LLM Prices

A Cloudflare Worker that caches litellm's model pricing data and provides:

• Web UI — filterable, sortable table of all LLM model prices
• REST API — query models by provider, mode, capabilities, cost, and context window
• MCP server — native remote MCP endpoint at /mcp
• OpenAPI spec — at /openapi.json for integration with LLM tools and MCP clients

Data is refreshed automatically every 6 hours via cron trigger. Zero ongoing cost on Cloudflare's free tier.

Production deployment:

https://llm-prices.llm-prices.workers.dev/

Quick Start

# Install dependencies
npm install

# Run locally
npm run dev

On first run, visit http://localhost:8787 — the table will be empty until data is loaded. Trigger a data refresh by calling the scheduled handler (wrangler dev supports this via the dashboard).

Deploy

1. Create KV Namespace

npx wrangler kv namespace create MODEL_PRICES

Copy the outputted ID and replace PLACEHOLDER_KV_ID in wrangler.toml.

For local dev, also create a preview namespace:

npx wrangler kv namespace create MODEL_PRICES --preview

Add the preview ID to wrangler.toml:

[[kv_namespaces]]
binding = "MODEL_PRICES"
id = "YOUR_PRODUCTION_ID"
preview_id = "YOUR_PREVIEW_ID"

2. Deploy

npm run deploy

3. Seed Initial Data

After deploying, trigger the cron manually from the Cloudflare dashboard (Workers → your worker → Triggers → Cron → Trigger Now), or wait up to 6 hours for the first automatic refresh.

API

GET /api/models

Query parameters:

Param	Description
q	Search model name or provider; supports wildcards like gpt-*-codex and multi-term queries like claude sonnet
provider	Filter by provider (e.g. openai, anthropic)
mode	Filter by mode (chat, embedding, completion, etc.)
supports	Comma-separated capabilities (vision, function_calling, reasoning, prompt_caching)
max_input_cost	Max input cost per token
min_context	Minimum context window (tokens)
sort	Sort field (e.g. input_cost_per_token, max_input_tokens)
order	asc or desc
limit	Results per page (default 100)
offset	Pagination offset

GET /api/providers

List all available providers.

GET /api/modes

List all available model modes.

GET /api/meta

Cache metadata (last update time).

GET /api/inspect-costs

Export Inspect-compatible model pricing as JSON or YAML.

Query parameters:

Param	Description
model	Inspect model name. Repeat the parameter to request multiple models.
models	Comma-separated Inspect model names. Alternative to repeated model parameters.
format	json or yaml (default json). Use yaml for --model-cost-config.

The response format matches Inspect's ModelCost object shape:

• input
• output
• input_cache_write
• input_cache_read

All values are returned in dollars per million tokens.

Examples:

curl "https://llm-prices.llm-prices.workers.dev/api/inspect-costs?model=openai/gpt-4o&model=anthropic/claude-sonnet-4-5&format=yaml" -o pricing.yaml

inspect eval ctf.py --model-cost-config pricing.yaml --cost-limit 2.00

curl "https://llm-prices.llm-prices.workers.dev/api/inspect-costs?models=openai/gpt-4o,google/gemini-2.5-pro,openrouter/gryphe/mythomax-l2-13b&format=json" -o pricing.json

If you want a Claude, GPT, or Gemini model but are not sure which exact model key to use, search the catalog first and pick the model yourself. You can also add sort (for example sort=key&order=desc or sort=input_cost_per_token&order=asc) to make the list easier to scan:

curl "https://llm-prices.llm-prices.workers.dev/api/models?provider=anthropic&q=claude&sort=key&order=desc"

curl "https://llm-prices.llm-prices.workers.dev/api/models?provider=openai&q=gpt&sort=key&order=desc"

curl "https://llm-prices.llm-prices.workers.dev/api/models?provider=gemini&q=gemini&sort=key&order=desc"

Then request Inspect-formatted pricing for the exact model you selected:

curl "https://llm-prices.llm-prices.workers.dev/api/inspect-costs?model=anthropic/claude-sonnet-4-5&format=yaml" -o pricing.yaml

If you want to confirm which cached dataset key was matched, add debug=1:

curl "https://llm-prices.llm-prices.workers.dev/api/inspect-costs?model=anthropic/claude-sonnet-4-5&format=yaml&debug=1" -o pricing-debug.yaml

Provider naming notes:

• Use Inspect-style provider prefixes such as openai, anthropic, google, openrouter, groq, ollama, bedrock, azureai, cf, fireworks, together, and perplexity.
• The service maps common Inspect names to LiteLLM dataset keys where they differ, for example:
  • google/... -> gemini/...
  • azureai/... -> azure_ai/...
  • cf/... -> cloudflare/@cf/...
  • groq/... -> xai/... is not applied; groq/... resolves against Groq dataset keys, while grok/... resolves against xAI keys
  • fireworks/... -> fireworks_ai/...
  • together/... -> together_ai/...
• If a model name cannot be resolved, the API returns a 400 with the unresolved model names and the candidate dataset keys it tried.

GET /openapi.json

OpenAPI 3.1 spec for tool/MCP integration.

MCP

POST /mcp

Remote MCP server endpoint exposed over Streamable HTTP.

Available tools:

• search_models — search and filter models using the same provider/mode/query/capability/cost/context filters as the REST API
• export_inspect_costs — export Inspect-compatible model cost config for one or more Inspect model names as JSON or YAML
• list_providers — list all known providers
• list_modes — list all known model modes
• get_metadata — return the last refresh timestamp and total model count

For clients that support remote MCP directly, use:

https://llm-prices.llm-prices.workers.dev/mcp

For clients that only support local stdio MCP, bridge with mcp-remote:

{
  "mcpServers": {
    "llm-prices": {
      "command": "npx",
      "args": [
        "mcp-remote",
        "https://llm-prices.llm-prices.workers.dev/mcp"
      ]
    }
  }
}

Using with LLMs / OpenAPI

The /openapi.json endpoint can be used directly with clients that support OpenAPI-based tool import.

Example: find the cheapest chat models with vision support and 100K+ context:

GET /api/models?mode=chat&supports=vision&min_context=100000&sort=input_cost_per_token&order=asc&limit=10

License

MIT