or-info
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@or-infocompare gpt-4o and claude-sonnet-4.5"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
or-info
CLI + MCP server to query OpenRouter model info: prices, ELO rankings, context and comparisons.
Any person or AI agent (Claude Code, Codex, Cursor, pi, etc.) can install it and use it to make informed decisions about which model to use.
Install
npm install -g @aggc/or-info
or-info --versionThe npm package is published as @aggc/or-info, but the installed executable is
or-info.
You can also run it without a global install:
npx -y @aggc/or-info models --limit 5Requires Node.js 22 or later.
Install from Smithery
Each release is also published as a Smithery MCPB bundle, importable in one click from clients that support the MCPB format (Claude Desktop, etc.):
https://smithery.ai/server/aggc/or-info
The bundle is attached as an asset to every GitHub release (or-info.mcpb) and
can also be installed manually by dropping the file into the client.
Supported runtimes and platforms:
Node.js 22+
macOS, Linux, Windows
Config and cache paths
or-info resolves config and cache natively per platform:
Platform | Config directory | Cache directory |
macOS / Linux |
|
|
Windows |
|
|
Any platform |
|
|
Files of interest:
Config file:
<config-dir>/.envModel cache:
<cache-dir>/models.jsonLMArena cache:
<cache-dir>/benchmarks.json
API key (optional)
Without an API key the CLI works with OpenRouter's public catalog. With a key you also see private/pay-gated models.
Bash / Zsh
export OPENROUTER_API_KEY=sk-or-...PowerShell
$env:OPENROUTER_API_KEY = "sk-or-..."CMD
set OPENROUTER_API_KEY=sk-or-...Config file
macOS / Linux:
mkdir -p ~/.config/or-info
echo 'OPENROUTER_API_KEY=sk-or-...' >> ~/.config/or-info/.envWindows PowerShell:
New-Item -ItemType Directory -Force "$env:APPDATA\or-info" | Out-Null
Add-Content -Path "$env:APPDATA\or-info\.env" -Value "OPENROUTER_API_KEY=sk-or-..."Windows CMD:
if not exist "%APPDATA%\or-info" mkdir "%APPDATA%\or-info"
echo OPENROUTER_API_KEY=sk-or-...>> "%APPDATA%\or-info\.env"For tests and debugging you can redirect storage without touching your real machine state:
OR_INFO_CONFIG_DIR=/tmp/or-info-config OR_INFO_CACHE_DIR=/tmp/or-info-cache or-info statusCLI usage
List models
or-info models # All models sorted by name
or-info models --sort price # Cheapest output first
or-info models --sort context # Largest context first
or-info models --filter coding # Models whose ID/name contains "coding"
or-info models --free # Free models only
or-info models --limit 20 # Limit the number of results
or-info models --tags # Show feature tags (vision, tools, reasoning…)
or-info models --json # Raw JSONPricing and details
or-info price anthropic/claude-sonnet-4.5
or-info price google/gemini-2.5-flash --jsonELO ranking (LMArena)
or-info benchmark openai/gpt-4o
or-info benchmark deepseek/deepseek-r1 --jsonShows the model's ELO score from LMArena (human preference votes), confidence interval, global rank and vote count. Data is fetched live from HuggingFace and cached locally for 24 hours — no API key required.
Compare two models
or-info compare anthropic/claude-sonnet-4.5 google/gemini-2.5-flash
or-info compare openai/gpt-4o deepseek/deepseek-chat-v3-0324 --jsonTop models for a task
or-info top --task coding # Best coding models
or-info top --task reasoning # Best reasoning models
or-info top --task general # Best all-rounders
or-info top --task vision # Best vision models (requires image input)
or-info top --task cheap # Best value for money
or-info top --task premium # Highest quality, ignoring price
or-info top --task coding --pricing premium # Best coder regardless of price
or-info top --task coding --budget 2 # Best coders under $2/M output
or-info top --task general --limit 10Ranking combines LMArena ELO with price and context window size.
--task controls which ELO category and capability filter to apply.
--pricing overrides the price-penalty strategy independently:
| Effect |
| Moderate penalty for expensive models |
| Steep penalty; strongly favours free/low-cost models |
| No penalty; ranks by quality alone |
Task defaults (when --pricing is not set):
Task | Default pricing | Capability filter |
| standard | none |
| standard | soft penalty (−15%) if no tool support |
| standard | none |
| standard | hard filter: image input required |
| cheap | none |
| premium | none |
Cache management
or-info status # Show cache age and TTL for each data source
or-info refresh # Force-refresh OpenRouter catalog + LMArena ELOMCP server
or-info can run as an MCP server for AI agents.
Tools available
Tool | Description |
| Pricing, context, architecture, features and LMArena ELO for a model |
| List models with optional filter, sort and limit |
| Side-by-side comparison of two models |
| Ranked top models for coding/reasoning/general/vision/cheap/premium; accepts optional |
| LMArena ELO score, global rank, vote count and confidence interval for a model |
| Force-refresh OpenRouter catalog + LMArena ELO |
Legacy flat names (get_model_info, list_models, get_benchmarks,
compare_models, best_for_task, refresh_cache) are still advertised in
tools/list as deprecated aliases (same schemas, prefixed [Deprecated])
and remain callable. The dot-notation names are the canonical ones.
Register in Claude Code
The recommended way is the claude mcp add command, which writes to ~/.claude.json:
# Global — available in all projects
claude mcp add --scope user or-info -- or-info --mcp
# Project-only (run from the project directory)
claude mcp add or-info -- or-info --mcpThen verify:
claude mcp listProject .mcp.json — commit this file to share the config with your team:
macOS / Linux:
{
"mcpServers": {
"or-info": {
"command": "or-info",
"args": ["--mcp"]
}
}
}Windows:
{
"mcpServers": {
"or-info": {
"command": "or-info.cmd",
"args": ["--mcp"]
}
}
}Without a global install (macOS/Linux):
{
"mcpServers": {
"or-info": {
"command": "npx",
"args": ["-y", "@aggc/or-info", "--mcp"]
}
}
}Without a global install (Windows):
{
"mcpServers": {
"or-info": {
"command": "npx.cmd",
"args": ["-y", "@aggc/or-info", "--mcp"]
}
}
}Register in Codex
Add to ~/.codex/config.toml:
[mcp_servers.or-info]
command = "or-info"
args = ["--mcp"]Then restart Codex for the change to take effect.
Use from Pi
Pi does not use an mcpServers settings schema. The recommended integration is a Pi skill
that calls the installed or-info CLI, for example ~/.pi/agent/skills/or-info/SKILL.md.
Test the MCP server
macOS / Linux:
echo '{"jsonrpc":"2.0","id":1,"method":"tools/list"}' | or-info --mcpWindows PowerShell:
'{"jsonrpc":"2.0","id":1,"method":"tools/list"}' | or-info.cmd --mcpWindows CMD:
echo {"jsonrpc":"2.0","id":1,"method":"tools/list"} | or-info.cmd --mcpData sources
Data | Source | Refresh |
Model catalog and pricing | Every 30 min | |
ELO rankings | Every 24 h |
ELO data is fetched directly from the lmarena-ai/leaderboard-dataset dataset on HuggingFace
using their public Datasets Server API — no API key required. Coverage: ~350 models
including all major commercial and open-source models tracked by LMArena.
Testing
npm testnpm test runs the deterministic local suite and is the release gate used before publishing.
Live integration tests are available separately because they depend on OpenRouter and
HuggingFace availability and can occasionally hit third-party rate limits.
Additional entry points:
npm run test:localfor deterministic no-network coveragenpm run test:online:smokefor the live smoke subset used by CI as a non-blocking signalnpm run test:onlinefor the full live CLI/MCP suite, including edge cases
Release
See CHANGELOG.md for release history and details.
Contributing
See CONTRIBUTING.md — adding new CLI commands or new MCP tools.
License
This server cannot be installed
Maintenance
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