Model ID Cheatsheet

io.github.aezizhuPublished by GitHub account aezizhuv1.0.0Updated Oct 24, 2026

Accurate API model IDs, pricing, and specs for 46 models across 7 AI providers.

The publisher's own server: model.tools — Zero-Auth Agent Utilities

VerifiedSSEWeb executableDeveloper ToolsAI & ML

Overview

AI-generated overview

A read-only model registry MCP server that lets an assistant look up current API model IDs, pricing, specs and deprecation status across many AI providers.

What you can do
Look up a model's API ID, pricing, context window and capabilities, and check whether it is current, legacy or deprecated, with suggested replacements for outdated IDs. Browse and filter the registry by provider, status or capability, and search it by free text. Get ranked model recommendations for a task and budget, and compare several models side by side. Read the full registry, the current-only list or a cheapest-first pricing table as resources.
When to use it
Useful when an assistant writes code that names a model ID, or answers questions about which models exist, what they cost, or which is cheapest or best for a task, and you want answers grounded in a maintained registry rather than training data.
Requirements
A remote SSE endpoint (or a Streamable HTTP endpoint) with no API key or authentication; network access to the hosted server. Self-hosting is optional and needs Go 1.23+ or Docker.
Before you install
The hosted endpoint is third-party infrastructure, so queries and your IP go to that operator; it applies per-IP rate and connection limits. The registry is read-only and makes no external calls, but its data is refreshed from public documentation and may lag real provider changes, so verify critical model IDs against official docs.

Installation

In SourceWeft

  1. Open Model ID Cheatsheet in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Web executable via SSE. Remote servers run from the web runtime once configured in a workspace.

Use with an agent

Paste this prompt into Claude Code, Codex, Cursor or another agent — or into SourceWeft chat. The agent reads this server's install guide, shows you its publisher, endpoint and sign-in, and adds it to your client once you agree. Keys never go into a command.

Read https://sourceweft.com/mcp/io.github.aezizhu%2Fmodel-id-cheatsheet/install.md and connect the MCP server it describes to my client. Before changing any config, show me its publisher, endpoint and sign-in method and wait for my OK. Never put an API key in a command or in a file I would commit; ask me where it should come from.

Read the install guide the agent follows

Other MCP clients

Add this to your client's mcpServers config.

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

README

[License: MIT] [Go] [Models] [Providers] [Tests]

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:

ToolWhat It DoesExample 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

URIDescription
model://registry/allFull JSON dump of all 107 models
model://registry/currentOnly current (non-deprecated) models as JSON
model://registry/pricingPricing 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?"

MetricValue
Binary size~10MB
Runtime memoryMinimal (static in-memory map, no database)
External API callsZero (all data is baked in)
Response timeSub-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)

ProviderModelsAPI 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-minigpt-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 4.5claude-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 2mistral-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 Proamazon-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 Flashgemini-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 R7Bcommand-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 1grok-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 Plusphi-4, phi-4-multimodal-instruct, phi-4-reasoning, phi-4-reasoning-plus
Perplexity (4)Sonar, Sonar Pro, Sonar Reasoning Pro, Sonar Deep Researchsonar, 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 A13Bhunyuan-turbos, hunyuan-t1, hunyuan-a13b
Zhipu (3)GLM-5, GLM-4.7, GLM-4.7 FlashXglm-5, glm-4.7, glm-4.7-flashx
Meta (2)Llama 4 Maverick, Llama 4 Scoutllama-4-maverick, llama-4-scout
DeepSeek (2)DeepSeek Reasoner, DeepSeek Chatdeepseek-reasoner, deepseek-chat
NVIDIA (2)Nemotron 3 Nano 30B, Nemotron Ultra 253Bnvidia/nemotron-3-nano-30b-a3b, nvidia/llama-3.1-nemotron-ultra-253b-v1
AI21 (2)Jamba Large 1.7, Jamba Mini 1.7jamba-large-1.7, jamba-mini-1.7
MiniMax (2)MiniMax M2.5, MiniMax M2.5 Lightningminimax-m2.5, minimax-m2.5-lightning
Kuaishou (1)KAT-Coder Prokat-coder-pro
Xiaomi (1)MiMo V2 Flashmimo-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.gitcd universal-model-registry/go-servergo 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.gitcd universal-model-registrydocker 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]

Or manually:

bash
railway loginrailway initrailway 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

Source: README.md at commit 8e309b1

Tools

0
SourceWeft has not read this server's tools yet.

Version history

1
  1. v1.0.0LatestSep 16, 2026

Score

75% of the applicable checks met

How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.

  • Reached in the last 30 daysRequired25 pts
  • Auth declaredRequired10 pts
  • Tools listedRequired15 pts

    Answer tools/list without a credential, or ship a package that starts in a sandbox without one.

  • DescriptionRequired15 pts
  • Logo10 pts
  • Documentation15 pts
  • License10 pts

    Declare a license in the repository or the registry entry.

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