Models

Pass a model id as model. GET /v1/models returns this same list at runtime and needs no API key.

Prices are per million tokens. Some open models with strong African-language performance carry non-commercial licences and cannot be served by any provider. They are absent from this list rather than silently swapped for a weaker model.

Capabilities are measured, not claimed

Can do above comes from a probe that sends a real tool call and a real json_schema request to every model and records what came back. It is not copied from a supplier’s catalogue tags.

That matters because those tags are wrong. One 70B model is tagged for structured output by its provider and ignores response_format outright: four consecutive strict: true requests returned conversational prose, and json_object mode returned JSON wrapped in code fences, which JSON.parse also rejects. We declare only what we measured.

Asking a model for a capability it does not declare is a 400 naming the parameter, before any billable work. An unmeasured capability counts as absent, so you always get a clear error rather than a plausible wrong answer.

Aliases

An alias resolves to whichever model is currently best for a job. You opt in by naming it.

Where an alias points changes as better models appear. Your code does not. The usage record keeps both ids — what you asked for and what served it — so a bill from last month stays explainable after an alias repoints.

Models that reason first

Some models think before answering, and the thinking is billed even though it is never returned. Two consequences that catch people out:

• A better tokenizer does not pay for itself on short answers. One model needs a quarter of another’s tokens for Amharic and still costs more for a one-line reply, because the reasoning dwarfs the saving. Compare per request for your workload, not per token.
• A small max_tokens can buy silence. The budget goes on reasoning and you get an empty string and a bill. We raise the ceiling for these models rather than hand you nothing, and you are billed on tokens actually produced, so a request that finishes early costs what it costs.

Use reasoning_effort to control it directly:

client.chat.completions.create(
    model="glm-4.7",
    messages=[{"role": "user", "content": "What is 2+2?"}],
    reasoning_effort="none",
)

Licences

Some open models with strong African-language performance carry non-commercial licences and cannot be served by anyone, us included. They are absent from the list rather than silently replaced with a weaker model. Asking for one is a 403 that names the licence, not a 404: pretending a model does not exist would be a lie about something we deliberately refuse to sell.

Retrieving one model

curl https://api.dawnstackai.com/v1/models/llama-3.1-8b

client.models.retrieve("llama-3.1-8b")

Aliases resolve here too, and the response carries alias_for and the rationale when you ask for one.