OpenRouter Model Catalog

Overview

Query the GET /api/v1/models endpoint to browse 400+ models, filter by capabilities, compare pricing, and check provider endpoints. No API key required for the models endpoint.

Prerequisites

• curl and jq for the command-line catalog queries — GET /api/v1/models itself requires no auth
• An OpenRouter API key exported as OPENROUTERAPIKEY only for the Special Routers completion example — see the openrouter-install-auth skill for setup
• Python 3.8+ with requests for filtering, plus the OpenAI SDK for the openrouter/auto example (pip install requests openai)

Instructions

1. List the catalog per List All Models: curl -s https://openrouter.ai/api/v1/models | jq '.data | length'; add ?supported_parameters=tools to filter to tool-calling models.
2. Read Model Object Shape to interpret each entry — pricing.prompt/pricing.completion are per token (multiply by 1M for readable rates), plus contextlength, topprovider.maxcompletiontokens, and architecture.modality.
3. Filter programmatically per Python: Query and Filter — free models, tool-calling models, cheapest paid models sorted by prompt price, and 128K+ context models.
4. Compare per-provider pricing and quantization for a single model via GET /api/v1/models/{id}/endpoints per List Providers for a Model.
5. Pick behavior with a suffix per Model Variants (:free, :nitro, :floor, :extended, :thinking), or delegate selection entirely to openrouter/auto per Special Routers.
6. Sanity-check choices against the Popular Model Quick Reference, but always verify live pricing via /api/v1/models — prices change frequently.

List All Models

# Full catalog (no auth required)
curl -s https://openrouter.ai/api/v1/models | jq '.data | length'
# → 400+

# Filter to text output models only
curl -s "https://openrouter.ai/api/v1/models?supported_parameters=tools" | jq '.data | length'

Model Object Shape

{
  "id": "anthropic/claude-3.5-sonnet",
  "name": "Claude 3.5 Sonnet",
  "description": "Anthropic's most intelligent model...",
  "context_length": 200000,
  "pricing": {
    "prompt": "0.000003",
    "completion": "0.000015",
    "image": "0.0048",
    "request": "0"
  },
  "top_provider": {
    "context_length": 200000,
    "max_completion_tokens": 8192,
    "is_moderated": false
  },
  "per_request_limits": null,
  "architecture": {
    "modality": "text+image->text",
    "tokenizer": "Claude",
    "instruct_type": null
  }
}

Key fields:

• pricing.prompt / pricing.completion -- cost per token (not per million; multiply by 1M for readable rates)
• context_length -- max input tokens
• topprovider.maxcompletion_tokens -- max output tokens
• architecture.modality -- text->text, text+image->text, etc.

Python: Query and Filter

import requests

models = requests.get("https://openrouter.ai/api/v1/models").json()["data"]

# Find all free models
free_models = [m for m in models if m["pricing"]["prompt"] == "0"]
print(f"Free models: {len(free_models)}")

# Models with tool calling support
# (query with supported_parameters)
tool_models = requests.get(
    "https://openrouter.ai/api/v1/models?supported_parameters=tools"
).json()["data"]
print(f"Tool-calling models: {len(tool_models)}")

# Sort by prompt price (cheapest first, excluding free)
paid = [m for m in models if float(m["pricing"]["prompt"]) > 0]
paid.sort(key=lambda m: float(m["pricing"]["prompt"]))
for m in paid[:10]:
    cost_per_m = float(m["pricing"]["prompt"]) * 1_000_000
    print(f"  ${cost_per_m:.2f}/M tokens — {m['id']} ({m['context_length']//1000}K ctx)")

# Filter by context length (128K+)
large_ctx = [m for m in models if m["context_length"] >= 128_000]
print(f"128K+ context models: {len(large_ctx)}")

List Providers for a Model

# See all providers and their pricing for a specific model
curl -s "https://openrouter.ai/api/v1/models/anthropic/claude-3.5-sonnet/endpoints" | jq '.data[] | {
  provider: .provider_name,
  price_prompt: .pricing.prompt,
  price_completion: .pricing.completion,
  context_length: .context_length,
  quantization: .quantization
}'

Model Variants

Append a suffix to any model ID for variant behavior:

Special Routers

# Let OpenRouter pick the best model
response = client.chat.completions.create(
    model="openrouter/auto",
    messages=[{"role": "user", "content": "Write a SQL query to find duplicate emails"}],
    max_tokens=200,
)
print(f"Auto-selected: {response.model}")  # Shows which model was chosen

Popular Model Quick Reference

Prices change frequently. Always verify via /api/v1/models.

Output

• Raw catalog JSON: one object per model with id, contextlength, pricing, topprovider, and architecture fields
• Filtered console listings, e.g. counts of free / tool-calling / 128K+ models and cheapest-paid lines like $0.06/M tokens — meta-llama/llama-3.1-8b-instruct (128K ctx)
• Per-provider endpoint rows for one model: providername, prompt/completion pricing, contextlength, quantization
• For openrouter/auto requests, response.model reveals which model the router actually selected

Examples

Fetch the catalog once, then slice it three ways with the filters from Python: Query and Filter:

models = requests.get("https://openrouter.ai/api/v1/models").json()["data"]
free = [m for m in models if m["pricing"]["prompt"] == "0"]
large = [m for m in models if m["context_length"] >= 128_000]
print(f"Total: {len(models)}, free: {len(free)}, 128K+: {len(large)}")
# Total: 267, free: 12, 128K+: 45   (counts drift as the catalog changes)

The same pass sorted by prompt price surfaces the cheapest paid options — meta-llama/llama-3-8b-instruct: $0.05/1M prompt tokens leads the list in the worked run. More worked examples: references/examples.md.

Error Handling

Enterprise Considerations

• Cache the model catalog with 1-hour TTL (model availability changes infrequently)
• Build a model allowlist for your organization to restrict which models teams can use
• Monitor /api/v1/models for deprecation notices and new model additions
• Use supported_parameters query filter to ensure models support features you need (tools, JSON mode, etc.)
• Compare providers via the endpoints API to find the cheapest or fastest provider for each model

References

• Examples | Errors
• Models Docs | Models API | Model Variants