelsDir = path.join(root, "providers", "baseten", "models");
-  const metadataDir = path.join(root, "models", "zhipuai");
-  await mkdir(path.join(modelsDir, "stale"), { recursive: true });
-  await mkdir(metadataDir, { recursive: true });
-  await Bun.write(
-    path.join(metadataDir, "glm-5.1.toml"),
-    Bun.file(path.join(import.meta.dirname, "../../../models/zhipuai/glm-5.1.toml")),
-  );
-  await Bun.write(path.join(modelsDir, "stale", "model.toml"), [
-    'name = "Retained"',
-    'release_date = "2025-01-01"',
-    'last_updated = "2025-01-01"',
-    "attachment = false",
-    "reasoning = false",
-    "tool_call = false",
-    "open_weights = false",
-    "[cost]",
-    "input = 1",
-    "output = 1",
-    "[limit]",
-    "context = 1000",
-    "output = 100",
-    "[modalities]",
-    'input = ["text"]',
-    'output = ["text"]',
-    "",
-  ].join("\n"));
-  baseten.modelsDir = modelsDir;
-  baseten.fetchModels = async () => ({ data: [newCatalogModel] });
-
-  const first = await syncProvider(baseten);
-  const second = await syncProvider(baseten);
-
-  expect(first.created).toBe(1);
-  expect(first.deleted).toBe(0);
-  expect(first.notices.join(" ")).toContain("stale/model.toml");
-  expect(second).toMatchObject({ created: 0, updated: 0, deleted: 0 });
-});
-
-test("Baseten rejects malformed catalog responses", () => {
-  expect(() => BasetenResponse.parse({ data: "broken" })).toThrow();
-});
-
-test("Baseten rejects non-success API responses", async () => {
-  const fetcher = async () => new Response("unauthorized", {
-    status: 401,
-    statusText: "Unauthorized",
-  });
-
-  expect(fetchBasetenModels("fixture-key", fetcher as typeof fetch))
-    .rejects.toThrow("Baseten models request failed: 401 Unauthorized");
-});
@@ -1,35 +0,0 @@
-import { expect, test } from "bun:test";
-
-import { buildWorkersAiModel } from "../src/sync/providers/cloudflare-workers-ai.js";
-import type { OpenRouterModel } from "../src/sync/providers/openrouter.js";
-
-test("Cloudflare Workers AI sync preserves reasoning options", () => {
-  const model: OpenRouterModel = {
-    id: "@cf/nvidia/nemotron-3-120b-a12b",
-    name: "Nemotron 3 Super 120B",
-    created: 1_773_187_200,
-    hugging_face_id: "nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16",
-    knowledge_cutoff: null,
-    context_length: 256_000,
-    architecture: {
-      input_modalities: ["text"],
-      output_modalities: ["text"],
-    },
-    pricing: {
-      prompt: "0.0000005",
-      completion: "0.0000015",
-    },
-    top_provider: {
-      context_length: 256_000,
-      max_completion_tokens: 256_000,
-    },
-    supported_parameters: ["reasoning", "tools", "temperature"],
-  };
-
-  const synced = buildWorkersAiModel(model, {
-    base_model: "nvidia/nemotron-3-super-120b-a12b",
-    reasoning_options: [{ type: "toggle" }],
-  });
-
-  expect(synced.reasoning_options).toEqual([{ type: "toggle" }]);
-});
@@ -1,35 +0,0 @@
-import { expect, test } from "bun:test";
-
-import { buildGoogleModel } from "../src/sync/providers/google.js";
-
-test("Google sync keeps base models compact", () => {
-  const synced = buildGoogleModel({
-    name: "models/gemini-3-pro-image-preview",
-    displayName: "Nano Banana Pro",
-    inputTokenLimit: 131_072,
-    outputTokenLimit: 32_768,
-    temperature: 1,
-    thinking: true,
-  }, {
-    base_model: "google/gemini-3-pro-image-preview",
-    name: "Nano Banana Pro",
-    family: "gemini-pro",
-    release_date: "2025-11-20",
-    last_updated: "2025-11-20",
-    attachment: true,
-    reasoning: true,
-    temperature: true,
-    tool_call: false,
-    knowledge: "2025-01",
-    open_weights: false,
-    cost: { input: 2, output: 120 },
-    limit: { context: 65_536, output: 32_768 },
-    modalities: { input: ["text", "image"], output: ["text", "image"] },
-  });
-
-  expect(synced).toEqual({
-    base_model: "google/gemini-3-pro-image-preview",
-    cost: { input: 2, output: 120 },
-    limit: { context: 131_072 },
-  });
-});
@@ -1,121 +0,0 @@
-import { expect, test } from "bun:test";
-
-import { preserveBaseModel } from "../src/sync/index.js";
-import { resolveCloudflareBaseModel } from "../src/sync/providers/cloudflare-workers-ai.js";
-import { buildOpenRouterModel, type OpenRouterModel } from "../src/sync/providers/openrouter.js";
-
-test("OpenRouter z-ai models inherit from zhipuai metadata", () => {
-  const model: OpenRouterModel = {
-    id: "z-ai/glm-5.1",
-    name: "Z.AI: GLM-5.1",
-    created: 1_777_680_000,
-    hugging_face_id: "zai-org/GLM-5.1",
-    knowledge_cutoff: null,
-    context_length: 200_000,
-    architecture: {
-      input_modalities: ["text"],
-      output_modalities: ["text"],
-    },
-    pricing: {
-      prompt: "0.0000014",
-      completion: "0.0000044",
-    },
-    top_provider: {
-      context_length: 200_000,
-      max_completion_tokens: 131_072,
-    },
-    supported_parameters: ["tools", "tool_choice", "temperature", "structured_outputs"],
-  };
-
-  const synced = buildOpenRouterModel(model, undefined);
-
-  expect("base_model" in synced ? synced.base_model : undefined).toBe("zhipuai/glm-5.1");
-});
-
-test("OpenRouter-derived syncs preserve existing base model links", () => {
-  const model: OpenRouterModel = {
-    id: "@cf/nvidia/nemotron-3-120b-a12b",
-    name: "Nemotron 3 Super 120B",
-    created: 1_773_187_200,
-    hugging_face_id: "nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16",
-    knowledge_cutoff: null,
-    context_length: 256_000,
-    architecture: {
-      input_modalities: ["text"],
-      output_modalities: ["text"],
-    },
-    pricing: {
-      prompt: "0.0000005",
-      completion: "0.0000015",
-    },
-    top_provider: {
-      context_length: 256_000,
-      max_completion_tokens: 256_000,
-    },
-    supported_parameters: ["reasoning", "tools", "temperature", "structured_outputs"],
-  };
-
-  const synced = preserveBaseModel(buildOpenRouterModel(model, undefined), {
-    base_model: "nvidia/nemotron-3-super-120b-a12b",
-    base_model_omit: ["limit.input"],
-  });
-
-  expect("base_model" in synced ? synced.base_model : undefined)
-    .toBe("nvidia/nemotron-3-super-120b-a12b");
-  expect("base_model_omit" in synced ? synced.base_model_omit : undefined)
-    .toEqual(["limit.input"]);
-});
-
-test("newly detected base models do not replace existing links", () => {
-  const model: OpenRouterModel = {
-    id: "z-ai/glm-5.1",
-    name: "Z.AI: GLM-5.1",
-    created: 1_777_680_000,
-    hugging_face_id: null,
-    knowledge_cutoff: null,
-    context_length: 200_000,
-    architecture: { input_modalities: ["text"], output_modalities: ["text"] },
-    pricing: { prompt: "0.0000014", completion: "0.0000044" },
-    top_provider: { context_length: 200_000, max_completion_tokens: 131_072 },
-    supported_parameters: ["tools"],
-  };
-  const synced = buildOpenRouterModel(model, {
-    base_model: "zhipuai/glm-5",
-  });
-
-  expect("base_model" in synced ? synced.base_model : undefined).toBe("zhipuai/glm-5");
-});
-
-test("undefined translated links preserve existing base model fields", () => {
-  const synced = preserveBaseModel({
-    base_model: undefined,
-  } as never, {
-    base_model: "nvidia/nemotron-3-super-120b-a12b",
-    base_model_omit: ["limit.input"],
-  });
-
-  expect("base_model" in synced ? synced.base_model : undefined)
-    .toBe("nvidia/nemotron-3-super-120b-a12b");
-  expect("base_model_omit" in synced ? synced.base_model_omit : undefined)
-    .toEqual(["limit.input"]);
-});
-
-test("new Cloudflare models discover a unique metadata base model", () => {
-  const model: OpenRouterModel = {
-    id: "@cf/nvidia/nemotron-3-120b-a12b",
-    name: "Nemotron 3 Super 120B",
-    created: 1_773_187_200,
-    hugging_face_id: null,
-    knowledge_cutoff: null,
-    context_length: 256_000,
-    architecture: { input_modalities: ["text"], output_modalities: ["text"] },
-    pricing: { prompt: "0.0000005", completion: "0.0000015" },
-    top_provider: { context_length: 256_000, max_completion_tokens: 256_000 },
-    supported_parameters: ["reasoning"],
-  };
-
-  expect(resolveCloudflareBaseModel(model)).toBe("nvidia/nemotron-3-super-120b-a12b");
-  const synced = buildOpenRouterModel(model, undefined, resolveCloudflareBaseModel(model));
-  expect("base_model" in synced ? synced.base_model : undefined)
-    .toBe("nvidia/nemotron-3-super-120b-a12b");
-});
@@ -1,227 +0,0 @@
-import { expect, test } from "bun:test";
-import path from "node:path";
-import { mkdtemp, mkdir, readlink, symlink } from "node:fs/promises";
-import os from "node:os";
-
-import { AuthoredModelShape } from "../src/schema.js";
-import { syncProvider, type SyncProvider, type SyncedFullModel } from "../src/sync/index.js";
-
-const model: SyncedFullModel = {
-  name: "Test model",
-  release_date: "2026-01-01",
-  last_updated: "2026-01-01",
-  attachment: false,
-  reasoning: false,
-  tool_call: false,
-  open_weights: false,
-  cost: { input: 1, output: 2 },
-  limit: { context: 1_000, output: 100 },
-  modalities: { input: ["text"], output: ["text"] },
-};
-
-test("reasoning budgets allow only the -1 negative sentinel", () => {
-  const authored = { id: "model", ...model };
-
-  expect(AuthoredModelShape.safeParse({
-    ...authored,
-    reasoning_options: [{ type: "budget_tokens", min: -1, max: 32_768 }],
-  }).success).toBe(true);
-  expect(AuthoredModelShape.safeParse({
-    ...authored,
-    reasoning_options: [{ type: "budget_tokens", min: -2, max: 32_768 }],
-  }).success).toBe(false);
-});
-
-test("reasoning efforts accept the provider default value", () => {
-  expect(AuthoredModelShape.safeParse({
-    id: "model",
-    ...model,
-    reasoning: true,
-    reasoning_options: [{ type: "effort", values: ["none", "default"] }],
-  }).success).toBe(true);
-});
-
-async function fixture() {
-  const root = await mkdtemp(path.join(os.tmpdir(), "models-dev-sync-"));
-  const modelsDir = path.join(root, "providers", "test", "models");
-  await mkdir(modelsDir, { recursive: true });
-  return { root, modelsDir };
-}
-
-function provider(
-  modelsDir: string,
-  ids: string[],
-  deleteMissing = true,
-  preserveSymlinks = false,
-): SyncProvider<string> {
-  return {
-    id: "test",
-    name: "Test",
-    modelsDir,
-    deleteMissing,
-    preserveSymlinks,
-    missingNotice: (paths) => paths.map((item) => `missing: ${item}`),
-    async fetchModels() {
-      return ids;
-    },
-    parseModels(raw) {
-      return raw as string[];
-    },
-    translateModel(id) {
-      return { id, model };
-    },
-  };
-}
-
-test("sync repairs a broken symlink returned by the source", async () => {
-  const { modelsDir } = await fixture();
-  const filePath = path.join(modelsDir, "model.toml");
-  await symlink("missing.toml", filePath);
-
-  const result = await syncProvider(provider(modelsDir, ["model"]));
-
-  expect(result.created).toBe(1);
-  expect(await Bun.file(filePath).text()).toContain('name = "Test model"');
-  expect(readlink(filePath)).rejects.toThrow();
-});
-
-test("sync preserves valid symlink aliases when configured", async () => {
-  const { root, modelsDir } = await fixture();
-  const targetPath = path.join(root, "target.toml");
-  const filePath = path.join(modelsDir, "model.toml");
-  await Bun.write(targetPath, `name = "Alias target"\n`);
-  await symlink(targetPath, filePath);
-
-  const result = await syncProvider(provider(modelsDir, ["model"], true, true));
-
-  expect(result.updated).toBe(0);
-  expect(await readlink(filePath)).toBe(targetPath);
-});
-
-test("sync removes a broken symlink absent from the source", async () => {
-  const { modelsDir } = await fixture();
-  const filePath = path.join(modelsDir, "model.toml");
-  await symlink("missing.toml", filePath);
-
-  const result = await syncProvider(provider(modelsDir, []));
-
-  expect(result.deleted).toBe(1);
-  expect(await Bun.file(filePath).exists()).toBe(false);
-});
-
-test("non-deleting sync reports missing broken symlinks", async () => {
-  const { modelsDir } = await fixture();
-  const filePath = path.join(modelsDir, "model.toml");
-  await symlink("missing.toml", filePath);
-
-  const result = await syncProvider(provider(modelsDir, [], false));
-
-  expect(result.deleted).toBe(0);
-  expect(result.notices).toEqual(["missing: model.toml"]);
-  expect(await readlink(filePath)).toBe("missing.toml");
-});
-
-test("sync preserves authored reasoning options omitted by a translator", async () => {
-  const { modelsDir } = await fixture();
-  const filePath = path.join(modelsDir, "model.toml");
-  await Bun.write(filePath, `name = "Old name"
-release_date = "2026-01-01"
-last_updated = "2026-01-01"
-attachment = false
-reasoning = true
-tool_call = false
-open_weights = false
-
-[[reasoning_options]]
-type = "effort"
-values = ["low", "high"]
-
-[[reasoning_options]]
-type = "budget_tokens"
-min = -1
-max = 32768
-
-[cost]
-input = 1
-output = 2
-
-[limit]
-context = 1000
-output = 100
-
-[modalities]
-input = ["text"]
-output = ["text"]
-`);
-  const sync = provider(modelsDir, ["model"]);
-  sync.translateModel = (id) => ({
-    id,
-    model: { ...model, reasoning: true },
-  });
-
-  const first = await syncProvider(sync);
-  const content = await Bun.file(filePath).text();
-  const second = await syncProvider(sync);
-
-  expect(first.updated).toBe(1);
-  expect(content).toContain("[[reasoning_options]]");
-  expect(content).toContain('values = ["low", "high"]');
-  expect(content).toContain("min = -1");
-  expect(content).toContain("max = 32_768");
-  expect(second.updated).toBe(0);
-  expect(second.unchanged).toBe(1);
-});
-
-test("sync writes metadata returned by a provider translator", async () => {
-  const root = await mkdtemp(path.join(os.tmpdir(), "models-dev-sync-metadata-"));
-  const modelsDir = path.join(root, "providers", "test", "models");
-  await mkdir(modelsDir, { recursive: true });
-  const sync = provider(modelsDir, ["model"]);
-  sync.translateModel = () => ({
-    id: "model",
-    model: {
-      base_model: "test/model",
-      reasoning_options: [],
-      cost: { input: 1, output: 2 },
-    },
-    metadata: {
-      id: "test/model",
-      model: {
-        name: "Model",
-        release_date: "2026-06-10",
-        last_updated: "2026-06-10",
-        attachment: false,
-        reasoning: false,
-        tool_call: true,
-        open_weights: false,
-        limit: { context: 1_000, output: 100 },
-        modalities: { input: ["text"], output: ["text"] },
-      },
-    },
-  });
-
-  const first = await syncProvider(sync);
-  const second = await syncProvider(sync);
-
-  expect(first).toMatchObject({ created: 2, updated: 0 });
-  expect(second).toMatchObject({ created: 0, updated: 0 });
-  expect(await Bun.file(path.join(root, "models", "test", "model.toml")).text()).toContain('name = "Model"');
-});
-
-test("sync removes missing metadata only from its owned namespace", async () => {
-  const { root, modelsDir } = await fixture();
-  const ownedDir = path.join(root, "models", "test");
-  const otherDir = path.join(root, "models", "other");
-  await mkdir(ownedDir, { recursive: true });
-  await mkdir(otherDir, { recursive: true });
-  await Bun.write(path.join(ownedDir, "stale.toml"), 'name = "Stale"\n');
-  await Bun.write(path.join(otherDir, "retained.toml"), 'name = "Retained"\n');
-  const sync = provider(modelsDir, []);
-  sync.metadataNamespace = "test";
-
-  const result = await syncProvider(sync);
-
-  expect(result.deleted).toBe(1);
-  expect(await Bun.file(path.join(ownedDir, "stale.toml")).exists()).toBe(false);
-  expect(await Bun.file(path.join(otherDir, "retained.toml")).exists()).toBe(true);
-});
@@ -0,0 +1,49 @@
+import { expect, test } from "bun:test";
+
+import { formatToml } from "../src/sync/index.js";
+
+test("formats interleaved as a root field before reasoning option tables", () => {
+  const content = formatToml({
+    id: "example/model",
+    name: "Example Model",
+    release_date: "2026-01-01",
+    last_updated: "2026-01-01",
+    attachment: false,
+    reasoning: true,
+    reasoning_options: [{ type: "toggle" }],
+    tool_call: true,
+    interleaved: true,
+    open_weights: false,
+    cost: { input: 1, output: 2 },
+    limit: { context: 1_000, output: 100 },
+    modalities: { input: ["text"], output: ["text"] },
+  });
+
+  expect(Bun.TOML.parse(content)).toMatchObject({
+    interleaved: true,
+    reasoning_options: [{ type: "toggle" }],
+  });
+});
+
+test("formats empty reasoning options outside the interleaved table", () => {
+  const content = formatToml({
+    id: "example/model",
+    name: "Example Model",
+    release_date: "2026-01-01",
+    last_updated: "2026-01-01",
+    attachment: false,
+    reasoning: true,
+    reasoning_options: [],
+    tool_call: true,
+    interleaved: { field: "reasoning_content" },
+    open_weights: false,
+    cost: { input: 1, output: 2 },
+    limit: { context: 1_000, output: 100 },
+    modalities: { input: ["text"], output: ["text"] },
+  });
+
+  expect(Bun.TOML.parse(content)).toMatchObject({
+    interleaved: { field: "reasoning_content" },
+    reasoning_options: [],
+  });
+});
@@ -1,167 +0,0 @@
-import { expect, test } from "bun:test";
-import { readdirSync } from "node:fs";
-import path from "node:path";
-
-import {
-  buildVeniceModel,
-  resolveVeniceBaseModel,
-  venice,
-  VeniceResponse,
-  type VeniceModel,
-} from "../src/sync/providers/venice.js";
-
-const catalogModel: VeniceModel = {
-  id: "openai-gpt-54",
-  created: 1_772_668_800,
-  model_spec: {
-    name: "GPT-5.4",
-    availableContextTokens: 400_000,
-    maxCompletionTokens: 128_000,
-    modelSource: "OpenAI",
-    capabilities: {
-      supportsVision: true,
-      supportsReasoning: true,
-      supportsReasoningEffort: true,
-      reasoningEffortOptions: ["none", "low", "medium", "high"],
-      supportsFunctionCalling: true,
-      supportsResponseSchema: true,
-    },
-    pricing: {
-      input: { usd: 3.13 },
-      output: { usd: 18.75 },
-      cache_input: { usd: 0.313 },
-      extended: {
-        context_token_threshold: 200_000,
-        input: { usd: 6.26 },
-        output: { usd: 28.125 },
-      },
-    },
-  },
-};
-
-test("Venice resolves flattened IDs to canonical metadata", () => {
-  expect(resolveVeniceBaseModel("openai-gpt-54", "GPT-5.4")).toBe("openai/gpt-5.4");
-  expect(resolveVeniceBaseModel("claude-opus-4-8-fast", "Claude Opus 4.8 Fast"))
-    .toBe("anthropic/claude-opus-4-8");
-});
-
-test("Venice emits empty reasoning options when efforts are unavailable", () => {
-  const synced = buildVeniceModel({
-    ...catalogModel,
-    id: "reasoning-without-efforts",
-    model_spec: {
-      ...catalogModel.model_spec,
-      name: "Reasoning Without Efforts",
-      capabilities: {
-        ...catalogModel.model_spec.capabilities,
-        reasoningEffortOptions: [],
-      },
-    },
-  }, undefined, undefined, "2026-06-10");
-
-  expect(synced).toMatchObject({ reasoning: true, reasoning_options: [] });
-});
-
-test("Venice does not infer temperature support", () => {
-  const synced = buildVeniceModel(catalogModel, undefined, null, "2026-06-10");
-
-  expect(synced.temperature).toBeUndefined();
-});
-
-test("Venice skips E2EE models", () => {
-  const translated = venice.translateModel({
-    ...catalogModel,
-    id: "e2ee-test-model",
-    model_spec: {
-      ...catalogModel.model_spec,
-      capabilities: { ...catalogModel.model_spec.capabilities, supportsE2EE: true },
-    },
-  }, { existing: () => undefined });
-
-  expect(translated).toBeUndefined();
-});
-
-test("Venice uses boundary-aware family matching", () => {
-  const synced = buildVeniceModel({
-    ...catalogModel,
-    id: "google-gemma-4-31b-it",
-    model_spec: { ...catalogModel.model_spec, name: "Google Gemma 4 31B Instruct" },
-  }, undefined, null, "2026-06-10");
-
-  expect(synced).toMatchObject({ family: "gemma" });
-});
-
-test("Venice maps API fields without bumping inherited model timestamps", () => {
-  const synced = buildVeniceModel(catalogModel, {
-    base_model: "openai/gpt-5.4",
-    name: "GPT-5.4",
-    family: "gpt",
-    release_date: "2026-03-05",
-    last_updated: "2026-03-09",
-    attachment: true,
-    reasoning: true,
-    tool_call: true,
-    structured_output: true,
-    temperature: true,
-    open_weights: false,
-    interleaved: { field: "reasoning_content" },
-    cost: { input: 3, output: 18, input_audio: 4 },
-    limit: { context: 400_000, output: 128_000 },
-    modalities: { input: ["text", "image", "pdf"], output: ["text"] },
-  }, "openai/gpt-5.4", "2026-06-10");
-
-  expect(synced).toMatchObject({
-    base_model: "openai/gpt-5.4",
-    base_model_omit: ["limit.input"],
-    last_updated: "2026-03-09",
-    reasoning_options: [{ type: "effort", values: ["none", "low", "medium", "high"] }],
-    interleaved: { field: "reasoning_content" },
-    cost: {
-      input: 3.13,
-      output: 18.75,
-      cache_read: 0.313,
-      input_audio: 4,
-      tiers: [{ tier: { type: "context", size: 200_000 }, input: 6.26, output: 28.125 }],
-    },
-  });
-  expect(synced).not.toHaveProperty("family");
-  expect(synced).not.toHaveProperty("release_date");
-  expect(synced).not.toHaveProperty("open_weights");
-  expect(synced).not.toHaveProperty("modalities");
-  expect(synced).not.toHaveProperty("temperature");
-});
-
-test("Venice preserves last_updated when authoritative data is unchanged", () => {
-  const providerModel = {
-    ...catalogModel,
-    id: "venice-only-test-model",
-    model_spec: { ...catalogModel.model_spec, name: "Venice Only Test Model" },
-  };
-  const full = buildVeniceModel(providerModel, undefined, undefined, "2026-06-10");
-  if ("base_model" in full) throw new Error("Expected a full provider model fixture");
-  const synced = buildVeniceModel(providerModel, full, undefined, "2026-06-11");
-
-  expect(synced).toMatchObject({ last_updated: "2026-06-10" });
-});
-
-test("Venice rejects malformed responses", () => {
-  expect(() => VeniceResponse.parse({ data: [{ id: "broken" }] })).toThrow();
-});
-
-test("Venice models use only canonical metadata and declare reasoning options", async () => {
-  const root = path.join(import.meta.dirname, "..", "..", "..");
-  const modelsDir = path.join(root, "providers", "venice", "models");
-
-  for (const file of readdirSync(modelsDir).filter((item) => item.endsWith(".toml"))) {
-    const model = Bun.TOML.parse(await Bun.file(path.join(modelsDir, file)).text()) as {
-      base_model?: string;
-      reasoning_options?: unknown[];
-    };
-    expect(model.reasoning_options, file).toBeDefined();
-    if (model.base_model !== undefined) {
-      expect(model.base_model.startsWith("venice/"), file).toBe(false);
-      expect(await Bun.file(path.join(root, "models", `${model.base_model}.toml`)).exists(), file).toBe(true);
-    }
-    expect(file.startsWith("e2ee-"), file).toBe(false);
-  }
-});
@@ -1,113 +0,0 @@
-import { expect, test } from "bun:test";
-
-import { buildVercelModel, type VercelModel, vercel } from "../src/sync/providers/vercel.js";
-
-const model: VercelModel = {
-  id: "openai/gpt-test",
-  name: "GPT Test",
-  created: 1_700_000_000,
-  released: 1_710_000_000,
-  context_window: 128_000,
-  max_tokens: 32_000,
-  type: "language",
-  tags: ["reasoning", "tool-use", "vision", "file-input"],
-  pricing: {
-    input: "0.000001",
-    output: "0.000004",
-    input_cache_read: "0.0000001",
-  },
-};
-
-test("Vercel models translate gateway metadata", () => {
-  const synced = buildVercelModel(model, undefined);
-
-  expect(synced).toMatchObject({
-    name: "GPT Test",
-    release_date: "2024-03-09",
-    last_updated: "2024-03-09",
-    attachment: true,
-    reasoning: true,
-    tool_call: true,
-    open_weights: false,
-    cost: { input: 1, output: 4, cache_read: 0.1 },
-    limit: { context: 128_000, input: 96_000, output: 32_000 },
-    modalities: { input: ["text", "image", "pdf"], output: ["text"] },
-  });
-});
-
-test("Vercel models preserve curated metadata and missing limits", () => {
-  const synced = buildVercelModel({
-    ...model,
-    context_window: 0,
-    max_tokens: 0,
-  }, {
-    name: "Curated name",
-    release_date: "2024-01-01",
-    last_updated: "2025-01-01",
-    reasoning_options: [{ type: "effort", values: ["low", "high"] }],
-    cost: {
-      input: 2,
-      output: 8,
-      tiers: [{
-        tier: { type: "context", size: 200_000 },
-        input: 3,
-        output: 12,
-      }],
-    },
-    limit: { context: 64_000, input: 48_000, output: 16_000 },
-  });
-
-  expect(synced.name).toBe("Curated name");
-  expect(synced.last_updated).toBe("2025-01-01");
-  expect(synced.reasoning_options).toEqual([{ type: "effort", values: ["low", "high"] }]);
-  expect(synced.cost?.tiers).toHaveLength(1);
-  expect(synced.limit).toEqual({ context: 64_000, input: 48_000, output: 16_000 });
-});
-
-test("Vercel non-language models use API tool capabilities", () => {
-  const synced = buildVercelModel({
-    ...model,
-    type: "image",
-    tags: [],
-  }, {
-    tool_call: true,
-  });
-
-  expect(synced.tool_call).toBe(false);
-});
-
-test("Vercel sync includes non-language model types", () => {
-  for (const [type, output] of [
-    ["image", ["image"]],
-    ["video", ["video"]],
-    ["reranking", ["text"]],
-  ] as const) {
-    const source = {
-      ...model,
-      id: `test/${type}`,
-      type,
-      tags: [],
-      context_window: 0,
-      max_tokens: 0,
-      pricing: undefined,
-    };
-
-    expect(vercel.translateModel(source, { existing: () => undefined })).toBeDefined();
-    expect(buildVercelModel(source, undefined)).toMatchObject({
-      tool_call: false,
-      modalities: { input: ["text"], output },
-    });
-  }
-});
-
-test("Vercel models use canonical metadata when available", () => {
-  const synced = buildVercelModel({
-    ...model,
-    id: "nvidia/nemotron-3-ultra-550b-a55b",
-    name: "Nemotron 3 Ultra",
-  }, undefined);
-
-  expect("base_model" in synced ? synced.base_model : undefined)
-    .toBe("nvidia/nemotron-3-ultra-550b-a55b");
-  expect("last_updated" in synced ? synced.last_updated : undefined).toBeUndefined();
-});
@@ -1,30 +0,0 @@
-import { expect, test } from "bun:test";
-
-import { buildXAIModel, type XAIModel } from "../src/sync/providers/xai.js";
-
-const model: XAIModel = {
-  id: "grok-test",
-  created: 1_700_000_000,
-  input_modalities: ["text"],
-  output_modalities: ["text"],
-  prompt_text_token_price: 10_000,
-  completion_text_token_price: 20_000,
-};
-
-test("xAI sync preserves reasoning options", () => {
-  const synced = buildXAIModel(model, {
-    name: "Grok Test",
-    release_date: "2024-01-01",
-    last_updated: "2024-01-01",
-    attachment: false,
-    reasoning: true,
-    reasoning_options: [{ type: "effort", values: ["none", "high"] }],
-    tool_call: true,
-    open_weights: false,
-    limit: { context: 2_000_000, output: 30_000 },
-    modalities: { input: ["text"], output: ["text"] },
-  });
-
-  expect(synced.reasoning_options).toEqual([{ type: "effort", values: ["none", "high"] }]);
-  expect(synced.limit?.context).toBe(2_000_000);
-});
@@ -4,6 +4,7 @@ release_date = "2025-10-16"
 last_updated = "2025-10-16"
 attachment = true
 reasoning = true
+reasoning_options = [{ type = "toggle" }, { type = "budget_tokens", min = 1024, max = 63999 }]
 temperature = true
 tool_call = true
 open_weights = false
@@ -4,6 +4,7 @@ release_date = "2025-10-16"
 last_updated = "2025-10-16"
 attachment = true
 reasoning = true
+reasoning_options = [{ type = "toggle" }, { type = "budget_tokens", min = 1024, max = 63999 }]
 temperature = true
 tool_call = true
 open_weights = false
@@ -3,6 +3,7 @@ release_date = "2025-05-27"
 last_updated = "2025-05-27"
 attachment = true
 reasoning = true
+reasoning_options = []
 temperature = true
 tool_call = true
 open_weights = false
@@ -4,6 +4,7 @@ release_date = "2025-08-05"
 last_updated = "2025-08-05"
 attachment = true
 reasoning = true
+reasoning_options = [{ type = "toggle" }, { type = "budget_tokens", min = 1024, max = 31999 }]
 temperature = true
 tool_call = true
 open_weights = false
@@ -4,6 +4,7 @@ release_date = "2025-05-22"
 last_updated = "2025-05-22"
 attachment = true
 reasoning = true
+reasoning_options = [{ type = "toggle" }, { type = "budget_tokens", min = 1024, max = 31999 }]
 temperature = true
 tool_call = true
 open_weights = false
@@ -3,6 +3,7 @@ release_date = "2025-11-25"
 last_updated = "2025-11-25"
 attachment = true
 reasoning = true
+reasoning_options = []
 temperature = true
 tool_call = true
 open_weights = false
@@ -4,6 +4,7 @@ release_date = "2025-11-25"
 last_updated = "2025-11-25"
 attachment = true
 reasoning = true
+reasoning_options = [{ type = "toggle" }, { type = "effort", values = ["low", "medium", "high"] }, { type = "budget_tokens", min = 1024, max = 63999 }]
 temperature = true
 tool_call = true
 open_weights = false
@@ -4,6 +4,7 @@ release_date = "2025-11-25"
 last_updated = "2025-11-25"
 attachment = true
 reasoning = true
+reasoning_options = [{ type = "toggle" }, { type = "effort", values = ["low", "medium", "high"] }, { type = "budget_tokens", min = 1024, max = 63999 }]
 temperature = true
 tool_call = true
 open_weights = false
@@ -3,6 +3,7 @@ release_date = "2026-02-06"
 last_updated = "2026-03-13"
 attachment = true
 reasoning = true
+reasoning_options = []
 temperature = true
 tool_call = true
 open_weights = false
@@ -4,6 +4,7 @@ release_date = "2026-02-06"
 last_updated = "2026-03-13"
 attachment = true
 reasoning = true
+reasoning_options = [{ type = "toggle" }, { type = "effort", values = ["low", "medium", "high", "max"] }, { type = "budget_tokens", min = 1024, max = 127999 }]
 temperature = true
 tool_call = true
 open_weights = false
@@ -4,6 +4,7 @@ release_date = "2026-04-17"
 last_updated = "2026-04-17"
 attachment = true
 reasoning = true
+reasoning_options = [{ type = "toggle" }, { type = "effort", values = ["low", "medium", "high", "xhigh", "max"] }]
 temperature = true
 tool_call = true
 open_weights = false
@@ -4,6 +4,7 @@ release_date = "2025-05-22"
 last_updated = "2025-05-22"
 attachment = true
 reasoning = true
+reasoning_options = [{ type = "toggle" }, { type = "budget_tokens", min = 1024, max = 63999 }]
 temperature = true
 tool_call = true
 open_weights = false
@@ -3,6 +3,7 @@ release_date = "2025-09-30"
 last_updated = "2025-09-30"
 attachment = true
 reasoning = true
+reasoning_options = []
 temperature = true
 tool_call = true
 open_weights = false
@@ -4,6 +4,7 @@ release_date = "2025-09-30"
 last_updated = "2025-09-30"
 attachment = true
 reasoning = true
+reasoning_options = [{ type = "toggle" }, { type = "budget_tokens", min = 1024, max = 63999 }]
 temperature = true
 tool_call = true
 open_weights = false
@@ -4,6 +4,7 @@ release_date = "2025-09-30"
 last_updated = "2025-09-30"
 attachment = true
 reasoning = true
+reasoning_options = [{ type = "toggle" }, { type = "budget_tokens", min = 1024, max = 63999 }]
 temperature = true
 tool_call = true
 open_weights = false
@@ -3,6 +3,7 @@ release_date = "2026-02-18"
 last_updated = "2026-03-13"
 attachment = true
 reasoning = true
+reasoning_options = []
 temperature = true
 tool_call = true
 open_weights = false
@@ -4,6 +4,7 @@ release_date = "2026-02-18"
 last_updated = "2026-03-13"
 attachment = true
 reasoning = true
+reasoning_options = [{ type = "toggle" }, { type = "budget_tokens", min = 1024, max = 63999 }]
 temperature = true
 tool_call = true
 open_weights = false
@@ -4,6 +4,7 @@ release_date = "2025-01-20"
 last_updated = "2025-01-20"
 attachment = false
 reasoning = true
+reasoning_options = []
 temperature = true
 tool_call = true
 open_weights = false
@@ -3,6 +3,7 @@ release_date = "2025-12-01"
 last_updated = "2025-12-01"
 attachment = false
 reasoning = true
+reasoning_options = []
 temperature = true
 tool_call = true
 open_weights = false
@@ -3,6 +3,7 @@ release_date = "2025-07-15"
 last_updated = "2025-07-15"
 attachment = true
 reasoning = true
+reasoning_options = []
 temperature = true
 tool_call = true
 open_weights = false
@@ -4,6 +4,7 @@ release_date = "2025-07-29"
 last_updated = "2025-07-29"
 attachment = false
 reasoning = true
+reasoning_options = [{ type = "toggle" }]
 temperature = true
 tool_call = true
 open_weights = true
@@ -4,6 +4,7 @@ release_date = "2025-07-29"
 last_updated = "2025-07-29"
 attachment = false
 reasoning = true
+reasoning_options = [{ type = "toggle" }]
 temperature = true
 tool_call = true
 open_weights = true
@@ -4,6 +4,7 @@ release_date = "2025-08-12"
 last_updated = "2025-08-12"
 attachment = true
 reasoning = true
+reasoning_options = [{ type = "toggle" }]
 temperature = true
 tool_call = true
 open_weights = true
@@ -4,6 +4,7 @@ release_date = "2025-09-30"
 last_updated = "2025-09-30"
 attachment = false
 reasoning = true
+reasoning_options = [{ type = "toggle" }]
 temperature = true
 tool_call = true
 open_weights = true
@@ -4,6 +4,7 @@ release_date = "2025-12-08"
 last_updated = "2025-12-08"
 attachment = true
 reasoning = true
+reasoning_options = [{ type = "toggle" }]
 temperature = true
 tool_call = true
 open_weights = true
@@ -4,6 +4,7 @@ release_date = "2026-01-20"
 last_updated = "2026-01-20"
 attachment = false
 reasoning = true
+reasoning_options = [{ type = "toggle" }]
 temperature = true
 tool_call = true
 open_weights = true
@@ -4,6 +4,7 @@ release_date = "2025-12-22"
 last_updated = "2025-12-22"
 attachment = false
 reasoning = true
+reasoning_options = [{ type = "toggle" }]
 temperature = true
 tool_call = true
 open_weights = true
@@ -4,6 +4,7 @@ release_date = "2026-03-16"
 last_updated = "2026-03-16"
 attachment = false
 reasoning = true
+reasoning_options = [{ type = "toggle" }]
 temperature = true
 tool_call = true
 structured_output = true
@@ -4,6 +4,7 @@ release_date = "2026-04-10"
 last_updated = "2026-04-10"
 attachment = false
 reasoning = true
+reasoning_options = [{ type = "toggle" }]
 temperature = true
 tool_call = true
 structured_output = true
@@ -4,6 +4,7 @@ release_date = "2026-02-12"
 last_updated = "2026-02-12"
 attachment = false
 reasoning = true
+reasoning_options = [{ type = "toggle" }]
 temperature = true
 tool_call = true
 open_weights = true
@@ -4,6 +4,7 @@ release_date = "2026-04-02"
 last_updated = "2026-04-02"
 attachment = true
 reasoning = true
+reasoning_options = [{ type = "toggle" }]
 temperature = true
 tool_call = true
 open_weights = false
@@ -4,6 +4,7 @@ release_date = "2025-09-30"
 last_updated = "2025-09-30"
 attachment = false
 reasoning = true
+reasoning_options = []
 temperature = true
 tool_call = true
 open_weights = false
@@ -4,6 +4,7 @@ release_date = "2025-08-08"
 last_updated = "2025-08-08"
 attachment = true
 reasoning = true
+reasoning_options = [{ type = "effort", values = ["minimal", "low", "medium", "high"] }]
 temperature = false
 tool_call = true
 structured_output = true
@@ -4,6 +4,7 @@ release_date = "2025-10-08"
 last_updated = "2025-10-08"
 attachment = true
 reasoning = true
+reasoning_options = [{ type = "effort", values = ["high"] }]
 temperature = false
 tool_call = true
 structured_output = true
@@ -3,6 +3,7 @@ release_date = "2025-08-08"
 last_updated = "2025-08-08"
 attachment = true
 reasoning = true
+reasoning_options = []
 temperature = true
 tool_call = true
 open_weights = false
@@ -4,6 +4,7 @@ release_date = "2025-11-14"
 last_updated = "2025-11-14"
 attachment = true
 reasoning = true
+reasoning_options = [{ type = "effort", values = ["medium"] }]
 temperature = false
 tool_call = true
 structured_output = true
@@ -4,6 +4,7 @@ release_date = "2025-11-14"
 last_updated = "2025-11-14"
 attachment = true
 reasoning = true
+reasoning_options = [{ type = "effort", values = ["none", "low", "medium", "high"] }]
 temperature = false
 tool_call = true
 structured_output = true
@@ -4,6 +4,7 @@ release_date = "2025-12-12"
 last_updated = "2025-12-12"
 attachment = true
 reasoning = true
+reasoning_options = [{ type = "effort", values = ["medium"] }]
 temperature = false
 tool_call = true
 structured_output = true
@@ -4,6 +4,7 @@ release_date = "2025-12-12"
 last_updated = "2025-12-12"
 attachment = true
 reasoning = true
+reasoning_options = [{ type = "effort", values = ["none", "low", "medium", "high", "xhigh"] }]
 temperature = false
 tool_call = true
 structured_output = true
@@ -4,6 +4,7 @@ release_date = "2026-03-19"
 last_updated = "2026-03-19"
 attachment = true
 reasoning = true
+reasoning_options = [{ type = "effort", values = ["none", "low", "medium", "high", "xhigh"] }]
 temperature = false
 tool_call = true
 structured_output = true
@@ -4,6 +4,7 @@ release_date = "2026-03-19"
 last_updated = "2026-03-19"
 attachment = true
 reasoning = true
+reasoning_options = [{ type = "effort", values = ["none", "low", "medium", "high", "xhigh"] }]
 temperature = false
 tool_call = true
 structured_output = true
@@ -4,6 +4,7 @@ release_date = "2026-03-19"
 last_updated = "2026-03-19"
 attachment = true
 reasoning = true
+reasoning_options = [{ type = "effort", values = ["none", "low", "medium", "high", "xhigh"] }]
 temperature = false
 tool_call = true
 structured_output = true
@@ -4,6 +4,7 @@ release_date = "2026-03-19"
 last_updated = "2026-03-19"
 attachment = true
 reasoning = true
+reasoning_options = [{ type = "effort", values = ["none", "low", "medium", "high", "xhigh"] }]
 temperature = false
 tool_call = true
 structured_output = true
@@ -4,6 +4,7 @@ release_date = "2026-03-05"
 last_updated = "2026-03-05"
 attachment = true
 reasoning = true
+reasoning_options = [{ type = "effort", values = ["medium", "high", "xhigh"] }]
 temperature = false
 tool_call = true
 structured_output = false
@@ -4,6 +4,7 @@ release_date = "2026-03-05"
 last_updated = "2026-03-05"
 attachment = true
 reasoning = true
+reasoning_options = [{ type = "effort", values = ["none", "low", "medium", "high", "xhigh"] }]
 temperature = false
 tool_call = true
 structured_output = true
@@ -4,6 +4,7 @@ release_date = "2025-08-08"
 last_updated = "2025-08-08"
 attachment = true
 reasoning = true
+reasoning_options = [{ type = "effort", values = ["minimal", "low", "medium", "high"] }]
 temperature = false
 tool_call = true
 structured_output = true
@@ -3,6 +3,7 @@ release_date = "2025-11-20"
 last_updated = "2025-11-20"
 attachment = true
 reasoning = true
+reasoning_options = []
 temperature = true
 tool_call = true
 open_weights = false
@@ -3,6 +3,7 @@ release_date = "2025-09-23"
 last_updated = "2025-09-23"
 attachment = true
 reasoning = true
+reasoning_options = []
 temperature = true
 tool_call = true
 open_weights = false
@@ -3,6 +3,7 @@ release_date = "2026-03-16"
 last_updated = "2026-03-16"
 attachment = true
 reasoning = true
+reasoning_options = []
 temperature = true
 tool_call = true
 open_weights = false
@@ -3,6 +3,7 @@ release_date = "2026-03-16"
 last_updated = "2026-03-16"
 attachment = true
 reasoning = true
+reasoning_options = [{ type = "effort", values = ["low", "medium", "high", "xhigh"] }]
 temperature = true
 tool_call = true
 open_weights = false
@@ -3,6 +3,7 @@ release_date = "2025-09-05"
 last_updated = "2025-09-05"
 attachment = false
 reasoning = true
+reasoning_options = []
 temperature = true
 tool_call = true
 open_weights = false
@@ -3,6 +3,7 @@ release_date = "2025-09-05"
 last_updated = "2025-09-05"
 attachment = false
 reasoning = true
+reasoning_options = []
 temperature = true
 tool_call = true
 open_weights = false
@@ -1,5 +1,11 @@
 name = "302.AI"
 env = ["302AI_API_KEY"]
 npm = "@ai-sdk/openai-compatible"
+# Reasoning HTTP format (accessed 2026-06-25):
+# Audited POST https://api.302.ai/v1/chat/completions. The provider's API guide
+# documents model/messages only; no reasoning toggle, effort, or numeric budget
+# request field is documented. Do not infer passthrough from upstream APIs.
+# Sources:
+# https://doc.302.ai/
 doc = "https://doc.302.ai"
 api = "https://api.302.ai/v1"
@@ -1,5 +1,11 @@
 name = "Abacus"
 npm = "@ai-sdk/openai-compatible"
+# Reasoning HTTP format (accessed 2026-06-25):
+# Audited POST https://routellm.abacus.ai/v1/chat/completions. The provider API
+# reference documents no reasoning toggle, effort, or numeric budget request
+# field. Do not infer behavior from the routed model developer's API.
+# Sources:
+# https://abacus.ai/help/api
 env = ["ABACUS_API_KEY"]
 doc = "https://abacus.ai/help/api"
 api = "https://routellm.abacus.ai/v1"
@@ -1,4 +1,10 @@
 name = "Abliterated Model"
+# Reasoning HTTP format (accessed 2026-06-25):
+# This model thinks by default. On POST /v1/chat/completions or /v1/messages,
+# top-level `thinking: false` skips thinking; omission keeps it enabled.
+# Sources:
+# https://docs.abliteration.ai/models
+# https://docs.abliteration.ai/capabilities/thinking
 release_date = "2026-01-06"
 last_updated = "2026-01-06"
 attachment = true
@@ -1,5 +1,12 @@
 name = "abliteration.ai"
 env = ["ABLIT_KEY"]
 npm = "@ai-sdk/openai-compatible"
+# Reasoning HTTP format (accessed 2026-06-25):
+# POST /v1/chat/completions and POST /v1/messages: top-level `thinking` is true
+# by default; false skips thinking. POST /v1/responses has no thinking toggle.
+# No effort or numeric reasoning-budget request field is documented.
+# Sources:
+# https://docs.abliteration.ai/capabilities/thinking
+# https://docs.abliteration.ai/compatibility-matrix
 api = "https://api.abliteration.ai/v1"
 doc = "https://docs.abliteration.ai/models"
@@ -4,6 +4,7 @@ release_date = "2026-04-24"
 last_updated = "2026-04-24"
 attachment = false
 reasoning = true
+reasoning_options = [{ type = "toggle" }]
 temperature = true
 tool_call = true
 structured_output = true
@@ -4,6 +4,7 @@ release_date = "2026-04-24"
 last_updated = "2026-04-24"
 attachment = false
 reasoning = true
+reasoning_options = [{ type = "toggle" }]
 temperature = true
 tool_call = true
 structured_output = true
@@ -4,6 +4,7 @@ release_date = "2026-03-27"
 last_updated = "2026-03-27"
 attachment = false
 reasoning = true
+reasoning_options = [{ type = "toggle" }]
 temperature = true
 tool_call = true
 structured_output = true
@@ -4,6 +4,7 @@ release_date = "2026-02-05"
 last_updated = "2026-03-13"
 attachment = true
 reasoning = true
+reasoning_options = [{ type = "toggle" }, { type = "effort", values = ["low", "medium", "high", "max"] }, { type = "budget_tokens", min = 1_024 }]
 temperature = true
 tool_call = true
 structured_output = true
--- a/Show More
+++ b/Show More