Model ID Cheatsheet

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Stop your AI coding agent from hallucinating outdated model names.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.

- model = "gpt-4-turbo"           # Hallucinated - doesn't exist anymore

• model = "gpt-5.3-codex" # Correct - verified against official docs

- model = "claude-3-opus-20240229" # Deprecated

• model = "claude-opus-4-6" # Current - latest Anthropic flagship

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Quick Start

Pick one option below. You'll be up and running in under a minute.

Option A: Claude Code (one command)

claude mcp add --transport sse --scope user model-id-cheatsheet \

https://universal-model-registry-production.up.railway.app/sse

Verify it works:

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:

{

"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):

{

"mcpServers": { "model-id-cheatsheet": { "serverUrl": "https://universal-model-registry-production.up.railway.app/sse" } } }

Option D: Codex CLI

Add to ~/.codex/config.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:

{

"$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.

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How It Works

Your AI agent gains 6 tools that it calls automatically before writing any model ID:

Resources

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:

# 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:

# 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:

# 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:

# 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?"*

For comparison, a single web search costs more tokens than all 6 tool schemas combined.

---

Covered Models (107 total)

Current Models (79)

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)

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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+.

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):

claude mcp add --scope user model-id-cheatsheet -- /path/to/model-id-cheatsheet

Or run in SSE mode for other clients:

MCP_TRANSPORT=sse PORT=8000 ./model-id-cheatsheet

Endpoint: http://localhost:8000/sse

Option 2: Docker

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/bu