Model ID Cheatsheet
Accurate API model IDs, pricing, and specs for 46 models across 7 AI providers.
Open source Repository Open in the app JSON README (API)
About
Accurate API model IDs, pricing, and specs for 46 models across 7 AI providers.
Details
- Kind
- MCP servers
- Topic
- No topic detected
- Publisher
- aezizhu
- Origin
- official
- Category
- ferramentas
- Transport
- sse
- Version
- 1.0.0
- Last push
- 2026-03-15T17:40:40Z
- Repository state
- ativo
- Language
- Go
- Added
- 2026-08-29 03:02:23
- Updated
- 2026-08-29 03:02:23
- Origin id
io.github.aezizhu/model-id-cheatsheet
README
[](https://opensource.org/licenses/MIT)
[](https://go.dev)
[](https://github.com/aezizhu/universal-model-registry)
[](https://github.com/aezizhu/universal-model-registry)
[](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**Stop your AI coding agent from hallucinating outdated model names.** This MCP server gives any AI assistant instant access to accurate, up-to-date API model IDs, pricing, and specs for **107 models across 19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
[](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.
<details>
<summary><strong>Auto-Update Pipeline Details</strong></summary>
**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).
</details>
---
## 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