# Model ID Cheatsheet **Category:** 🎓 Education **Repository:** https://github.com/aezizhu/universal-model-registry **Views:** 0 **Installs:** 0 **Upvotes:** 0 **Directory Page:** https://allmcps.com/mcp/model-id-cheatsheet ## Description Accurate API model IDs, pricing, and specs for 46 models across 7 AI providers. ## Claude Desktop Quick Installation Heuristic fallback — verify the package name and runner against the repository README before running it. Uses `npx` (confidence: low): ```json "mcpServers": { "model-id-cheatsheet": { "command": "npx", "args": ["-y","model-id-cheatsheet"] } } ``` ## Documentation & README [![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT) [![Go](https://img.shields.io/badge/Go-1.23-00ADD8?logo=go&logoColor=white)](https://go.dev) [![Models](https://img.shields.io/badge/Models-107-blueviolet)](https://github.com/aezizhu/universal-model-registry) [![Providers](https://img.shields.io/badge/Providers-19-orange)](https://github.com/aezizhu/universal-model-registry) [![Tests](https://img.shields.io/badge/Tests-156%20passing-brightgreen)](https://github.com/aezizhu/universal-model-registry) # Model ID Cheatsheet **Stop your AI coding agent from hallucinating outdated model names.** This MCP server gives any AI assistant instant access to accurate, up-to-date API model IDs, pricing, and specs for **107 models across 19 providers**. Built in Go. Single 10MB binary. Zero external calls. Sub-millisecond responses. Auto-updated daily. ```diff - model = "gpt-4-turbo" # Hallucinated - doesn't exist anymore + model = "gpt-5.3-codex" # Correct - verified against official docs ``` ```diff - model = "claude-3-opus-20240229" # Deprecated + model = "claude-opus-4-6" # Current - latest Anthropic flagship ``` --- ## Quick Start Pick **one** option below. You'll be up and running in under a minute. ### Option A: Claude Code (one command) ```bash claude mcp add --transport sse --scope user model-id-cheatsheet \ https://universal-model-registry-production.up.railway.app/sse ``` Verify it works: ```bash claude mcp list # Should show: model-id-cheatsheet ... Connected ``` Then start a new Claude Code session and ask: *"What's the latest OpenAI model?"* - it will use the tools automatically. ### Option B: Cursor Add to `~/.cursor/mcp.json`: ```json { "mcpServers": { "model-id-cheatsheet": { "url": "https://universal-model-registry-production.up.railway.app/sse" } } } ``` Restart Cursor to pick up the change. ### Option C: Windsurf Add to Settings > MCP Servers (or edit `~/.codeium/windsurf/mcp_config.json`): ```json { "mcpServers": { "model-id-cheatsheet": { "serverUrl": "https://universal-model-registry-production.up.railway.app/sse" } } } ``` ### Option D: Codex CLI Add to `~/.codex/config.toml`: ```toml [mcp_servers.model-id-cheatsheet] command = "uvx" args = ["mcp-proxy", "--transport", "sse", "https://universal-model-registry-production.up.railway.app/sse"] ``` ### Option E: OpenCode Add to `~/.config/opencode/opencode.json`: ```json { "$schema": "https://opencode.ai/config.json", "mcp": { "model-id-cheatsheet": { "type": "remote", "url": "https://universal-model-registry-production.up.railway.app/sse" } } } ``` ### Option F: Any MCP Client Connect to the SSE endpoint directly (no API key, no auth): ``` https://universal-model-registry-production.up.railway.app/sse ``` Or use the Streamable HTTP transport: ``` https://universal-model-registry-production.up.railway.app/mcp ``` ### Verify Your Setup Once connected, try asking your AI assistant any of these: - *"What's the correct model ID for Claude Opus 4.6?"* - *"Is gpt-4o still available?"* - *"Compare gpt-5.2 vs claude-opus-4-6"* - *"What's the cheapest model with vision?"* If the agent calls a tool like `get_model_info` or `check_model_status` before answering, it's working. --- ## How It Works Your AI agent gains **6 tools** that it calls automatically before writing any model ID: | Tool | What It Does | Example Prompt | |------|-------------|----------------| | `get_model_info(model_id)` | Full specs: API ID, pricing, context window, capabilities | "What's the model ID for Claude Sonnet?" | | `list_models(provider?, status?, capability?)` | Browse and filter the registry | "Show me all current Google models" | | `recommend_model(task, budget?)` | Ranked recommendations for a task | "Best model for coding, cheap budget" | | `check_model_status(model_id)` | Verify if a model is current, legacy, or deprecated | "Is gpt-4o still available?" | | `compare_models(model_ids)` | Side-by-side comparison table | "Compare gpt-5.2 vs claude-opus-4-6" | | `search_models(query)` | Free-text search across all fields | "Search for reasoning models" | ### Resources | URI | Description | |-----|-------------| | `model://registry/all` | Full JSON dump of all 107 models | | `model://registry/current` | Only current (non-deprecated) models as JSON | | `model://registry/pricing` | Pricing table sorted cheapest-first (markdown) | ### What Happens Under the Hood 1. You ask your agent to write code or answer a model question 2. The agent automatically calls the appropriate tool (e.g., `get_model_info`) 3. The server responds in sub-milliseconds with verified data (no external API calls) 4. The agent writes code with the correct, current model ID The server instructions tell the agent: *"NEVER use a model ID from your training data without verifying it first."* This means the agent will always check before writing. --- ## Real-World Examples **Writing an API call:** ```python # You: "Call the OpenAI API with their best coding model" # Agent calls: get_model_info("gpt-5.4") response = client.chat.completions.create( model="gpt-5.4", # Verified via model registry messages=[...] ) ``` **Catching deprecated models:** ```python # You: "Use gpt-4o for this task" # Agent calls: check_model_status("gpt-4o") # Agent: "gpt-4o is deprecated. I'll use gpt-5 instead." response = client.chat.completions.create( model="gpt-5", # Updated automatically messages=[...] ) ``` **Finding the cheapest option:** ```python # You: "Use the cheapest model that supports vision" # Agent calls: list_models(capability="vision", status="current") response = client.chat.completions.create( model="gpt-5-nano", # $0.05/$0.40 per 1M tokens messages=[...] ) ``` **Comparing options:** ```python # You: "Should I use Claude or GPT for this?" # Agent calls: compare_models(["claude-opus-4-6", "gpt-5.2"]) # Agent gets a side-by-side table and makes a recommendation ``` --- ## Resource Footprint A common concern: *"Will this slow down my agent or eat tokens?"* | Metric | Value | |--------|-------| | Binary size | ~10MB | | Runtime memory | Minimal (static in-memory map, no database) | | External API calls | Zero (all data is baked in) | | Response time | Sub-millisecond | | Token cost per tool call | ~200-500 tokens (small text response) | | Tool schema overhead | ~500-800 tokens in system prompt | For comparison, a single web search costs more tokens than all 6 tool schemas combined. --- ## Covered Models (107 total) ### Current Models (79) | Provider | Models | API IDs | |----------|--------|---------| | **OpenAI** (15) | GPT-5.4, GPT-5.4 Pro, GPT-5.3 Instant, GPT-5.2, GPT-5.2 Pro, GPT-5.1, GPT-5.1 Codex, GPT-5.1 Mini, GPT-5, GPT-5 Mini, GPT-5 Nano, GPT-4.1 Mini, GPT-4.1 Nano, o3, o4-mini | `gpt-5.4`, `gpt-5.4-pro`, `gpt-5.3-chat-latest`, `gpt-5.2`, `gpt-5.2-pro`, `gpt-5.1`, `gpt-5.1-codex`, `gpt-5.1-mini`, `gpt-5`, `gpt-5-mini`, `gpt-5-nano`, `gpt-4.1-mini`, `gpt-4.1-nano`, `o3`, `o4-mini` | | **Anthropic** (4) | Claude Opus 4.6, Claude Sonnet 4.6, Claude Sonnet 4.5, Claude Haiku 4.5 | `claude-opus-4-6`, `claude-sonnet-4-6`, `claude-sonnet-4-5-20250929`, `claude-haiku-4-5-20251001` | | **Mistral** (11) | Mistral Large 3, Mistral Medium 3, Mistral Small 3.2, Mistral Saba, Ministral 3B, Ministral 8B, Ministral 14B, Magistral Small 1.2, Magistral Medium 1.2, Devstral 2, Devstral Small 2 | `mistral-large-2512`, `mistral-medium-2505`, `mistral-small-2506`, `mistral-saba-2502`, `ministral-3b-2512`, `ministral-8b-2512`, `ministral-14b-2512`, `magistral-small-2509`, `magistral-medium-2509`, `devstral-2512`, `devstral-small-2512` | | **Amazon** (6) | Nova Micro, Nova Lite, Nova Pro, Nova Premier, Nova 2 Lite, Nova 2 Pro | `amazon-nova-micro`, `amazon-nova-lite`, `amazon-nova-pro`, `amazon-nova-premier`, `amazon-nova-2-lite`, `amazon-nova-2-pro` | | **Google** (5) | Gemini 3.1 Pro, Gemini 3.1 Flash Lite, Gemini 3 Flash, Gemini 2.5 Pro, Gemini 2.5 Flash | `gemini-3.1-pro-preview`, `gemini-3.1-flash-lite-preview`, `gemini-3-flash-preview`, `gemini-2.5-pro`, `gemini-2.5-flash` | | **Cohere** (5) | Command A, Command A Reasoning, Command A Vision, Command A Translate, Command R7B | `command-a-03-2025`, `command-a-reasoning-08-2025`, `command-a-vision-07-2025`, `command-a-translate-08-2025`, `command-r7b-12-2024` | | **xAI** (4) | Grok 4, Grok 4.1 Fast, Grok 4 Fast, Grok Code Fast 1 | `grok-4`, `grok-4.1-fast`, `grok-4-fast`, `grok-code-fast-1` | | **Microsoft** (4) | Phi-4, Phi-4 Multimodal, Phi-4 Reasoning, Phi-4 Reasoning Plus | `phi-4`, `phi-4-multimodal-instruct`, `phi-4-reasoning`, `phi-4-reasoning-plus` | | **Perplexity** (4) | Sonar, Sonar Pro, Sonar Reasoning Pro, Sonar Deep Research | `sonar`, `sonar-pro`, `sonar-reasoning-pro`, `sonar-deep-research` | | **Moonshot** (3) | Kimi K2.5, Kimi K2 Thinking, Kimi K2 (0905) | `kimi-k2.5`, `kimi-k2-thinking`, `kimi-k2-0905-preview` | | **Tencent** (3) | Hunyuan TurboS, Hunyuan T1, Hunyuan A13B | `hunyuan-turbos`, `hunyuan-t1`, `hunyuan-a13b` | | **Zhipu** (3) | GLM-5, GLM-4.7, GLM-4.7 FlashX | `glm-5`, `glm-4.7`, `glm-4.7-flashx` | | **Meta** (2) | Llama 4 Maverick, Llama 4 Scout | `llama-4-maverick`, `llama-4-scout` | | **DeepSeek** (2) | DeepSeek Reasoner, DeepSeek Chat | `deepseek-reasoner`, `deepseek-chat` | | **NVIDIA** (2) | Nemotron 3 Nano 30B, Nemotron Ultra 253B | `nvidia/nemotron-3-nano-30b-a3b`, `nvidia/llama-3.1-nemotron-ultra-253b-v1` | | **AI21** (2) | Jamba Large 1.7, Jamba Mini 1.7 | `jamba-large-1.7`, `jamba-mini-1.7` | | **MiniMax** (2) | MiniMax M2.5, MiniMax M2.5 Lightning | `minimax-m2.5`, `minimax-m2.5-lightning` | | **Kuaishou** (1) | KAT-Coder Pro | `kat-coder-pro` | | **Xiaomi** (1) | MiMo V2 Flash | `mimo-v2-flash` | ### Legacy & Deprecated Models (30) Tracked so your agent can detect outdated model IDs and suggest current replacements: - **OpenAI**: `gpt-5.3-codex` (deprecated), `gpt-5.2-codex` (deprecated), `gpt-5.1-codex-mini` (deprecated), `o3-pro` (deprecated), `o3-deep-research` (deprecated), `o3-mini` (legacy), `gpt-4.1` (deprecated), `gpt-4o` (deprecated), `gpt-4o-mini` (deprecated) - **Anthropic**: `claude-opus-4-5` (legacy), `claude-opus-4-1` (legacy), `claude-opus-4-0` (legacy), `claude-sonnet-4-0` (legacy), `claude-3-7-sonnet-20250219` (deprecated) - **Google**: `gemini-3-pro-preview` (deprecated), `gemini-3-pro-image-preview` (deprecated), `gemini-2.5-flash-lite` (deprecated), `gemini-2.0-flash-lite` (deprecated), `gemini-2.0-flash` (deprecated) - **xAI**: `grok-4.1` (deprecated), `grok-3` (legacy), `grok-3-mini` (legacy) - **Mistral**: `mistral-small-2503` (legacy), `codestral-2508` (legacy) - **MiniMax**: `minimax-m2.1` (legacy), `minimax-01` (deprecated) - **Meta**: `llama-3.3-70b` (legacy) - **DeepSeek**: `deepseek-r1` (legacy), `deepseek-v3` (deprecated) - **Zhipu**: `glm-4.6v` (deprecated) --- ## Self-Hosting If you prefer to run the server locally instead of using the hosted endpoint: ### Option 1: Build from Source (recommended for local use) Requires Go 1.23+. ```bash git clone https://github.com/aezizhu/universal-model-registry.git cd universal-model-registry/go-server go build -o model-id-cheatsheet ./cmd/server ``` Then add it to Claude Code as a local stdio server (zero latency, no network): ```bash claude mcp add --scope user model-id-cheatsheet -- /path/to/model-id-cheatsheet ``` Or run in SSE mode for other clients: ```bash MCP_TRANSPORT=sse PORT=8000 ./model-id-cheatsheet # Endpoint: http://localhost:8000/sse ``` ### Option 2: Docker ```bash git clone https://github.com/aezizhu/universal-model-registry.git cd universal-model-registry docker build -t model-id-cheatsheet . docker run -p 8000:8000 model-id-cheatsheet ``` Your SSE endpoint will be at `http://localhost:8000/sse`. ### Option 3: Deploy to Railway [![Deploy on Railway](https://railway.com/button.svg)](https://railway.com/template) Or manually: ```bash railway login railway init railway up ``` --- ## Staying Up to Date Model data is automatically checked and updated **daily at 7 PM Pacific Time** -- no human intervention needed. **How it works:** 1. Railway cron runs the updater daily, scraping 6 providers' public documentation pages (no API keys needed) 2. **Models removed from docs** --> auto-deprecated via PR (status changed to `"deprecated"` in code) 3. **New models detected** --> GitHub issue created for review 4. CI runs on the auto-generated PR --> if tests pass --> **auto-merged** into main 5. Railway auto-deploys from main **No provider API keys required.** The updater reads publicly available documentation pages to detect model changes. Only `GITHUB_TOKEN` and `GITHUB_REPO` are needed for creating PRs and issues.
Auto-Update Pipeline Details **Railway Cron (primary)** -- The hosted instance uses a Railway cron service that runs the updater daily. See `configs/railway-updater.toml` for the configuration. Required env vars (set in Railway dashboard): - `GITHUB_TOKEN` -- GitHub personal access token with repo scope - `GITHUB_REPO` -- Repository in `"owner/repo"` format (e.g. `"aezizhu/universal-model-registry"`) **Providers checked (via public docs):** - OpenAI (via GitHub SDK source), Anthropic, Google, Mistral, xAI, DeepSeek **CI/CD Workflows:** - `.github/workflows/ci.yml` -- runs tests on every PR - `.github/workflows/auto-merge.yml` -- auto-merges bot PRs (labeled `auto-update`) after CI passes **GitHub Actions (alternative)** -- A GitHub Actions workflow is also included at `.github/workflows/auto-update.yml` for users who self-host without Railway. No API keys needed -- only `GITHUB_TOKEN` (automatically provided by GitHub Actions).
--- ## Security - **Rate limiting**: 60 requests/minute per IP - **Connection limits**: Max 5 SSE connections per IP, 100 total - **Request body limit**: 64KB max - **Input sanitization**: All string inputs truncated to safe lengths - **HTTP hardening**: ReadTimeout 15s, ReadHeaderTimeout 5s, IdleTimeout 120s, 64KB max headers - **Non-root Docker**: Containers run as unprivileged user - **Graceful shutdown**: Clean connection draining on SIGINT/SIGTERM ## Tech Stack - **Language**: Go 1.23 - **MCP SDK**: `github.com/modelcontextprotocol/go-sdk` v1.3.0 (official) - **Transports**: stdio, SSE, Streamable HTTP - **Binary size**: ~10MB - **Tests**: 156 unit tests - **Security**: Per-IP rate limiting, connection limits, input sanitization - **Deploy**: Docker (alpine), Railway ## Contributing Contributions are welcome! Whether it's adding a new model, fixing data, or improving the server: 1. Fork the repo and clone it locally 2. Edit model data in `go-server/internal/models/data.go` 3. Update test counts in `go-server/internal/models/data_test.go` 4. Run the tests: ```bash cd go-server && go test ./... -v ``` 5. Submit a PR -- we'll review it quickly If you spot an outdated model or incorrect pricing, opening an issue is just as helpful. ## License MIT