Free REST API for LLM pricing, context windows and cost estimation

Axium Lab has released a free REST API that serves pricing, context windows, and capability data for 3,214 LLM models from 127 providers, along with a cost estimator that itemizes each rate key used. The API reads a 1.7 MB JSON file at boot and serves filtered, sorted, and projected model data from memory without a database, with endpoints for model details and cost estimation. The hosted instance at https://api-llm-specs.axium-lab.com is not yet live, but the service can be run locally via Docker.

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: { // 29 attributes in total, trimmed here "deprecation date": "2026-09-29", "cache creation input token cost": 0.00000375, "cache creation input token cost above 1hr": 0.000006, "cache read input token cost": 3e-7, "input cost per token": 0.000003, "input cost per token above 200k tokens": 0.000006, "output cost per token above 200k tokens": 0.0000225, "provider": "anthropic", "max input tokens": 200000, "max output tokens": 64000, "mode": "chat", "output cost per token": 0.000015, "supports prompt caching": true, "supports vision": true, "prompt cache min tokens": 1024, "id": "claude-sonnet-4-5" } 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 → https://llm-specs.axium-lab.com/api-models.html 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 → https://llm-specs.axium-lab.com/api-estimate.html ⚠️ 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. 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 The catalog ships floating point noise already serialized number . 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 . Requires Bun https://bun.sh . 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' | Method | Path | Description | |---|---|---| GET | /health | startup error . GET /v1/models GET /v1/models/ / , : and . GET /v1/models/by-id?id= GET /v1/compare?ids=a,b,c GET /v1/providers GET /v1/modes GET /v1/attributes GET /v1/meta POST /v1/estimate 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 → https://llm-specs.axium-lab.com/api-models.html Everything is optional — the service runs out of the box. | Environment variable | Default | Description | |---|---|---| PORT | 8080 | Injected by Cloud Run. | DATASET PATH | data/model prices and context window.json | The source of truth. The sidecar path is derived from it. | UPSTREAM URL | LiteLLM's litellm internal staging branch | Revalidation target. See | FETCH TIMEOUT MS 30000 DEFAULT LIMIT 50 /v1/models . MAX LIMIT 500 limit ; a larger value is clamped, not rejected.Positive integers only — a malformed value fails the boot instead of being silently ignored. Every release https://github.com/axium-lab/llm-specs-api/releases 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 https://github.com/axium-lab/llm-specs-api/pkgs/container/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. 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. 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: | Limitation | Effect | |---|---| usage.web search and usage.search results | Accepted and validated, but never billed. search context cost per query 282 models is declared in the catalog and never read. They do not even show up in unpriced . | options.tier policy: "marginal" | Does not compute per-band pricing. It falls through to the base rate, so the result is the same as a request that never crossed a threshold. | tiered pricing | Not read. The 21 models that price only through it answer 200 with a total of 0 and all usage in unpriced . | | Databricks DBU rates | The input dbu cost per token / output dbu cost per token keys 47 models are described in the catalog but no usage field maps to them, so a DBU total is never produced. | | Malformed JSON, or a body over 256 kB | Answers 500 internal-error instead of 400 / 413 . | The catalog is LiteLLM's model prices and context window.json https://github.com/BerriAI/litellm , 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 https://llm-specs.axium-lab.com/dataset.html suspicious rather than fixing them. How the dataset is loaded → https://llm-specs.axium-lab.com/dataset.html bun run dev start with file watching bun test HTTP surface, pricing engine, dataset resolution, catalog completeness bun run typecheck tsc --noEmit Stack: Bun https://bun.sh + TypeScript + Express 5 https://expressjs.com . No build step, no database, no state: src/data/ owns the dataset, src/pricing/ owns the cost engine, src/routes/ owns the HTTP surface. Issues and PRs are welcome — see CONTRIBUTING.md /axium-lab/llm-specs-api/blob/main/CONTRIBUTING.md . The one hard rule: every endpoint and every pricing rule keeps a test that proves it.