#!/usr/bin/env tsx import { writeFileSync } from "fs"; import { join, dirname } from "path"; import { fileURLToPath } from "url"; import { Api, KnownProvider, Model } from "../src/types.js"; const __filename = fileURLToPath(import.meta.url); const __dirname = dirname(__filename); const packageRoot = join(__dirname, ".."); interface ModelsDevModel { id: string; name: string; tool_call?: boolean; reasoning?: boolean; limit?: { context?: number; output?: number; }; cost?: { input?: number; output?: number; cache_read?: number; cache_write?: number; }; modalities?: { input?: string[]; }; provider?: { npm?: string; }; } interface AiGatewayModel { id: string; name?: string; context_window?: number; max_tokens?: number; tags?: string[]; pricing?: { input?: string | number; output?: string | number; input_cache_read?: string | number; input_cache_write?: string | number; }; } const COPILOT_STATIC_HEADERS = { "User-Agent": "GitHubCopilotChat/0.35.0", "Editor-Version": "vscode/1.107.0", "Editor-Plugin-Version": "copilot-chat/0.35.0", "Copilot-Integration-Id": "vscode-chat", } as const; const AI_GATEWAY_MODELS_URL = "https://ai-gateway.vercel.sh/v1"; const AI_GATEWAY_BASE_URL = "https://ai-gateway.vercel.sh"; async function fetchOpenRouterModels(): Promise[]> { try { console.log("Fetching models from OpenRouter API..."); const response = await fetch("https://openrouter.ai/api/v1/models"); const data = await response.json(); const models: Model[] = []; for (const model of data.data) { // Only include models that support tools if (!model.supported_parameters?.includes("tools")) continue; // Parse provider from model ID let provider: KnownProvider = "openrouter"; let modelKey = model.id; modelKey = model.id; // Keep full ID for OpenRouter // Parse input modalities const input: ("text" | "image")[] = ["text"]; if (model.architecture?.modality?.includes("image")) { input.push("image"); } // Convert pricing from $/token to $/million tokens const inputCost = parseFloat(model.pricing?.prompt || "0") * 1_000_000; const outputCost = parseFloat(model.pricing?.completion || "0") * 1_000_000; const cacheReadCost = parseFloat(model.pricing?.input_cache_read || "0") * 1_000_000; const cacheWriteCost = parseFloat(model.pricing?.input_cache_write || "0") * 1_000_000; const normalizedModel: Model = { id: modelKey, name: model.name, api: "openai-completions", baseUrl: "https://openrouter.ai/api/v1", provider, reasoning: model.supported_parameters?.includes("reasoning") || false, input, cost: { input: inputCost, output: outputCost, cacheRead: cacheReadCost, cacheWrite: cacheWriteCost, }, contextWindow: model.context_length || 4096, maxTokens: model.top_provider?.max_completion_tokens || 4096, }; models.push(normalizedModel); } console.log(`Fetched ${models.length} tool-capable models from OpenRouter`); return models; } catch (error) { console.error("Failed to fetch OpenRouter models:", error); return []; } } async function fetchAiGatewayModels(): Promise[]> { try { console.log("Fetching models from Vercel AI Gateway API..."); const response = await fetch(`${AI_GATEWAY_MODELS_URL}/models`); const data = await response.json(); const models: Model[] = []; const toNumber = (value: string | number | undefined): number => { if (typeof value === "number") { return Number.isFinite(value) ? value : 0; } const parsed = parseFloat(value ?? "0"); return Number.isFinite(parsed) ? parsed : 0; }; const items = Array.isArray(data.data) ? (data.data as AiGatewayModel[]) : []; for (const model of items) { const tags = Array.isArray(model.tags) ? model.tags : []; // Only include models that support tools if (!tags.includes("tool-use")) continue; const input: ("text" | "image")[] = ["text"]; if (tags.includes("vision")) { input.push("image"); } const inputCost = toNumber(model.pricing?.input) * 1_000_000; const outputCost = toNumber(model.pricing?.output) * 1_000_000; const cacheReadCost = toNumber(model.pricing?.input_cache_read) * 1_000_000; const cacheWriteCost = toNumber(model.pricing?.input_cache_write) * 1_000_000; models.push({ id: model.id, name: model.name || model.id, api: "anthropic-messages", baseUrl: AI_GATEWAY_BASE_URL, provider: "vercel-ai-gateway", reasoning: tags.includes("reasoning"), input, cost: { input: inputCost, output: outputCost, cacheRead: cacheReadCost, cacheWrite: cacheWriteCost, }, contextWindow: model.context_window || 4096, maxTokens: model.max_tokens || 4096, }); } console.log(`Fetched ${models.length} tool-capable models from Vercel AI Gateway`); return models; } catch (error) { console.error("Failed to fetch Vercel AI Gateway models:", error); return []; } } async function loadModelsDevData(): Promise[]> { try { console.log("Fetching models from models.dev API..."); const response = await fetch("https://models.dev/api.json"); const data = await response.json(); const models: Model[] = []; // Process Amazon Bedrock models if (data["amazon-bedrock"]?.models) { for (const [modelId, model] of Object.entries(data["amazon-bedrock"].models)) { const m = model as ModelsDevModel; if (m.tool_call !== true) continue; let id = modelId; if (id.startsWith("ai21.jamba")) { // These models doesn't support tool use in streaming mode continue; } if (id.startsWith("amazon.titan-text-express") || id.startsWith("mistral.mistral-7b-instruct-v0")) { // These models doesn't support system messages continue; } // Some Amazon Bedrock models require cross-region inference profiles to work. // To use cross-region inference, we need to add a region prefix to the models. // See https://docs.aws.amazon.com/bedrock/latest/userguide/inference-profiles-support.html#inference-profiles-support-system // TODO: Remove Claude models once https://github.com/anomalyco/models.dev/pull/607 is merged, and follow-up with other models. // Models with global cross-region inference profiles if (id.startsWith("anthropic.claude-haiku-4-5") || id.startsWith("anthropic.claude-sonnet-4") || id.startsWith("anthropic.claude-opus-4-5") || id.startsWith("amazon.nova-2-lite") || id.startsWith("cohere.embed-v4") || id.startsWith("twelvelabs.pegasus-1-2")) { id = "global." + id; } // Models with US cross-region inference profiles if (id.startsWith("amazon.nova-lite") || id.startsWith("amazon.nova-micro") || id.startsWith("amazon.nova-premier") || id.startsWith("amazon.nova-pro") || id.startsWith("anthropic.claude-3-7-sonnet") || id.startsWith("anthropic.claude-opus-4-1") || id.startsWith("anthropic.claude-opus-4-20250514") || id.startsWith("deepseek.r1") || id.startsWith("meta.llama3-2") || id.startsWith("meta.llama3-3") || id.startsWith("meta.llama4")) { id = "us." + id; } const bedrockModel = { id, name: m.name || id, api: "bedrock-converse-stream" as const, provider: "amazon-bedrock" as const, baseUrl: "https://bedrock-runtime.us-east-1.amazonaws.com", reasoning: m.reasoning === true, input: (m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"]) as ("text" | "image")[], cost: { input: m.cost?.input || 0, output: m.cost?.output || 0, cacheRead: m.cost?.cache_read || 0, cacheWrite: m.cost?.cache_write || 0, }, contextWindow: m.limit?.context || 4096, maxTokens: m.limit?.output || 4096, }; models.push(bedrockModel); // Add EU cross-region inference variants for Claude models if (modelId.startsWith("anthropic.claude-haiku-4-5") || modelId.startsWith("anthropic.claude-sonnet-4-5") || modelId.startsWith("anthropic.claude-opus-4-5")) { models.push({ ...bedrockModel, id: "eu." + modelId, name: (m.name || modelId) + " (EU)", }); } } } // Process Anthropic models if (data.anthropic?.models) { for (const [modelId, model] of Object.entries(data.anthropic.models)) { const m = model as ModelsDevModel; if (m.tool_call !== true) continue; models.push({ id: modelId, name: m.name || modelId, api: "anthropic-messages", provider: "anthropic", baseUrl: "https://api.anthropic.com", reasoning: m.reasoning === true, input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"], cost: { input: m.cost?.input || 0, output: m.cost?.output || 0, cacheRead: m.cost?.cache_read || 0, cacheWrite: m.cost?.cache_write || 0, }, contextWindow: m.limit?.context || 4096, maxTokens: m.limit?.output || 4096, }); } } // Process Google models if (data.google?.models) { for (const [modelId, model] of Object.entries(data.google.models)) { const m = model as ModelsDevModel; if (m.tool_call !== true) continue; models.push({ id: modelId, name: m.name || modelId, api: "google-generative-ai", provider: "google", baseUrl: "https://generativelanguage.googleapis.com/v1beta", reasoning: m.reasoning === true, input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"], cost: { input: m.cost?.input || 0, output: m.cost?.output || 0, cacheRead: m.cost?.cache_read || 0, cacheWrite: m.cost?.cache_write || 0, }, contextWindow: m.limit?.context || 4096, maxTokens: m.limit?.output || 4096, }); } } // Process OpenAI models if (data.openai?.models) { for (const [modelId, model] of Object.entries(data.openai.models)) { const m = model as ModelsDevModel; if (m.tool_call !== true) continue; models.push({ id: modelId, name: m.name || modelId, api: "openai-responses", provider: "openai", baseUrl: "https://api.openai.com/v1", reasoning: m.reasoning === true, input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"], cost: { input: m.cost?.input || 0, output: m.cost?.output || 0, cacheRead: m.cost?.cache_read || 0, cacheWrite: m.cost?.cache_write || 0, }, contextWindow: m.limit?.context || 4096, maxTokens: m.limit?.output || 4096, }); } } // Process Groq models if (data.groq?.models) { for (const [modelId, model] of Object.entries(data.groq.models)) { const m = model as ModelsDevModel; if (m.tool_call !== true) continue; models.push({ id: modelId, name: m.name || modelId, api: "openai-completions", provider: "groq", baseUrl: "https://api.groq.com/openai/v1", reasoning: m.reasoning === true, input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"], cost: { input: m.cost?.input || 0, output: m.cost?.output || 0, cacheRead: m.cost?.cache_read || 0, cacheWrite: m.cost?.cache_write || 0, }, contextWindow: m.limit?.context || 4096, maxTokens: m.limit?.output || 4096, }); } } // Process Cerebras models if (data.cerebras?.models) { for (const [modelId, model] of Object.entries(data.cerebras.models)) { const m = model as ModelsDevModel; if (m.tool_call !== true) continue; models.push({ id: modelId, name: m.name || modelId, api: "openai-completions", provider: "cerebras", baseUrl: "https://api.cerebras.ai/v1", reasoning: m.reasoning === true, input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"], cost: { input: m.cost?.input || 0, output: m.cost?.output || 0, cacheRead: m.cost?.cache_read || 0, cacheWrite: m.cost?.cache_write || 0, }, contextWindow: m.limit?.context || 4096, maxTokens: m.limit?.output || 4096, }); } } // Process xAi models if (data.xai?.models) { for (const [modelId, model] of Object.entries(data.xai.models)) { const m = model as ModelsDevModel; if (m.tool_call !== true) continue; models.push({ id: modelId, name: m.name || modelId, api: "openai-completions", provider: "xai", baseUrl: "https://api.x.ai/v1", reasoning: m.reasoning === true, input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"], cost: { input: m.cost?.input || 0, output: m.cost?.output || 0, cacheRead: m.cost?.cache_read || 0, cacheWrite: m.cost?.cache_write || 0, }, contextWindow: m.limit?.context || 4096, maxTokens: m.limit?.output || 4096, }); } } // Process zAi models if (data.zai?.models) { for (const [modelId, model] of Object.entries(data.zai.models)) { const m = model as ModelsDevModel; if (m.tool_call !== true) continue; const supportsImage = m.modalities?.input?.includes("image") models.push({ id: modelId, name: m.name || modelId, api: "openai-completions", provider: "zai", baseUrl: "https://api.z.ai/api/coding/paas/v4", reasoning: m.reasoning === true, input: supportsImage ? ["text", "image"] : ["text"], cost: { input: m.cost?.input || 0, output: m.cost?.output || 0, cacheRead: m.cost?.cache_read || 0, cacheWrite: m.cost?.cache_write || 0, }, compat: { supportsDeveloperRole: false, thinkingFormat: "zai", }, contextWindow: m.limit?.context || 4096, maxTokens: m.limit?.output || 4096, }); } } // Process Mistral models if (data.mistral?.models) { for (const [modelId, model] of Object.entries(data.mistral.models)) { const m = model as ModelsDevModel; if (m.tool_call !== true) continue; models.push({ id: modelId, name: m.name || modelId, api: "openai-completions", provider: "mistral", baseUrl: "https://api.mistral.ai/v1", reasoning: m.reasoning === true, input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"], cost: { input: m.cost?.input || 0, output: m.cost?.output || 0, cacheRead: m.cost?.cache_read || 0, cacheWrite: m.cost?.cache_write || 0, }, contextWindow: m.limit?.context || 4096, maxTokens: m.limit?.output || 4096, }); } } // Process OpenCode Zen models // API mapping based on provider.npm field: // - @ai-sdk/openai → openai-responses // - @ai-sdk/anthropic → anthropic-messages // - @ai-sdk/google → google-generative-ai // - null/undefined/@ai-sdk/openai-compatible → openai-completions if (data.opencode?.models) { for (const [modelId, model] of Object.entries(data.opencode.models)) { const m = model as ModelsDevModel & { status?: string }; if (m.tool_call !== true) continue; if (m.status === "deprecated") continue; const npm = m.provider?.npm; let api: Api; let baseUrl: string; if (npm === "@ai-sdk/openai") { api = "openai-responses"; baseUrl = "https://opencode.ai/zen/v1"; } else if (npm === "@ai-sdk/anthropic") { api = "anthropic-messages"; // Anthropic SDK appends /v1/messages to baseURL baseUrl = "https://opencode.ai/zen"; } else if (npm === "@ai-sdk/google") { api = "google-generative-ai"; baseUrl = "https://opencode.ai/zen/v1"; } else { // null, undefined, or @ai-sdk/openai-compatible api = "openai-completions"; baseUrl = "https://opencode.ai/zen/v1"; } models.push({ id: modelId, name: m.name || modelId, api, provider: "opencode", baseUrl, reasoning: m.reasoning === true, input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"], cost: { input: m.cost?.input || 0, output: m.cost?.output || 0, cacheRead: m.cost?.cache_read || 0, cacheWrite: m.cost?.cache_write || 0, }, contextWindow: m.limit?.context || 4096, maxTokens: m.limit?.output || 4096, }); } } // Process GitHub Copilot models if (data["github-copilot"]?.models) { for (const [modelId, model] of Object.entries(data["github-copilot"].models)) { const m = model as ModelsDevModel & { status?: string }; if (m.tool_call !== true) continue; if (m.status === "deprecated") continue; // gpt-5 models require responses API, others use completions const needsResponsesApi = modelId.startsWith("gpt-5") || modelId.startsWith("oswe"); const copilotModel: Model = { id: modelId, name: m.name || modelId, api: needsResponsesApi ? "openai-responses" : "openai-completions", provider: "github-copilot", baseUrl: "https://api.individual.githubcopilot.com", reasoning: m.reasoning === true, input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"], cost: { input: m.cost?.input || 0, output: m.cost?.output || 0, cacheRead: m.cost?.cache_read || 0, cacheWrite: m.cost?.cache_write || 0, }, contextWindow: m.limit?.context || 128000, maxTokens: m.limit?.output || 8192, headers: { ...COPILOT_STATIC_HEADERS }, // compat only applies to openai-completions ...(needsResponsesApi ? {} : { compat: { supportsStore: false, supportsDeveloperRole: false, supportsReasoningEffort: false, }, }), }; models.push(copilotModel); } } // Process MiniMax models const minimaxVariants = [ { key: "minimax", provider: "minimax", baseUrl: "https://api.minimax.io/anthropic" }, { key: "minimax-cn", provider: "minimax-cn", baseUrl: "https://api.minimaxi.com/anthropic" }, ] as const; for (const { key, provider, baseUrl } of minimaxVariants) { if (data[key]?.models) { for (const [modelId, model] of Object.entries(data[key].models)) { const m = model as ModelsDevModel; if (m.tool_call !== true) continue; models.push({ id: modelId, name: m.name || modelId, api: "anthropic-messages", provider, // MiniMax's Anthropic-compatible API - SDK appends /v1/messages baseUrl, reasoning: m.reasoning === true, input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"], cost: { input: m.cost?.input || 0, output: m.cost?.output || 0, cacheRead: m.cost?.cache_read || 0, cacheWrite: m.cost?.cache_write || 0, }, contextWindow: m.limit?.context || 4096, maxTokens: m.limit?.output || 4096, }); } } } console.log(`Loaded ${models.length} tool-capable models from models.dev`); return models; } catch (error) { console.error("Failed to load models.dev data:", error); return []; } } async function generateModels() { // Fetch models from both sources // models.dev: Anthropic, Google, OpenAI, Groq, Cerebras // OpenRouter: xAI and other providers (excluding Anthropic, Google, OpenAI) // AI Gateway: OpenAI-compatible catalog with tool-capable models const modelsDevModels = await loadModelsDevData(); const openRouterModels = await fetchOpenRouterModels(); const aiGatewayModels = await fetchAiGatewayModels(); // Combine models (models.dev has priority) const allModels = [...modelsDevModels, ...openRouterModels, ...aiGatewayModels]; // Fix incorrect cache pricing for Claude Opus 4.5 from models.dev // models.dev has 3x the correct pricing (1.5/18.75 instead of 0.5/6.25) const opus45 = allModels.find(m => m.provider === "anthropic" && m.id === "claude-opus-4-5"); if (opus45) { opus45.cost.cacheRead = 0.5; opus45.cost.cacheWrite = 6.25; } // Add missing gpt models if (!allModels.some(m => m.provider === "openai" && m.id === "gpt-5-chat-latest")) { allModels.push({ id: "gpt-5-chat-latest", name: "GPT-5 Chat Latest", api: "openai-responses", baseUrl: "https://api.openai.com/v1", provider: "openai", reasoning: false, input: ["text", "image"], cost: { input: 1.25, output: 10, cacheRead: 0.125, cacheWrite: 0, }, contextWindow: 128000, maxTokens: 16384, }); } if (!allModels.some(m => m.provider === "openai" && m.id === "gpt-5.1-codex")) { allModels.push({ id: "gpt-5.1-codex", name: "GPT-5.1 Codex", api: "openai-responses", baseUrl: "https://api.openai.com/v1", provider: "openai", reasoning: true, input: ["text", "image"], cost: { input: 1.25, output: 5, cacheRead: 0.125, cacheWrite: 1.25, }, contextWindow: 400000, maxTokens: 128000, }); } if (!allModels.some(m => m.provider === "openai" && m.id === "gpt-5.1-codex-max")) { allModels.push({ id: "gpt-5.1-codex-max", name: "GPT-5.1 Codex Max", api: "openai-responses", baseUrl: "https://api.openai.com/v1", provider: "openai", reasoning: true, input: ["text", "image"], cost: { input: 1.25, output: 10, cacheRead: 0.125, cacheWrite: 0, }, contextWindow: 400000, maxTokens: 128000, }); } // OpenAI Codex (ChatGPT OAuth) models // NOTE: These are not fetched from models.dev; we keep a small, explicit list to avoid aliases. // Context window is based on observed server limits (400s above ~272k), not marketing numbers. const CODEX_BASE_URL = "https://chatgpt.com/backend-api"; const CODEX_CONTEXT = 272000; const CODEX_MAX_TOKENS = 128000; const codexModels: Model<"openai-codex-responses">[] = [ { id: "gpt-5.1", name: "GPT-5.1", api: "openai-codex-responses", provider: "openai-codex", baseUrl: CODEX_BASE_URL, reasoning: true, input: ["text", "image"], cost: { input: 1.25, output: 10, cacheRead: 0.125, cacheWrite: 0 }, contextWindow: CODEX_CONTEXT, maxTokens: CODEX_MAX_TOKENS, }, { id: "gpt-5.1-codex-max", name: "GPT-5.1 Codex Max", api: "openai-codex-responses", provider: "openai-codex", baseUrl: CODEX_BASE_URL, reasoning: true, input: ["text", "image"], cost: { input: 1.25, output: 10, cacheRead: 0.125, cacheWrite: 0 }, contextWindow: CODEX_CONTEXT, maxTokens: CODEX_MAX_TOKENS, }, { id: "gpt-5.1-codex-mini", name: "GPT-5.1 Codex Mini", api: "openai-codex-responses", provider: "openai-codex", baseUrl: CODEX_BASE_URL, reasoning: true, input: ["text", "image"], cost: { input: 0.25, output: 2, cacheRead: 0.025, cacheWrite: 0 }, contextWindow: CODEX_CONTEXT, maxTokens: CODEX_MAX_TOKENS, }, { id: "gpt-5.2", name: "GPT-5.2", api: "openai-codex-responses", provider: "openai-codex", baseUrl: CODEX_BASE_URL, reasoning: true, input: ["text", "image"], cost: { input: 1.75, output: 14, cacheRead: 0.175, cacheWrite: 0 }, contextWindow: CODEX_CONTEXT, maxTokens: CODEX_MAX_TOKENS, }, { id: "gpt-5.2-codex", name: "GPT-5.2 Codex", api: "openai-codex-responses", provider: "openai-codex", baseUrl: CODEX_BASE_URL, reasoning: true, input: ["text", "image"], cost: { input: 1.75, output: 14, cacheRead: 0.175, cacheWrite: 0 }, contextWindow: CODEX_CONTEXT, maxTokens: CODEX_MAX_TOKENS, }, ]; allModels.push(...codexModels); // Add missing Grok models if (!allModels.some(m => m.provider === "xai" && m.id === "grok-code-fast-1")) { allModels.push({ id: "grok-code-fast-1", name: "Grok Code Fast 1", api: "openai-completions", baseUrl: "https://api.x.ai/v1", provider: "xai", reasoning: false, input: ["text"], cost: { input: 0.2, output: 1.5, cacheRead: 0.02, cacheWrite: 0, }, contextWindow: 32768, maxTokens: 8192, }); } // Add missing OpenRouter model if (!allModels.some(m => m.provider === "openrouter" && m.id === "openrouter/auto")) { allModels.push({ id: "openrouter/auto", name: "OpenRouter: Auto Router", api: "openai-completions", provider: "openrouter", baseUrl: "https://openrouter.ai/api/v1", reasoning: true, input: ["text", "image"], cost: { // we dont know about the costs because OpenRouter auto routes to different models // and then charges you for the underlying used model input:0, output:0, cacheRead:0, cacheWrite:0, }, contextWindow: 2000000, maxTokens: 30000, }); } // Google Cloud Code Assist models (Gemini CLI) // Uses production endpoint, standard Gemini models only const CLOUD_CODE_ASSIST_ENDPOINT = "https://cloudcode-pa.googleapis.com"; const cloudCodeAssistModels: Model<"google-gemini-cli">[] = [ { id: "gemini-2.5-pro", name: "Gemini 2.5 Pro (Cloud Code Assist)", api: "google-gemini-cli", provider: "google-gemini-cli", baseUrl: CLOUD_CODE_ASSIST_ENDPOINT, reasoning: true, input: ["text", "image"], cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 }, contextWindow: 1048576, maxTokens: 65535, }, { id: "gemini-2.5-flash", name: "Gemini 2.5 Flash (Cloud Code Assist)", api: "google-gemini-cli", provider: "google-gemini-cli", baseUrl: CLOUD_CODE_ASSIST_ENDPOINT, reasoning: true, input: ["text", "image"], cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 }, contextWindow: 1048576, maxTokens: 65535, }, { id: "gemini-2.0-flash", name: "Gemini 2.0 Flash (Cloud Code Assist)", api: "google-gemini-cli", provider: "google-gemini-cli", baseUrl: CLOUD_CODE_ASSIST_ENDPOINT, reasoning: false, input: ["text", "image"], cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 }, contextWindow: 1048576, maxTokens: 8192, }, { id: "gemini-3-pro-preview", name: "Gemini 3 Pro Preview (Cloud Code Assist)", api: "google-gemini-cli", provider: "google-gemini-cli", baseUrl: CLOUD_CODE_ASSIST_ENDPOINT, reasoning: true, input: ["text", "image"], cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 }, contextWindow: 1048576, maxTokens: 65535, }, { id: "gemini-3-flash-preview", name: "Gemini 3 Flash Preview (Cloud Code Assist)", api: "google-gemini-cli", provider: "google-gemini-cli", baseUrl: CLOUD_CODE_ASSIST_ENDPOINT, reasoning: true, input: ["text", "image"], cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 }, contextWindow: 1048576, maxTokens: 65535, }, ]; allModels.push(...cloudCodeAssistModels); // Antigravity models (Gemini 3, Claude, GPT-OSS via Google Cloud) // Uses sandbox endpoint and different OAuth credentials for access to additional models const ANTIGRAVITY_ENDPOINT = "https://daily-cloudcode-pa.sandbox.googleapis.com"; const antigravityModels: Model<"google-gemini-cli">[] = [ { id: "gemini-3-pro-high", name: "Gemini 3 Pro High (Antigravity)", api: "google-gemini-cli", provider: "google-antigravity", baseUrl: ANTIGRAVITY_ENDPOINT, reasoning: true, input: ["text", "image"], // the Model type doesn't seem to support having extended-context costs, so I'm just using the pricing for <200k input cost: { input: 2, output: 12, cacheRead: 0.2, cacheWrite: 2.375 }, contextWindow: 1048576, maxTokens: 65535, }, { id: "gemini-3-pro-low", name: "Gemini 3 Pro Low (Antigravity)", api: "google-gemini-cli", provider: "google-antigravity", baseUrl: ANTIGRAVITY_ENDPOINT, reasoning: true, input: ["text", "image"], // the Model type doesn't seem to support having extended-context costs, so I'm just using the pricing for <200k input cost: { input: 2, output: 12, cacheRead: 0.2, cacheWrite: 2.375 }, contextWindow: 1048576, maxTokens: 65535, }, { id: "gemini-3-flash", name: "Gemini 3 Flash (Antigravity)", api: "google-gemini-cli", provider: "google-antigravity", baseUrl: ANTIGRAVITY_ENDPOINT, reasoning: true, input: ["text", "image"], cost: { input: 0.5, output: 3, cacheRead: 0.5, cacheWrite: 0 }, contextWindow: 1048576, maxTokens: 65535, }, { id: "claude-sonnet-4-5", name: "Claude Sonnet 4.5 (Antigravity)", api: "google-gemini-cli", provider: "google-antigravity", baseUrl: ANTIGRAVITY_ENDPOINT, reasoning: false, input: ["text", "image"], cost: { input: 3, output: 15, cacheRead: 0.3, cacheWrite: 3.75 }, contextWindow: 200000, maxTokens: 64000, }, { id: "claude-sonnet-4-5-thinking", name: "Claude Sonnet 4.5 Thinking (Antigravity)", api: "google-gemini-cli", provider: "google-antigravity", baseUrl: ANTIGRAVITY_ENDPOINT, reasoning: true, input: ["text", "image"], cost: { input: 3, output: 15, cacheRead: 0.3, cacheWrite: 3.75 }, contextWindow: 200000, maxTokens: 64000, }, { id: "claude-opus-4-5-thinking", name: "Claude Opus 4.5 Thinking (Antigravity)", api: "google-gemini-cli", provider: "google-antigravity", baseUrl: ANTIGRAVITY_ENDPOINT, reasoning: true, input: ["text", "image"], cost: { input: 5, output: 25, cacheRead: 0.5, cacheWrite: 6.25 }, contextWindow: 200000, maxTokens: 64000, }, { id: "gpt-oss-120b-medium", name: "GPT-OSS 120B Medium (Antigravity)", api: "google-gemini-cli", provider: "google-antigravity", baseUrl: ANTIGRAVITY_ENDPOINT, reasoning: false, input: ["text"], cost: { input: 0.09, output: 0.36, cacheRead: 0, cacheWrite: 0 }, contextWindow: 131072, maxTokens: 32768, }, ]; allModels.push(...antigravityModels); const VERTEX_BASE_URL = "https://{location}-aiplatform.googleapis.com"; const vertexModels: Model<"google-vertex">[] = [ { id: "gemini-3-pro-preview", name: "Gemini 3 Pro Preview (Vertex)", api: "google-vertex", provider: "google-vertex", baseUrl: VERTEX_BASE_URL, reasoning: true, input: ["text", "image"], cost: { input: 2, output: 12, cacheRead: 0.2, cacheWrite: 0 }, contextWindow: 1000000, maxTokens: 64000, }, { id: "gemini-3-flash-preview", name: "Gemini 3 Flash Preview (Vertex)", api: "google-vertex", provider: "google-vertex", baseUrl: VERTEX_BASE_URL, reasoning: true, input: ["text", "image"], cost: { input: 0.5, output: 3, cacheRead: 0.05, cacheWrite: 0 }, contextWindow: 1048576, maxTokens: 65536, }, { id: "gemini-2.0-flash", name: "Gemini 2.0 Flash (Vertex)", api: "google-vertex", provider: "google-vertex", baseUrl: VERTEX_BASE_URL, reasoning: false, input: ["text", "image"], cost: { input: 0.15, output: 0.6, cacheRead: 0.0375, cacheWrite: 0 }, contextWindow: 1048576, maxTokens: 8192, }, { id: "gemini-2.0-flash-lite", name: "Gemini 2.0 Flash Lite (Vertex)", api: "google-vertex", provider: "google-vertex", baseUrl: VERTEX_BASE_URL, reasoning: true, input: ["text", "image"], cost: { input: 0.075, output: 0.3, cacheRead: 0.01875, cacheWrite: 0 }, contextWindow: 1048576, maxTokens: 65536, }, { id: "gemini-2.5-pro", name: "Gemini 2.5 Pro (Vertex)", api: "google-vertex", provider: "google-vertex", baseUrl: VERTEX_BASE_URL, reasoning: true, input: ["text", "image"], cost: { input: 1.25, output: 10, cacheRead: 0.125, cacheWrite: 0 }, contextWindow: 1048576, maxTokens: 65536, }, { id: "gemini-2.5-flash", name: "Gemini 2.5 Flash (Vertex)", api: "google-vertex", provider: "google-vertex", baseUrl: VERTEX_BASE_URL, reasoning: true, input: ["text", "image"], cost: { input: 0.3, output: 2.5, cacheRead: 0.03, cacheWrite: 0 }, contextWindow: 1048576, maxTokens: 65536, }, { id: "gemini-2.5-flash-lite-preview-09-2025", name: "Gemini 2.5 Flash Lite Preview 09-25 (Vertex)", api: "google-vertex", provider: "google-vertex", baseUrl: VERTEX_BASE_URL, reasoning: true, input: ["text", "image"], cost: { input: 0.1, output: 0.4, cacheRead: 0.01, cacheWrite: 0 }, contextWindow: 1048576, maxTokens: 65536, }, { id: "gemini-2.5-flash-lite", name: "Gemini 2.5 Flash Lite (Vertex)", api: "google-vertex", provider: "google-vertex", baseUrl: VERTEX_BASE_URL, reasoning: true, input: ["text", "image"], cost: { input: 0.1, output: 0.4, cacheRead: 0.01, cacheWrite: 0 }, contextWindow: 1048576, maxTokens: 65536, }, { id: "gemini-1.5-pro", name: "Gemini 1.5 Pro (Vertex)", api: "google-vertex", provider: "google-vertex", baseUrl: VERTEX_BASE_URL, reasoning: false, input: ["text", "image"], cost: { input: 1.25, output: 5, cacheRead: 0.3125, cacheWrite: 0 }, contextWindow: 1000000, maxTokens: 8192, }, { id: "gemini-1.5-flash", name: "Gemini 1.5 Flash (Vertex)", api: "google-vertex", provider: "google-vertex", baseUrl: VERTEX_BASE_URL, reasoning: false, input: ["text", "image"], cost: { input: 0.075, output: 0.3, cacheRead: 0.01875, cacheWrite: 0 }, contextWindow: 1000000, maxTokens: 8192, }, { id: "gemini-1.5-flash-8b", name: "Gemini 1.5 Flash-8B (Vertex)", api: "google-vertex", provider: "google-vertex", baseUrl: VERTEX_BASE_URL, reasoning: false, input: ["text", "image"], cost: { input: 0.0375, output: 0.15, cacheRead: 0.01, cacheWrite: 0 }, contextWindow: 1000000, maxTokens: 8192, }, ]; allModels.push(...vertexModels); const azureOpenAiModels: Model[] = allModels .filter((model) => model.provider === "openai" && model.api === "openai-responses") .map((model) => ({ ...model, api: "azure-openai-responses", provider: "azure-openai-responses", baseUrl: "", })); allModels.push(...azureOpenAiModels); // Group by provider and deduplicate by model ID const providers: Record>> = {}; for (const model of allModels) { if (!providers[model.provider]) { providers[model.provider] = {}; } // Use model ID as key to automatically deduplicate // Only add if not already present (models.dev takes priority over OpenRouter) if (!providers[model.provider][model.id]) { providers[model.provider][model.id] = model; } } // Generate TypeScript file let output = `// This file is auto-generated by scripts/generate-models.ts // Do not edit manually - run 'npm run generate-models' to update import type { Model } from "./types.js"; export const MODELS = { `; // Generate provider sections (sorted for deterministic output) const sortedProviderIds = Object.keys(providers).sort(); for (const providerId of sortedProviderIds) { const models = providers[providerId]; output += `\t${JSON.stringify(providerId)}: {\n`; const sortedModelIds = Object.keys(models).sort(); for (const modelId of sortedModelIds) { const model = models[modelId]; output += `\t\t"${model.id}": {\n`; output += `\t\t\tid: "${model.id}",\n`; output += `\t\t\tname: "${model.name}",\n`; output += `\t\t\tapi: "${model.api}",\n`; output += `\t\t\tprovider: "${model.provider}",\n`; if (model.baseUrl !== undefined) { output += `\t\t\tbaseUrl: "${model.baseUrl}",\n`; } if (model.headers) { output += `\t\t\theaders: ${JSON.stringify(model.headers)},\n`; } if (model.compat) { output += ` compat: ${JSON.stringify(model.compat)}, `; } output += `\t\t\treasoning: ${model.reasoning},\n`; output += `\t\t\tinput: [${model.input.map(i => `"${i}"`).join(", ")}],\n`; output += `\t\t\tcost: {\n`; output += `\t\t\t\tinput: ${model.cost.input},\n`; output += `\t\t\t\toutput: ${model.cost.output},\n`; output += `\t\t\t\tcacheRead: ${model.cost.cacheRead},\n`; output += `\t\t\t\tcacheWrite: ${model.cost.cacheWrite},\n`; output += `\t\t\t},\n`; output += `\t\t\tcontextWindow: ${model.contextWindow},\n`; output += `\t\t\tmaxTokens: ${model.maxTokens},\n`; output += `\t\t} satisfies Model<"${model.api}">,\n`; } output += `\t},\n`; } output += `} as const; `; // Write file writeFileSync(join(packageRoot, "src/models.generated.ts"), output); console.log("Generated src/models.generated.ts"); // Print statistics const totalModels = allModels.length; const reasoningModels = allModels.filter(m => m.reasoning).length; console.log(`\nModel Statistics:`); console.log(` Total tool-capable models: ${totalModels}`); console.log(` Reasoning-capable models: ${reasoningModels}`); for (const [provider, models] of Object.entries(providers)) { console.log(` ${provider}: ${Object.keys(models).length} models`); } } // Run the generator generateModels().catch(console.error);

Summary

Summary