mirror of
https://github.com/getcompanion-ai/co-mono.git
synced 2026-04-15 14:03:49 +00:00
730 lines
21 KiB
TypeScript
730 lines
21 KiB
TypeScript
#!/usr/bin/env tsx
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import { writeFileSync } from "fs";
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import { join, dirname } from "path";
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import { fileURLToPath } from "url";
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import { Api, KnownProvider, Model } from "../src/types.js";
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const __filename = fileURLToPath(import.meta.url);
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const __dirname = dirname(__filename);
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const packageRoot = join(__dirname, "..");
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interface ModelsDevModel {
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id: string;
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name: string;
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tool_call?: boolean;
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reasoning?: boolean;
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limit?: {
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context?: number;
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output?: number;
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};
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cost?: {
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input?: number;
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output?: number;
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cache_read?: number;
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cache_write?: number;
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};
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modalities?: {
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input?: string[];
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};
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}
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const COPILOT_STATIC_HEADERS = {
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"User-Agent": "GitHubCopilotChat/0.35.0",
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"Editor-Version": "vscode/1.107.0",
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"Editor-Plugin-Version": "copilot-chat/0.35.0",
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"Copilot-Integration-Id": "vscode-chat",
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} as const;
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async function fetchOpenRouterModels(): Promise<Model<any>[]> {
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try {
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console.log("Fetching models from OpenRouter API...");
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const response = await fetch("https://openrouter.ai/api/v1/models");
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const data = await response.json();
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const models: Model<any>[] = [];
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for (const model of data.data) {
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// Only include models that support tools
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if (!model.supported_parameters?.includes("tools")) continue;
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// Parse provider from model ID
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let provider: KnownProvider = "openrouter";
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let modelKey = model.id;
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modelKey = model.id; // Keep full ID for OpenRouter
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// Parse input modalities
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const input: ("text" | "image")[] = ["text"];
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if (model.architecture?.modality?.includes("image")) {
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input.push("image");
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}
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// Convert pricing from $/token to $/million tokens
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const inputCost = parseFloat(model.pricing?.prompt || "0") * 1_000_000;
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const outputCost = parseFloat(model.pricing?.completion || "0") * 1_000_000;
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const cacheReadCost = parseFloat(model.pricing?.input_cache_read || "0") * 1_000_000;
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const cacheWriteCost = parseFloat(model.pricing?.input_cache_write || "0") * 1_000_000;
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const normalizedModel: Model<any> = {
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id: modelKey,
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name: model.name,
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api: "openai-completions",
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baseUrl: "https://openrouter.ai/api/v1",
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provider,
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reasoning: model.supported_parameters?.includes("reasoning") || false,
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input,
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cost: {
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input: inputCost,
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output: outputCost,
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cacheRead: cacheReadCost,
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cacheWrite: cacheWriteCost,
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},
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contextWindow: model.context_length || 4096,
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maxTokens: model.top_provider?.max_completion_tokens || 4096,
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};
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models.push(normalizedModel);
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}
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console.log(`Fetched ${models.length} tool-capable models from OpenRouter`);
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return models;
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} catch (error) {
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console.error("Failed to fetch OpenRouter models:", error);
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return [];
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}
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}
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async function loadModelsDevData(): Promise<Model<any>[]> {
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try {
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console.log("Fetching models from models.dev API...");
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const response = await fetch("https://models.dev/api.json");
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const data = await response.json();
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const models: Model<any>[] = [];
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// Process Anthropic models
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if (data.anthropic?.models) {
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for (const [modelId, model] of Object.entries(data.anthropic.models)) {
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const m = model as ModelsDevModel;
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if (m.tool_call !== true) continue;
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models.push({
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id: modelId,
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name: m.name || modelId,
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api: "anthropic-messages",
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provider: "anthropic",
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baseUrl: "https://api.anthropic.com",
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reasoning: m.reasoning === true,
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input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"],
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cost: {
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input: m.cost?.input || 0,
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output: m.cost?.output || 0,
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cacheRead: m.cost?.cache_read || 0,
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cacheWrite: m.cost?.cache_write || 0,
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},
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contextWindow: m.limit?.context || 4096,
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maxTokens: m.limit?.output || 4096,
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});
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}
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}
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// Process Google models
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if (data.google?.models) {
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for (const [modelId, model] of Object.entries(data.google.models)) {
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const m = model as ModelsDevModel;
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if (m.tool_call !== true) continue;
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models.push({
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id: modelId,
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name: m.name || modelId,
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api: "google-generative-ai",
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provider: "google",
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baseUrl: "https://generativelanguage.googleapis.com/v1beta",
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reasoning: m.reasoning === true,
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input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"],
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cost: {
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input: m.cost?.input || 0,
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output: m.cost?.output || 0,
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cacheRead: m.cost?.cache_read || 0,
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cacheWrite: m.cost?.cache_write || 0,
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},
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contextWindow: m.limit?.context || 4096,
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maxTokens: m.limit?.output || 4096,
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});
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}
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}
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// Process OpenAI models
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if (data.openai?.models) {
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for (const [modelId, model] of Object.entries(data.openai.models)) {
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const m = model as ModelsDevModel;
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if (m.tool_call !== true) continue;
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models.push({
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id: modelId,
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name: m.name || modelId,
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api: "openai-responses",
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provider: "openai",
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baseUrl: "https://api.openai.com/v1",
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reasoning: m.reasoning === true,
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input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"],
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cost: {
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input: m.cost?.input || 0,
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output: m.cost?.output || 0,
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cacheRead: m.cost?.cache_read || 0,
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cacheWrite: m.cost?.cache_write || 0,
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},
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contextWindow: m.limit?.context || 4096,
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maxTokens: m.limit?.output || 4096,
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});
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}
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}
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// Process Groq models
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if (data.groq?.models) {
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for (const [modelId, model] of Object.entries(data.groq.models)) {
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const m = model as ModelsDevModel;
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if (m.tool_call !== true) continue;
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models.push({
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id: modelId,
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name: m.name || modelId,
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api: "openai-completions",
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provider: "groq",
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baseUrl: "https://api.groq.com/openai/v1",
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reasoning: m.reasoning === true,
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input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"],
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cost: {
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input: m.cost?.input || 0,
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output: m.cost?.output || 0,
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cacheRead: m.cost?.cache_read || 0,
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cacheWrite: m.cost?.cache_write || 0,
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},
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contextWindow: m.limit?.context || 4096,
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maxTokens: m.limit?.output || 4096,
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});
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}
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}
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// Process Cerebras models
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if (data.cerebras?.models) {
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for (const [modelId, model] of Object.entries(data.cerebras.models)) {
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const m = model as ModelsDevModel;
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if (m.tool_call !== true) continue;
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models.push({
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id: modelId,
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name: m.name || modelId,
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api: "openai-completions",
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provider: "cerebras",
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baseUrl: "https://api.cerebras.ai/v1",
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reasoning: m.reasoning === true,
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input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"],
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cost: {
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input: m.cost?.input || 0,
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output: m.cost?.output || 0,
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cacheRead: m.cost?.cache_read || 0,
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cacheWrite: m.cost?.cache_write || 0,
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},
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contextWindow: m.limit?.context || 4096,
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maxTokens: m.limit?.output || 4096,
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});
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}
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}
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// Process xAi models
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if (data.xai?.models) {
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for (const [modelId, model] of Object.entries(data.xai.models)) {
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const m = model as ModelsDevModel;
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if (m.tool_call !== true) continue;
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models.push({
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id: modelId,
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name: m.name || modelId,
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api: "openai-completions",
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provider: "xai",
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baseUrl: "https://api.x.ai/v1",
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reasoning: m.reasoning === true,
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input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"],
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cost: {
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input: m.cost?.input || 0,
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output: m.cost?.output || 0,
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cacheRead: m.cost?.cache_read || 0,
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cacheWrite: m.cost?.cache_write || 0,
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},
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contextWindow: m.limit?.context || 4096,
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maxTokens: m.limit?.output || 4096,
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});
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}
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}
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// Process zAi models
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if (data.zai?.models) {
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for (const [modelId, model] of Object.entries(data.zai.models)) {
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const m = model as ModelsDevModel;
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if (m.tool_call !== true) continue;
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const supportsImage = m.modalities?.input?.includes("image")
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models.push({
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id: modelId,
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name: m.name || modelId,
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api: "openai-completions",
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provider: "zai",
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baseUrl: "https://api.z.ai/api/coding/paas/v4",
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reasoning: m.reasoning === true,
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input: supportsImage ? ["text", "image"] : ["text"],
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cost: {
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input: m.cost?.input || 0,
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output: m.cost?.output || 0,
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cacheRead: m.cost?.cache_read || 0,
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cacheWrite: m.cost?.cache_write || 0,
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},
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compat: {
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supportsDeveloperRole: false,
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},
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contextWindow: m.limit?.context || 4096,
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maxTokens: m.limit?.output || 4096,
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});
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}
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}
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// Process Mistral models
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if (data.mistral?.models) {
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for (const [modelId, model] of Object.entries(data.mistral.models)) {
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const m = model as ModelsDevModel;
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if (m.tool_call !== true) continue;
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models.push({
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id: modelId,
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name: m.name || modelId,
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api: "openai-completions",
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provider: "mistral",
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baseUrl: "https://api.mistral.ai/v1",
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reasoning: m.reasoning === true,
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input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"],
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cost: {
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input: m.cost?.input || 0,
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output: m.cost?.output || 0,
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cacheRead: m.cost?.cache_read || 0,
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cacheWrite: m.cost?.cache_write || 0,
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},
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contextWindow: m.limit?.context || 4096,
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maxTokens: m.limit?.output || 4096,
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});
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}
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}
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// Process GitHub Copilot models
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if (data["github-copilot"]?.models) {
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for (const [modelId, model] of Object.entries(data["github-copilot"].models)) {
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const m = model as ModelsDevModel & { status?: string };
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if (m.tool_call !== true) continue;
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if (m.status === "deprecated") continue;
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// gpt-5 models require responses API, others use completions
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const needsResponsesApi = modelId.startsWith("gpt-5") || modelId.startsWith("oswe");
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const copilotModel: Model<any> = {
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id: modelId,
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name: m.name || modelId,
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api: needsResponsesApi ? "openai-responses" : "openai-completions",
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provider: "github-copilot",
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baseUrl: "https://api.individual.githubcopilot.com",
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reasoning: m.reasoning === true,
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input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"],
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cost: {
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input: m.cost?.input || 0,
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output: m.cost?.output || 0,
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cacheRead: m.cost?.cache_read || 0,
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cacheWrite: m.cost?.cache_write || 0,
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},
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contextWindow: m.limit?.context || 128000,
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maxTokens: m.limit?.output || 8192,
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headers: { ...COPILOT_STATIC_HEADERS },
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// compat only applies to openai-completions
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...(needsResponsesApi ? {} : {
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compat: {
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supportsStore: false,
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supportsDeveloperRole: false,
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supportsReasoningEffort: false,
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},
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}),
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};
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models.push(copilotModel);
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}
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}
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console.log(`Loaded ${models.length} tool-capable models from models.dev`);
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return models;
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} catch (error) {
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console.error("Failed to load models.dev data:", error);
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return [];
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}
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}
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async function generateModels() {
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// Fetch models from both sources
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// models.dev: Anthropic, Google, OpenAI, Groq, Cerebras
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// OpenRouter: xAI and other providers (excluding Anthropic, Google, OpenAI)
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const modelsDevModels = await loadModelsDevData();
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const openRouterModels = await fetchOpenRouterModels();
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// Combine models (models.dev has priority)
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const allModels = [...modelsDevModels, ...openRouterModels];
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// Fix incorrect cache pricing for Claude Opus 4.5 from models.dev
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// models.dev has 3x the correct pricing (1.5/18.75 instead of 0.5/6.25)
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const opus45 = allModels.find(m => m.provider === "anthropic" && m.id === "claude-opus-4-5");
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if (opus45) {
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opus45.cost.cacheRead = 0.5;
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opus45.cost.cacheWrite = 6.25;
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}
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// Add missing gpt models
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if (!allModels.some(m => m.provider === "openai" && m.id === "gpt-5-chat-latest")) {
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allModels.push({
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id: "gpt-5-chat-latest",
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name: "GPT-5 Chat Latest",
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api: "openai-responses",
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baseUrl: "https://api.openai.com/v1",
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provider: "openai",
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reasoning: false,
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input: ["text", "image"],
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cost: {
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input: 1.25,
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output: 10,
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cacheRead: 0.125,
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cacheWrite: 0,
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},
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contextWindow: 128000,
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maxTokens: 16384,
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});
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}
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if (!allModels.some(m => m.provider === "openai" && m.id === "gpt-5.1-codex")) {
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allModels.push({
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id: "gpt-5.1-codex",
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name: "GPT-5.1 Codex",
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api: "openai-responses",
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baseUrl: "https://api.openai.com/v1",
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provider: "openai",
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reasoning: true,
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input: ["text", "image"],
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cost: {
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input: 1.25,
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output: 5,
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cacheRead: 0.125,
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cacheWrite: 1.25,
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},
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contextWindow: 400000,
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maxTokens: 128000,
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});
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}
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if (!allModels.some(m => m.provider === "openai" && m.id === "gpt-5.1-codex-max")) {
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allModels.push({
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id: "gpt-5.1-codex-max",
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name: "GPT-5.1 Codex Max",
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api: "openai-responses",
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baseUrl: "https://api.openai.com/v1",
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provider: "openai",
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reasoning: true,
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input: ["text", "image"],
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cost: {
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input: 1.25,
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output: 10,
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cacheRead: 0.125,
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cacheWrite: 0,
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},
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contextWindow: 400000,
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maxTokens: 128000,
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});
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}
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// Add missing Grok models
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if (!allModels.some(m => m.provider === "xai" && m.id === "grok-code-fast-1")) {
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allModels.push({
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id: "grok-code-fast-1",
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name: "Grok Code Fast 1",
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api: "openai-completions",
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baseUrl: "https://api.x.ai/v1",
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provider: "xai",
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reasoning: false,
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input: ["text"],
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cost: {
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input: 0.2,
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output: 1.5,
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cacheRead: 0.02,
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cacheWrite: 0,
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},
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contextWindow: 32768,
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maxTokens: 8192,
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});
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}
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// Add missing OpenRouter model
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if (!allModels.some(m => m.provider === "openrouter" && m.id === "openrouter/auto")) {
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allModels.push({
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id: "openrouter/auto",
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name: "OpenRouter: Auto Router",
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api: "openai-completions",
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provider: "openrouter",
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baseUrl: "https://openrouter.ai/api/v1",
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reasoning: true,
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input: ["text", "image"],
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cost: {
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// we dont know about the costs because OpenRouter auto routes to different models
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// and then charges you for the underlying used model
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input:0,
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output:0,
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cacheRead:0,
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cacheWrite:0,
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},
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contextWindow: 2000000,
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maxTokens: 30000,
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});
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}
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// Google Cloud Code Assist models (Gemini CLI)
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// Uses production endpoint, standard Gemini models only
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const CLOUD_CODE_ASSIST_ENDPOINT = "https://cloudcode-pa.googleapis.com";
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const cloudCodeAssistModels: Model<"google-gemini-cli">[] = [
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{
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id: "gemini-2.5-pro",
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name: "Gemini 2.5 Pro (Cloud Code Assist)",
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api: "google-gemini-cli",
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provider: "google-gemini-cli",
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baseUrl: CLOUD_CODE_ASSIST_ENDPOINT,
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reasoning: true,
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input: ["text", "image"],
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cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
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contextWindow: 1048576,
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maxTokens: 65535,
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},
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{
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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"],
|
|
cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
|
|
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"],
|
|
cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
|
|
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, output: 0, cacheRead: 0, 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: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
|
|
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: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
|
|
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: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
|
|
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, output: 0, cacheRead: 0, cacheWrite: 0 },
|
|
contextWindow: 131072,
|
|
maxTokens: 32768,
|
|
},
|
|
];
|
|
allModels.push(...antigravityModels);
|
|
|
|
// Group by provider and deduplicate by model ID
|
|
const providers: Record<string, Record<string, Model<any>>> = {};
|
|
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: "${model.provider}",\n`;
|
|
if (model.baseUrl) {
|
|
output += `\t\t\tbaseUrl: "${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);
|