Someone asks what the AI feature will cost at scale, and the honest answer around most teams is a shrug. Rates moved twice since anyone last checked, and the model itself will happily quote prices from its training data. This MCP server keeps current per-million-token rates for 69 models where your assistant can reach them, and does the arithmetic itself.

Watch it work

$ You: price Claude Opus 4.8 on a 25k-token prompt with a 1k answer, run 5,000 times

  llm-cost › estimate_cost

  Cost estimate: Claude Opus 4.8 (Anthropic)
  Rates: input $5/1M, output $25/1M, cached input $0.5/1M

  Per call (25,000 in + 1,000 out tokens):
    input:  $0.1250
    output: $0.0250
    per call total: $0.1500

  Across 5,000 calls: $750.00

  Ways to pay less for the same 5,000 calls:
    with cached input:  $187.50
    via batch API:      $375.00

Nothing here is rounded or guessed. The rate is verified, date-stamped, and the server multiplied.

Open a second session: same call across four models, ranked

$ You: compare that call on Opus 4.8, Sonnet 5, GPT-5.6 Terra and Gemini 3.1 Pro

  llm-cost › compare_models_cost

  25,000 in + 1,000 out, cheapest first:

  1. Claude Sonnet 5      $0.0600 /call    $300.00 /5k
  2. Gemini 3.1 Pro       $0.0620 /call    $310.00 /5k
  3. GPT-5.6 Terra        $0.0775 /call    $387.50 /5k
  4. Claude Opus 4.8      $0.1500 /call    $750.00 /5k

  Ranking uses each model's live feed entry, not remembered prices.

The gap it closes

An assistant on its own	An assistant with llm-cost
quotes output rates from training data, often a generation stale	reads the current rate, dated
flattens the estimate to "a few cents"	$0.1500 per call, $750.00 across 5,000
never mentions batch or caching discounts	$375.00 batch, $187.50 cached, only where the model really offers them
confidently wrong, no way to tell	every figure traces to a feed entry with a verification date

The same numbers answer in a browser through the LLM calculator, and the LLM category hub ranks every tracked model by rating and price.

Where the numbers travel

flowchart LR
    V["provider pricing pages<br/>17 providers"] --> CE["verification<br/>date-stamped checks"]
    CE --> F["model prices feed<br/>69 models, USD per 1M tokens"]
    F -->|"6h cache, serve stale on failure"| MCP["llm-cost server<br/>local arithmetic"]
    MCP --> A["your agent"]

Loading

The server never calls a provider API. It reads one public feed, llms-model-prices.json, and computes locally. Nothing to rate-limit, no key to leak, and a network hiccup serves the last good copy instead of an error. How each price gets checked is written up in the methodology, and the catalog behind it ships as an open dataset under CC BY 4.0.

The six tools

Four do the math. Two help you find the exact model id the math wants.

Tool	Answers	Params
estimate_cost	one call, or N identical calls, in dollars	model, input_tokens, output_tokens, calls?
compare_models_cost	the same call priced across 2 to 6 models	models[], input_tokens, output_tokens
monthly_budget	daily, monthly, yearly spend for a workload	model, daily_calls, avg_input_tokens, avg_output_tokens
cheapest_models	lowest-cost models, optional context floor	min_context?, limit?
list_models	every model with rates, context and tier	provider?
list_providers	providers with model counts and cheapest pick	none

Token rules of thumb, for when nobody knows the counts

• A page of English prose is roughly 500 tokens; one token is about four characters.
• Model references are forgiving: claude-opus-4-8, Opus 4.8 and anthropic/opus resolve to the same model. When the resolver is unsure, it returns candidates instead of guessing.
• cheapest_models ranks by a blended rate weighting input to output 3 to 1, because real workloads read far more than they write. Confirm the winner with estimate_cost on your actual split.

Prompts

Prompt	Args	Runs
estimate_my_workflow	workflow, model?	token estimates plus per-run and monthly cost for a described workflow
pick_cheapest_model	task	cheapest model that still meets the requirement, top candidates priced
forecast_ai_budget	model, usage	monthly and yearly bill projected from expected volume

Wire it up

{
  "mcpServers": {
    "llm-cost": {
      "command": "npx",
      "args": ["-y", "@comparedge/llm-cost-mcp@latest"]
    }
  }
}

Claude Desktop keeps this file at ~/Library/Application Support/Claude/claude_desktop_config.json. Cursor: Settings, then MCP. VS Code with Copilot reads .vscode/mcp.json. Restart the client; six tools appear. No API key, no account. Per-client walkthroughs live in the setup guide.

Family

Built by ComparEdge, where software prices are checked against vendor pages before anyone quotes them. Two siblings share the data: the full catalog server and a price-change watcher, both on ComparEdge MCP.

MIT licensed. JSON-RPC 2.0 over stdio, standard Model Context Protocol.