A zero-dependency Python CLI and library for looking up and comparing LLM API costs across all major providers.
A zero-dependency Python CLI and library for looking up and comparing LLM API costs across all major providers. That is what the llm prices mcp server brings to an AI assistant: the same capability, reachable through the Model Context Protocol rather than a separate app or dashboard.
The server publishes 7 tools. What each one is for:
get_model_pricing — Get pricing for a specific modelcalculate_api_cost — Calculate exact cost for input+output tokenscompare_models — Compare cost of a workload across multiple modelsfind_cheapest_models — Find the N cheapest models for your workloadlist_providers — List all 24 providers with min pricingsearch_llm_models — Search models by name or filter by providerSources — The Sources tool exposed by this serverThe server ships on PyPI as git, so your MCP client can launch it on demand — there is no separate build step. Add the server block to your client's configuration, restart it, and the tools register themselves.
Among the AI and media services options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. Llm Prices's toolset — get_model_pricing, calculate_api_cost, compare_models and 4 more — is a fair guide to whether it matches your workflow. It is maintained by benbencodes; worth a glance at recent repository activity before you build anything load-bearing on it.
This entry was verified against Llm Prices's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.
| Tool | What it does |
|---|---|
| get_model_pricing | Get pricing for a specific model |
| calculate_api_cost | Calculate exact cost for input+output tokens |
| compare_models | Compare cost of a workload across multiple models |
| find_cheapest_models | Find the N cheapest models for your workload |
| list_providers | List all 24 providers with min pricing |
| search_llm_models | Search models by name or filter by provider |
| Sources | The Sources tool exposed by this server. |
Or for `uvx` users:
```json
{
"mcpServers": {
"llm-prices": {
"command": "uvx",
"args": ["--from", "git+https://github.com/benbencodes/llm-prices[mcp]", "llm-prices-mcp"]
}
}
}Configuration as documented by the project. Restart the client after saving.
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.