can1357
6d2bae2c41
- Added long-context pricing tiers and billing policies for subscription Codex models in the catalog. - Introduced the `extendedContext` configuration setting to control premium long-context windows. - Implemented runtime policy refresh and model re-binding when context settings change. - Added comprehensive unit tests for pricing tiers, context capping, and policy toggling behavior.
553 lines
22 KiB
TypeScript
553 lines
22 KiB
TypeScript
/**
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* Generation-time catalog policies: upstream metadata corrections, derived
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* field baking, and promotion-target linking. Runs only from
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* `generate-models.ts` — none of this ships in the runtime bundle.
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*/
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import { buildCompat } from "../src/build";
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import {
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type AnthropicModel,
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bareModelId,
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isFableOrMythos,
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type OpenAIModel,
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type OpenAIVariant,
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type ParsedModel,
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parseKnownModel,
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semverEqual,
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} from "../src/identity/classify";
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import { isMimoModelIdOrName } from "../src/identity/family";
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import { getLongestModelLikeIdSegment } from "../src/identity/id";
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import { buildModelReferenceIndex, resolveModelReference } from "../src/identity/reference";
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import { resolveModelThinking } from "../src/model-thinking";
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import { isOllamaCloudOutputCapped, OLLAMA_CLOUD_MAX_OUTPUT_TOKENS } from "../src/provider-models/ollama";
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import {
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ALIBABA_TOKEN_PLAN_STATIC_MODELS,
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applyXaiResponsesThinkingPolicy,
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OPENAI_GPT_56_LONG_CONTEXT_COSTS,
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resolveWaferServerlessThinkingFormat,
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} from "../src/provider-models/openai-compat";
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import type { Api, LongContextTokenCost, Model, ModelSpec } from "../src/types";
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import { isVariantCollapsedSpec } from "../src/variant-collapse";
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import { buildCanonicalModelIndex, buildCanonicalReferenceData } from "./equivalence";
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const CLOUDFLARE_AI_GATEWAY_BASE_URL = "https://gateway.ai.cloudflare.com/v1/<account>/<gateway>/anthropic";
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/**
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* Static fallback model injected when Cloudflare AI Gateway discovery
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* returns no results. Ensures the provider always has at least one usable
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* model entry in the catalog.
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*/
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export const CLOUDFLARE_FALLBACK_MODEL: ModelSpec<"anthropic-messages"> = {
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id: "claude-sonnet-4-5",
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name: "Claude Sonnet 4.5",
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api: "anthropic-messages",
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provider: "cloudflare-ai-gateway",
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baseUrl: CLOUDFLARE_AI_GATEWAY_BASE_URL,
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reasoning: true,
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input: ["text", "image"],
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cost: {
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input: 3,
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output: 15,
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cacheRead: 0.3,
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cacheWrite: 3.75,
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},
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contextWindow: 200000,
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maxTokens: 64000,
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};
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/**
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* `stencil.so` currently lists `jp.anthropic.claude-opus-5`, but AWS's own
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* Bedrock model card documents only `anthropic.claude-opus-5` plus the `us.`,
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* `eu.`, `au.`, and `global.` Geo/Global inference-profile IDs under
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* Programmatic Access; Japan regions are marked unsupported for Geo inference
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* in the same card's regional-availability table. Bedrock rejects an
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* undocumented inference-profile ID outright, so drop this specific upstream
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* row rather than ship a selector that 4xxs on first use (PR #6591 review).
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* https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-anthropic-claude-opus-5.html
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*/
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export function dropUnsupportedBedrockGeoIds(models: readonly ModelSpec[]): ModelSpec[] {
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return models.filter(model => !(model.provider === "amazon-bedrock" && model.id === "jp.anthropic.claude-opus-5"));
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}
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const BEDROCK_MANTLE_OPENAI_MODEL_IDS: Record<string, true> = {
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"openai.gpt-5.4": true,
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"openai.gpt-5.5": true,
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"openai.gpt-5.6-luna": true,
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"openai.gpt-5.6-sol": true,
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"openai.gpt-5.6-terra": true,
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};
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/**
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* models.dev exposes these Responses-only models under amazon-bedrock, whose
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* descriptor uses Converse. The working Mantle rows come from the static seed.
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*/
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export function dropBedrockMantleOpenAIModels(models: readonly ModelSpec[]): ModelSpec[] {
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return models.filter(model => !(model.provider === "amazon-bedrock" && BEDROCK_MANTLE_OPENAI_MODEL_IDS[model.id]));
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}
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/** True when any component of a model's per-million-token cost is nonzero. */
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export function hasBillableCost(cost: ModelSpec["cost"]): boolean {
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return cost.input !== 0 || cost.output !== 0 || cost.cacheRead !== 0 || cost.cacheWrite !== 0;
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}
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/**
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* Providers whose first-party list prices back-fill Antigravity's unpriced
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* rows, in lookup order. Antigravity discovery reports no pricing (the
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* subscription bills upstream), so without this the whole provider surfaces
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* $0 cost for every request. Antigravity bills through Google, so Vertex
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* prices outrank Anthropic list prices for Claude ids.
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*/
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const ANTIGRAVITY_PRICING_PEERS = ["google", "google-vertex", "anthropic"] as const;
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/**
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* Antigravity ids whose Google peer ships under a different id: Gemini
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* previews carry a `-preview` suffix on the Google API, Claude ids carry a
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* Vertex `@<version>` suffix. A dangling alias (retired Vertex id) falls back
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* to the plain-id lookup, i.e. Anthropic list prices for Claude.
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*/
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const ANTIGRAVITY_PRICING_ID_ALIASES: Readonly<Record<string, string>> = {
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"gemini-3-flash": "gemini-3-flash-preview",
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"gemini-3-pro": "gemini-3-pro-preview",
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"gemini-3.1-pro": "gemini-3.1-pro-preview",
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"claude-opus-4-5": "claude-opus-4-5@20251101",
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"claude-opus-4-6": "claude-opus-4-6@default",
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"claude-sonnet-4-5": "claude-sonnet-4-5@20250929",
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"claude-sonnet-4-6": "claude-sonnet-4-6@default",
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};
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/**
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* Price `google-antigravity` models at their first-party equivalents: Gemini
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* ids at Google API list prices, Claude ids at Google Vertex list prices
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* (falling back to Anthropic). Models without a priced peer (gpt-oss,
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* internal tab models) keep zero cost.
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*/
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export function applyAntigravityPricingFallback(models: readonly ModelSpec[]): ModelSpec[] {
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const peerCosts = new Map<string, ModelSpec["cost"]>();
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for (const peer of ANTIGRAVITY_PRICING_PEERS) {
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for (const model of models) {
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if (model.provider === peer && hasBillableCost(model.cost) && !peerCosts.has(model.id)) {
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peerCosts.set(model.id, model.cost);
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}
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}
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}
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return models.map(model => {
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if (model.provider !== "google-antigravity" || hasBillableCost(model.cost)) {
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return model;
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}
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const alias = ANTIGRAVITY_PRICING_ID_ALIASES[model.id];
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const cost = (alias ? peerCosts.get(alias) : undefined) ?? peerCosts.get(model.id);
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return cost ? { ...model, cost: { ...cost } } : model;
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});
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}
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const CODEX_GPT_5_4_PRIORITY_BY_VARIANT: Partial<Record<OpenAIVariant, number>> = {
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base: 0,
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mini: 1,
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nano: 2,
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};
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const CODEX_GPT_5_6_1M_MODEL_IDS: Record<string, true> = {
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"gpt-5.6-luna": true,
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"gpt-5.6-sol": true,
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"gpt-5.6-terra": true,
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};
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const OPENAI_GPT_5_6_LONG_CONTEXT_COST_BY_MODEL_ID: Readonly<Record<string, LongContextTokenCost>> = {
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"daybreak-blue-latest": OPENAI_GPT_56_LONG_CONTEXT_COSTS.sol,
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"gpt-5.6": OPENAI_GPT_56_LONG_CONTEXT_COSTS.sol,
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"gpt-5.6-luna": OPENAI_GPT_56_LONG_CONTEXT_COSTS.luna,
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"gpt-5.6-sol": OPENAI_GPT_56_LONG_CONTEXT_COSTS.sol,
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"gpt-5.6-terra": OPENAI_GPT_56_LONG_CONTEXT_COSTS.terra,
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};
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const OPENAI_NONE_EFFORT_MODEL_IDS: Record<string, true> = {
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"daybreak-blue-latest": true,
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"daybreak-red-latest": true,
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"gpt-5.6": true,
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"gpt-5.6-cyber": true,
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"gpt-5.6-luna": true,
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"gpt-5.6-sol": true,
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"gpt-5.6-terra": true,
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};
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function modelOrRequestIdValue<T>(
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model: Pick<ModelSpec<Api>, "id" | "requestModelId">,
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values: Readonly<Record<string, T>>,
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): T | undefined {
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const direct = values[bareModelId(model.id)];
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if (direct !== undefined) return direct;
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return model.requestModelId === undefined ? undefined : values[bareModelId(model.requestModelId)];
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}
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const COPILOT_GENERATED_LIMITS: Record<string, { contextWindow: number; maxTokens: number }> = {
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"claude-opus-4.6": { contextWindow: 168000, maxTokens: 32000 },
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"gpt-5.2": { contextWindow: 272000, maxTokens: 128000 },
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"gpt-5.4": { contextWindow: 272000, maxTokens: 128000 },
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"gpt-5.4-mini": { contextWindow: 272000, maxTokens: 128000 },
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"grok-code-fast-1": { contextWindow: 192000, maxTokens: 64000 },
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};
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/**
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* Apply upstream metadata corrections to a mutable array of models, then
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* re-bake canonical thinking metadata so generated catalogs always carry the
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* deriver's output for the post-policy spec.
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*/
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export function applyGeneratedModelPolicies(models: ModelSpec<Api>[]): void {
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for (const model of models) {
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applyGeneratedModelPolicy(model);
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rebakeModelThinking(model);
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}
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}
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/**
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* Recompute `thinking` from the canonical deriver, replacing any baked value.
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* Mirrors `buildModel`'s trust-or-derive resolution with trust disabled: the
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* generator is the authority that produces the trusted values. Collapsed
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* effort-tier variants and provider-authored wire ladders are exempt because
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* the generic deriver cannot reproduce that routing metadata.
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*/
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export function rebakeModelThinking(model: ModelSpec<Api>): void {
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if (isVariantCollapsedSpec(model)) return;
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if (
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model.provider === "alibaba-token-plan" &&
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(model.id === "qwen3.8-max-preview" || model.id === "qwen3.8-max") &&
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model.thinking
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) {
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return;
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}
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const requiresProviderAuthoredEffort =
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model.provider === "umans" && (model.thinking?.requiresEffort === true || model.id === "umans-kimi-k2.7");
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const thinking = resolveModelThinking({ ...model, thinking: undefined }, buildCompat(model));
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if (thinking) {
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model.thinking = requiresProviderAuthoredEffort ? { ...thinking, requiresEffort: true } : thinking;
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} else {
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delete model.thinking;
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}
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}
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/**
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* Link OpenAI model variants to their context promotion targets.
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*
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* When a model's context is exhausted, the agent can promote to a sibling model
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* on the same provider:
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* - `codex-spark` variants promote to the full `gpt-5.5`.
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* - every `gpt-5.5` flavor (base, `-pro`, `-instant`, dated snapshots, and
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* namespaced ids like `openai/gpt-5.5`) promotes to its `gpt-5.4` sibling.
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*
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* The sibling is resolved by parsed version + matching provider/api, not a
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* hardcoded bare id, so namespaced (`openrouter/openai/gpt-5.4`), dotted
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* (`amazon-bedrock` `openai.gpt-5.4`), and dated (`gpt-5.4-2026-03-05`) ids all
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* link. The runtime still gates on the target actually being larger
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* (`#resolveContextPromotionTarget`), so an equal/smaller sibling is a harmless
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* no-op rather than a counterproductive switch.
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*/
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export function linkOpenAIPromotionTargets(models: ModelSpec<Api>[]): void {
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for (const candidate of models) {
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const parsedCandidate = parseKnownModel(candidate.id);
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if (parsedCandidate.family !== "openai") continue;
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let targetVersion: string | undefined;
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if (parsedCandidate.variant === "codex-spark") {
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targetVersion = "5.5";
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} else if (semverEqual(parsedCandidate.version, "5.5")) {
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targetVersion = "5.4";
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} else {
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continue;
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}
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// Prefer the plainest sibling id (shortest bare segment) so the base model
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// wins over `-pro`/`-mini`/`-nano` siblings that parse to the same version.
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let fallback: ModelSpec<Api> | undefined;
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let fallbackBareLength = Number.POSITIVE_INFINITY;
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for (const model of models) {
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if (model === candidate) continue;
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if (model.provider !== candidate.provider || model.api !== candidate.api) continue;
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const parsed = parseKnownModel(model.id);
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if (parsed.family !== "openai" || !semverEqual(parsed.version, targetVersion)) continue;
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const bareLength = bareModelId(model.id).length;
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if (bareLength < fallbackBareLength) {
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fallback = model;
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fallbackBareLength = bareLength;
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}
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}
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if (!fallback) continue;
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candidate.contextPromotionTarget = `${fallback.provider}/${fallback.id}`;
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}
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}
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/**
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* Fill `null` `contextWindow` / `maxTokens` from a model's family reference.
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* Proxies and resellers serve first-party models under mangled ids and report
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* no limits, so discovery emits `null` rather than a magic number. Two lookups
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* cover the two ways an id drifts from its family head:
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*
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* 1. Compact / re-spelled versions (`venice/openai-gpt-54-mini`,
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* `aimlapi/moonshot/kimi-k2-5`) — the canonical-equivalence index maps these
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* to their head (`gpt-5.4-mini`, `kimi-k2.5`).
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* 2. Org-namespace variance (`aimlapi/alibaba/qwen3-32b` vs `groq/qwen/qwen3-32b`)
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* — these never share an exact id, so the bare model-segment (`qwen3-32b`)
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* is resolved through the proxy-reference suffix-alias map instead.
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*
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* Both lookups draw metadata from the proxy-reference index, which prefers the
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* largest limits with complete cache pricing and first-party providers, and
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* excludes zero-cost xai-oauth subscription entries (inflated `maxTokens`) as
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* sources. The canonical head is tried first (more precise); the segment alias
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* backfills any field it leaves null.
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*
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* Only `null` fields are filled; provider-specific limits that discovery
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* returned explicitly are never overwritten.
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*/
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export function applyCanonicalLimitFallback(models: ModelSpec<Api>[]): void {
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if (!models.some(model => model.contextWindow === null || model.maxTokens === null)) {
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return;
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}
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// The identity indices read only id/provider/name/limit/cost fields, all of
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// which ModelSpec carries — no built-only field (compat/thinking) is read —
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// so reusing the runtime Model<Api> builders over raw specs is sound.
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const catalog = models as unknown as readonly Model<Api>[];
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const referenceData = buildCanonicalReferenceData(catalog);
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const canonicalIndex = buildCanonicalModelIndex(catalog, referenceData);
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const referenceIndex = buildModelReferenceIndex(catalog);
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for (const model of models) {
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if (model.contextWindow !== null && model.maxTokens !== null) {
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continue;
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}
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const canonicalId = canonicalIndex.bySelector.get(`${model.provider}/${model.id}`.toLowerCase());
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const segment = getLongestModelLikeIdSegment(model.id);
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const references = [
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canonicalId ? resolveModelReference(canonicalId, referenceIndex) : undefined,
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segment ? referenceIndex.suffixAlias.get(segment) : undefined,
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];
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for (const reference of references) {
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if (!reference || (reference.provider === model.provider && reference.id === model.id)) {
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continue;
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}
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if (model.contextWindow === null && reference.contextWindow !== null) {
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model.contextWindow = reference.contextWindow;
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}
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if (model.maxTokens === null && reference.maxTokens !== null) {
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model.maxTokens = reference.maxTokens;
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}
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if (model.contextWindow !== null && model.maxTokens !== null) {
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break;
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}
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}
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}
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}
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/**
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* Pin the max-output figure for Ollama Cloud models whose deployment enforces a
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* lower ceiling than their advertised window.
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*
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* Ollama's `/api/show` never reports a per-model output cap, so discovery and
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* previous snapshots leave `maxTokens` at the full context window (or a stale
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* conservative fallback, as with `deepseek-v4-flash:0731`). DeepSeek V4
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* Pro/Flash deployments actually reject any output budget above
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* {@link OLLAMA_CLOUD_MAX_OUTPUT_TOKENS} (ollama/ollama#16890, #3392/#3394), so
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* pin those ids to `min(contextWindow, ceiling)` — the true amount the endpoint
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* accepts (#7266). Other cloud models keep their discovered limits.
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*/
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export function applyOllamaCloudOutputCap(models: ModelSpec<Api>[]): void {
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for (const model of models) {
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if (model.provider !== "ollama-cloud" || model.contextWindow === null) continue;
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if (!isOllamaCloudOutputCapped(model.id)) continue;
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model.maxTokens = Math.min(model.contextWindow, OLLAMA_CLOUD_MAX_OUTPUT_TOKENS);
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}
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}
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function applyGeneratedModelPolicy(model: ModelSpec<Api>): void {
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if ((model.provider === "xai" || model.provider === "xai-oauth") && model.api === "openai-responses") {
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const updated = applyXaiResponsesThinkingPolicy(model as ModelSpec<"openai-responses">);
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model.compat = updated.compat;
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}
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const copilotLimits = model.provider === "github-copilot" ? COPILOT_GENERATED_LIMITS[model.id] : undefined;
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if (copilotLimits) {
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model.contextWindow = copilotLimits.contextWindow;
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model.maxTokens = copilotLimits.maxTokens;
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}
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if (model.provider === "alibaba-token-plan") {
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const reference = ALIBABA_TOKEN_PLAN_STATIC_MODELS.find(candidate => candidate.id === model.id);
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if (reference) model.name = reference.name;
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}
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if (model.provider === "ollama-cloud") {
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model.omitMaxOutputTokens = true;
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}
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// GLM Coding Plan: the selectable 1M-context served ids; pin them so
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// endpoint discovery or older bundled fallbacks cannot regress to 200k.
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// GLM-5.3 succeeds GLM-5.2 with the same 1M context window.
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if (
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(model.provider === "zai" || model.provider === "zhipu-coding-plan") &&
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(model.id === "glm-5.2" || model.id === "glm-5.3")
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) {
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model.contextWindow = 1_000_000;
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model.maxTokens = 131_072;
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}
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// MiniMax-M3: 512K is the standard pricing tier boundary, not the
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// model ceiling. Pin every long-context provider that serves the model
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// (anthropic-messages `minimax`/`minimax-cn` and the openai-completions
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// MiniMax Coding/Token Plan endpoints `minimax-code`/`minimax-code-cn`)
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// to the documented 1M tier.
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if (
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model.id === "MiniMax-M3" &&
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(model.provider === "minimax" ||
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model.provider === "minimax-cn" ||
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model.provider === "minimax-code" ||
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model.provider === "minimax-code-cn")
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) {
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model.contextWindow = 1_000_000;
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}
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if (
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model.api === "openai-completions" &&
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|
(model.provider === "minimax-code" || model.provider === "minimax-code-cn")
|
|
) {
|
|
model.compat = {
|
|
...(model.compat ?? {}),
|
|
supportsStore: false,
|
|
supportsDeveloperRole: false,
|
|
supportsReasoningEffort: false,
|
|
reasoningContentField: "reasoning_content",
|
|
};
|
|
delete model.compat.thinkingFormat;
|
|
}
|
|
if (model.api === "openai-completions" && model.provider === "wafer-serverless" && model.reasoning) {
|
|
const thinkingFormat = resolveWaferServerlessThinkingFormat(model.id, undefined);
|
|
if (thinkingFormat === "zai") {
|
|
model.compat = {
|
|
...(model.compat ?? {}),
|
|
thinkingFormat,
|
|
reasoningContentField: "reasoning_content",
|
|
supportsDeveloperRole: false,
|
|
};
|
|
}
|
|
}
|
|
if (model.api === "openai-completions" && model.provider === "opencode-go" && isMimoModelIdOrName(model.id)) {
|
|
model.compat = {
|
|
...(model.compat ?? {}),
|
|
supportsToolChoice: false,
|
|
};
|
|
}
|
|
if (model.api === "openai-completions" && model.provider === "opencode-go" && model.id === "kimi-k2.7-code") {
|
|
model.compat = {
|
|
...(model.compat ?? {}),
|
|
supportsForcedToolChoice: false,
|
|
};
|
|
}
|
|
if (
|
|
(model.api === "openai-completions" || model.api === "openai-responses") &&
|
|
model.provider === "opencode-go" &&
|
|
(model.id === "deepseek-v4-flash" || model.id === "deepseek-v4-pro")
|
|
) {
|
|
model.compat = {
|
|
...(model.compat ?? {}),
|
|
supportsToolChoice: false,
|
|
maxTokensField: "max_tokens",
|
|
reasoningContentField: "reasoning_content",
|
|
requiresReasoningContentForToolCalls: true,
|
|
};
|
|
}
|
|
const parsedModel = parseKnownModel(model.id);
|
|
const applyPatchToolType = inferGeneratedApplyPatchToolType(model, parsedModel);
|
|
if (applyPatchToolType) {
|
|
model.applyPatchToolType = applyPatchToolType;
|
|
} else {
|
|
delete model.applyPatchToolType;
|
|
}
|
|
if (parsedModel.family === "anthropic") {
|
|
applyAnthropicCatalogPolicy(model, parsedModel);
|
|
}
|
|
if (parsedModel.family === "openai") {
|
|
applyOpenAICatalogPolicy(model, parsedModel);
|
|
}
|
|
}
|
|
|
|
function applyAnthropicCatalogPolicy(model: ModelSpec<Api>, parsedModel: AnthropicModel): void {
|
|
// Claude Opus 4.5: stencil.so reports 3x the correct cache pricing.
|
|
if (model.provider === "anthropic" && parsedModel.kind === "opus" && semverEqual(parsedModel.version, "4.5")) {
|
|
model.cost.cacheRead = 0.5;
|
|
model.cost.cacheWrite = 6.25;
|
|
}
|
|
|
|
// Bedrock Opus 4.6: upstream metadata is stale for cache pricing and context.
|
|
if (model.provider === "amazon-bedrock" && parsedModel.kind === "opus" && semverEqual(parsedModel.version, "4.6")) {
|
|
model.cost.cacheRead = 0.5;
|
|
model.cost.cacheWrite = 6.25;
|
|
model.contextWindow = 1000000;
|
|
model.maxTokens = 128000;
|
|
}
|
|
|
|
// Claude Fable/Mythos 5: Anthropic's /v1/models omits token limits and
|
|
// pricing, and stencil.so lags new releases. Pin authoritative values from
|
|
// the model card (1M context / 128k output) and pricing docs ($10 in / $50
|
|
// out per MTok).
|
|
if (model.provider === "anthropic" && isFableOrMythos(parsedModel.kind)) {
|
|
model.contextWindow = 1_000_000;
|
|
model.maxTokens = 128_000;
|
|
model.cost.input = 10;
|
|
model.cost.output = 50;
|
|
model.cost.cacheRead = 1;
|
|
model.cost.cacheWrite = 12.5;
|
|
}
|
|
}
|
|
|
|
function inferGeneratedApplyPatchToolType(
|
|
model: ModelSpec<Api>,
|
|
parsedModel: ParsedModel,
|
|
): ModelSpec<Api>["applyPatchToolType"] {
|
|
if (parsedModel.family !== "openai" || parsedModel.version.major !== 5) {
|
|
return undefined;
|
|
}
|
|
if (model.provider === "openai" && model.api === "openai-responses") {
|
|
return "freeform";
|
|
}
|
|
if (model.provider === "openai-codex" && model.api === "openai-codex-responses") {
|
|
return "freeform";
|
|
}
|
|
return undefined;
|
|
}
|
|
|
|
function applyOpenAICatalogPolicy(model: ModelSpec<Api>, parsedModel: OpenAIModel): void {
|
|
const isFirstPartyResponses = model.provider === "openai" && model.api === "openai-responses";
|
|
// Subscription Codex rates usage at the same >272K long-context tier as the
|
|
// API (openai/codex#32486), so first-party Codex SKUs carry the tier too —
|
|
// it drives both cost attribution and the extended-context window clamp.
|
|
const isFirstPartyCodex = model.provider === "openai-codex" && model.api === "openai-codex-responses";
|
|
if (isFirstPartyResponses && modelOrRequestIdValue(model, OPENAI_NONE_EFFORT_MODEL_IDS)) {
|
|
model.compat = { ...(model.compat ?? {}), reasoningDisableMode: "none-effort" };
|
|
}
|
|
const longContextCost =
|
|
isFirstPartyResponses || isFirstPartyCodex
|
|
? modelOrRequestIdValue(model, OPENAI_GPT_5_6_LONG_CONTEXT_COST_BY_MODEL_ID)
|
|
: undefined;
|
|
if (longContextCost) {
|
|
model.cost = { ...model.cost, longContext: longContextCost };
|
|
}
|
|
|
|
// Codex models: 400K figure includes output budget; input window is 272K.
|
|
if (parsedModel.variant.startsWith("codex") && parsedModel.variant !== "codex-spark") {
|
|
model.contextWindow = 272000;
|
|
return;
|
|
}
|
|
// GPT-5.4 mini/nano use plain OpenAI IDs on the Codex transport, but Codex still
|
|
// enforces the lower prompt budget for these variants. Codex discovery can also
|
|
// report inconsistent priorities for the GPT-5.4 family, so normalize by parsed
|
|
// variant instead of special-casing raw model ids.
|
|
if (model.api === "openai-codex-responses" && semverEqual(parsedModel.version, "5.4")) {
|
|
const normalizedPriority = CODEX_GPT_5_4_PRIORITY_BY_VARIANT[parsedModel.variant];
|
|
if (normalizedPriority !== undefined) {
|
|
model.priority = normalizedPriority;
|
|
}
|
|
if (parsedModel.variant === "mini" || parsedModel.variant === "nano") {
|
|
model.contextWindow = 272000;
|
|
}
|
|
}
|
|
// GPT-5.6 luna/sol/terra on the Codex transport: OpenAI enabled a 1M-token
|
|
// window for subscription Codex (2026-08-16), but the Codex model registry
|
|
// still reports the stale 272000 (openai/codex#38917), so floor the bundled
|
|
// window at 1,000,000. Daybreak aliases are excluded — the registry actively
|
|
// reports their true window.
|
|
if (model.api === "openai-codex-responses" && CODEX_GPT_5_6_1M_MODEL_IDS[model.id]) {
|
|
model.contextWindow = Math.max(model.contextWindow ?? 0, 1_000_000);
|
|
}
|
|
}
|