GitHub - axium-lab/llm-specs-api: Free REST API for LLM pricing and context windows. Cost estimation with auditable breakdown.

Every LLM's price, context window and capabilities behind one REST API — and a cost estimator that shows its work.

Picking a model means checking four things at once: what it costs, how much context it takes, what it can do, and whether it is about to be deprecated. That lives in a 1.7 MB JSON file you have to parse yourself. This service reads it once at boot and serves 3,214 models from 127 providers out of memory, with no database — filtered, sorted, compared — and prices a single call down to the exact rate key it used.

curl https://api-llm-specs.axium-lab.com/v1/models/claude-sonnet-4-5

Ask for a model and you get everything the catalog knows about it — every rate, including the ones that only apply past a threshold or to a cache write, plus the context window and the capability flags:

Or ask a question of the whole catalog — a provider, a mode, a price ceiling, a context window — and project just the fields you care about:

curl 'https://api-llm-specs.axium-lab.com/v1/models?provider=anthropic&mode=chat&sort=input_cost_per_token:desc&fields=id,input_cost_per_token,output_cost_per_token,max_input_tokens&limit=3'

{
  "total": 26,
  "limit": 3,
  "offset": 0,
  "data": [
    { "id": "claude-3-opus-20240229", "input_cost_per_token": 0.000015, "output_cost_per_token": 0.000075, "max_input_tokens": 200000 },
    { "id": "claude-4-opus-20250514", "input_cost_per_token": 0.000015, "output_cost_per_token": 0.000075, "max_input_tokens": 200000 },
    { "id": "claude-opus-4-1",        "input_cost_per_token": 0.000015, "output_cost_per_token": 0.000075, "max_input_tokens": 200000 }
  ]
}

total is the size of the filtered set before the page is cut, and 126 more providers' worth of models are one provider= away. Every filter, sort and projection →

And what a call actually costs

Rates are one thing; knowing which of those four Anthropic cache keys applies to your request is another. That is what POST /v1/estimate is for — and it answers with a receipt, not a number. Every line carries rate_key, the literal key it was billed with:

input.text         190000 x 0.000006   = 1.14    [input_cost_per_token_above_200k_tokens]
output.text          4000 x 0.0000225  = 0.09    [output_cost_per_token_above_200k_tokens]
cache_read.text     20000 x 6e-7       = 0.012   [cache_read_input_token_cost_above_200k_tokens]
cache_write.1h       8000 x 0.000012   = 0.096   [cache_creation_input_token_cost_above_1hr_above_200k_tokens]
                                        ------
                                         1.338 USD

That prompt crossed the 200k threshold, so the whole request was repriced — output included — and the 1 hour cache TTL composed with it into that quadruple key. Both are decisions the estimator makes explicitly and reports back in resolution. The estimator in full →

⚠️ Hosted instance — not live yet. https://api-llm-specs.axium-lab.com is the address the free instance will answer on; it is not deployed at the time of writing. Until then, run it locally or deploy your own — it is one docker run away, and the dataset ships inside the image.

📖 Full API reference at llm-specs.axium-lab.com — every endpoint, parameter, response shape and error, with the numbers taken from real responses.

Why

• Auditable, not magic. Every cost line names the dataset key it used (rate_key) and the quantity it multiplied. A total you cannot check against the source is a total you cannot trust.
• Decimal arithmetic, never number. The catalog ships floating point noise already serialized (1.2999000000000001e-07) and rates as small as 1.3e-10. Everything accumulates in Decimal.
• A missing rate is never billed as 0. 258 models legitimately declare output_cost_per_token: 0, so "free" and "unknown" have to stay distinguishable: unknown usage goes to unpriced[] and never inflates or deflates the total silently.
• It boots without network. The dataset is versioned in the repo and baked into the image. At boot the instance revalidates it upstream with If-None-Match; on a 304 it transfers 0 bytes, and if upstream is down it serves the local copy and says so in /health.
• Errors you can branch on. Every failure is RFC 9457 application/problem+json with a stable type slug — model-not-found, ambiguous-model, model-not-priced, limits-exceeded, invalid-query.

Quick start

Requires Bun.

git clone https://github.com/axium-lab/llm-specs-api.git
cd llm-specs-api
bun install
bun start          # http://localhost:8080

Or straight from the published image — nothing to build, and the dataset is already inside:

docker run --rm -p 8080:8080 ghcr.io/axium-lab/llm-specs-api:latest

To build it yourself instead:

docker build -t llm-specs-api .
docker run --rm -p 8080:8080 llm-specs-api

Then ask it something:

# The cheapest chat models with a 1M context window
curl 'localhost:8080/v1/models?mode=chat&min_input_tokens=1000000&sort=input_cost_per_token:asc&fields=id,provider,input_cost_per_token&limit=5'

# Ids containing / and : need no escaping — the lookup route takes them literally
curl 'localhost:8080/v1/models/bedrock/us.anthropic.claude-3-5-haiku-20241022-v1:0'

Endpoints

Filters for /v1/models: provider, mode, q, any of the 37 supports_* keys, min_input_tokens, max_input_cost, sort=field:asc|desc, fields, limit, offset. Full parameter reference →

Configuration

Everything is optional — the service runs out of the box.

Positive integers only — a malformed value fails the boot instead of being silently ignored.

Deploy with Docker Compose

Every release ships a docker-compose.yml with the image pinned to that exact version:

curl -LO https://github.com/axium-lab/llm-specs-api/releases/latest/download/docker-compose.yml
docker compose up -d
curl localhost:8080/health

Images are published for linux/amd64 and linux/arm64 at ghcr.io/axium-lab/llm-specs-api, tagged X.Y.Z, X.Y and latest. A pre-release is never tagged latest.

The compose file declares a named volume over /app/data, and it matters: the dataset travels inside the image and the service rewrites it when upstream has something newer, so without the volume every restart throws that update away and starts again from the copy baked in at build time. It has to be a named volume — Docker seeds one from the image on first use, whereas an empty bind mount would hide the dataset and leave the service with nothing to serve.

Deploying to Cloud Run

gcloud run deploy llm-specs-api --source . --region europe-west1 \
  --min-instances 1 --cpu-boost --allow-unauthenticated

--min-instances 1 is a latency choice, not a correctness one: a cold start revalidates the baked-in copy with If-None-Match and transfers nothing on a 304, and a boot while GitHub is down serves the local dataset instead of failing. Keep it if you care about cold-start latency, drop it if you care about idle cost.

If you mount a Cloud Storage bucket, do not mount it over data/: GCS FUSE hides whatever sits below the mount point, just like any Linux mount, and you would lose the file baked into the image. Use a separate path (/mnt/dataset) and point DATASET_PATH at it.

Known limitations

The estimator is honest about what it does not price. These are accepted inputs or dataset features that currently produce no cost line, and they are worth knowing before you trust a total:

Data source

The catalog is LiteLLM's model_prices_and_context_window.json, MIT licensed, vendored into data/ and redistributed normalized: src/data/parse.ts renames litellm_provider to provider on the way in, and that is the only place where upstream's shape is adjusted. The prices are the ones LiteLLM publishes — this project does not source, correct or negotiate them, and flags the implausible ones rather than fixing them. How the dataset is loaded →

Development

bun run dev        # start with file watching
bun test           # HTTP surface, pricing engine, dataset resolution, catalog completeness
bun run typecheck  # tsc --noEmit

Stack: Bun + TypeScript + Express 5. No build step, no database, no state: src/data/ owns the dataset, src/pricing/ owns the cost engine, src/routes/ owns the HTTP surface.

Contributing

Issues and PRs are welcome — see CONTRIBUTING.md. The one hard rule: every endpoint and every pricing rule keeps a test that proves it.

License

MIT