llm-prices

Up-to-date LLM pricing reference and cost calculator. Claude, GPT, Gemini, DeepSeek, Mistral, Grok — input/output/cache prices per 1M tokens, context windows, and a cost calculator. Zero dependencies, works as a library and as a CLI.

Prices are regenerated from LiteLLM's canonical pricing dataset — the same source used across the industry.

Quick start (CLI, no install)

npx llm-prices claude-opus-4-8 --in 200k --out 8k

MODEL            PROVIDER   IN/1M  OUT/1M  CACHE-RD  CTX
---------------  ---------  -----  ------  --------  ---
claude-opus-4-8  anthropic  $5     $25     $0.5      1M

USAGE   TOKENS  COST
------  ------  -----
input   200K    $1
output  8K      $0.2
TOTAL           $1.2

More commands:

npx llm-prices ls anthropic            # all Anthropic models
npx llm-prices ls                      # every model in the dataset
npx llm-prices compare claude-sonnet-5 gpt-5.2 gemini-3.1-pro-preview
npx llm-prices providers

Token counts accept k/m suffixes: 500, 100k, 1.5m.

Library

npm install llm-prices

import { getPrice, calcCost, listModels } from "llm-prices";

getPrice("claude-opus-4-8");
// { id: 'claude-opus-4-8', input: 5, output: 25, cacheRead: 0.5, cacheWrite: 6.25 }
// (USD per 1M tokens)

calcCost("gpt-5.2", { input: 120_000, output: 4_000 });
// { input: ..., output: ..., cacheRead: 0, cacheWrite: 0, total: ... }  (USD)

listModels({ provider: "anthropic" });
// [{ id, provider, input, output, cacheRead, cacheWrite, context, maxOutput }, ...]

CommonJS works too:

const { calcCost } = require("llm-prices");

Fuzzy lookup: getModel("fable") resolves to claude-fable-5 when the substring is unambiguous.

Raw data

The whole dataset is a single small JSON you can import directly:

import prices from "llm-prices/data" with { type: "json" };

API

| Function | Returns | |---|---| | getModel(id) | Full entry: prices, cache prices, context window, max output | | getPrice(id) | { input, output, cacheRead, cacheWrite } in USD per 1M tokens | | calcCost(id, { input, output, cacheRead, cacheWrite }) | Cost breakdown + total in USD for given token counts | | listModels({ provider? }) | All entries, optionally filtered | | listProviders() | Provider names in the dataset | | compare(ids) | Entries for several models at once | | updated | ISO date the data was last regenerated |

TypeScript definitions included.

Use in CI

Print the cost of your test-suite's LLM calls on every pipeline run — no install step, npx fetches the CLI on the fly:

# .github/workflows/llm-cost.yml
name: LLM cost report
on: [pull_request]

jobs:
  cost:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/setup-node@v4
        with: { node-version: 20 }
      # replace the token counts with your run's actuals
      - run: npx -y llm-prices claude-opus-4-8 --in 250k --out 12k >> "$GITHUB_STEP_SUMMARY"

Or budget-gate a script programmatically:

import { calcCost } from "llm-prices";

const cost = calcCost(process.env.MODEL, { input: usedIn, output: usedOut });
if (cost.total > 5) throw new Error(`LLM budget exceeded: $${cost.total.toFixed(2)}`);

Keeping prices fresh

The dataset ships inside the package and is refreshed with regular releases. To regenerate locally from the live source:

npm run update-prices

If you spot a stale price, open an issue — updates ship fast.

Why not parse provider docs or LiteLLM's 1.6 MB JSON yourself?

• Small: the curated dataset is ~30 KB vs 1.6 MB upstream.
• Zero dependencies: nothing but Node ≥ 18.
• Both worlds: one package for scripts (npx), backends (import), and dashboards (raw JSON).

License

MIT © Cryptoteep