GPT-6 Astra
GPT-6 Astra by OpenAI: $10 input and $50 output per 1M tokens, 1.1M context. Call it through the odnoga LLM gateway.
GPT-6 Astra is served by OpenAI and called through odnoga on /v1/chat/completions, in the same OpenAI-compatible shape every other model on that endpoint uses. It is a reasoning model, so it spends extra output tokens working through a problem before answering — budget for higher output cost on hard tasks. It accepts images alongside text. It supports tool and function calling. It can be forced to return structured JSON. Its 1.1M-token context window sets how much input you can send in one request. Cached input is billed at $1 per 1M tokens, so repeated prefixes cost less.
Specification and price
| Vendor | OpenAI |
|---|---|
| Model ID | gpt-6-astra |
| Context window | 1.1M tokens |
| Max output | 128K tokens |
| Served on | /v1/chat/completions |
| Capabilities | Reasoning, Reads images, Tool calling, JSON schema, Streaming, Prompt caching, Batch |
| Per 1M tokens | Vendor cost | Your price on Free+7% |
|---|---|---|
| Input | $10 | $10.75 |
| Output | $50 | $53.76 |
| Cached input | $1 | $1.08 |
Vendor cost is the list price per million tokens as recorded in the odnoga catalog; your price applies your plan margin with the same formula that bills every request — see pricing. Pricing
Call it through odnoga
const res = await fetch("https://api.odnoga.com/v1/chat/completions", {
method: "POST",
headers: {
Authorization: `Bearer ${process.env.ODNOGA_API_KEY}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
model: "gpt-6-astra",
messages: [{ role: "user", content: "Hello" }],
}),
});
One endpoint, one key, one bill — /v1/chat/completions takes the same shape whichever vendor is behind it. Swap the model id and odnoga handles keys, routing, limits and cost accounting.
What GPT-6 Astra can do
Everything below is what the model itself supports. Ask for something it does not do and odnoga tells you before the request reaches the provider, instead of returning a worse answer.
Works through a problem step by step before answering, which costs more tokens and buys accuracy on hard tasks.
Send a screenshot, a photo or a scan in the same message as your text.
The model can call functions you define and use what they return.
Answers with valid JSON instead of prose you would have to parse.
Answers in the exact shape you specify, so the response fits your types without a fallback path.
Tokens arrive as they are produced, so your interface can answer immediately.
A prompt you send repeatedly is charged at the cached rate, which is a fraction of the input price.
Work that can wait goes through at a lower price.
Finer control
- Structured outputs · The provider guarantees the schema rather than merely aiming for it.
- Parallel tools · Can call several of your functions in one turn instead of one after another.
- The provider requires a verified organisation before it will serve this model.
How to use GPT-6 Astra
Derived from the odnoga catalog record for this model.
Best for
- Multi-step problems where the answer has to be worked out: planning, debugging, data reconciliation, analysis with intermediate steps.
- Work that mixes images with text — screenshots, scanned documents, charts, product photos.
- Agents and workflows that call your own functions, because the model supports tool calling.
- Machine-readable output you can write straight into a database, using enforced JSON.
- Long inputs: a 1.1M-token window fits whole contracts, codebases or transcripts in one request.
- User-facing chat where partial output should appear while the model is still writing.
Not the right pick when
- Simple, high-frequency calls — reasoning spends extra output tokens, so a non-reasoning model in the same catalog is usually cheaper and faster.
- Anything where a wrong answer is costly without a human check — no model in the catalog removes that requirement.
Practical tips through odnoga
- 01Pin the model id in a managed prompt version, so a model swap is a version change you can compare and roll back, not an edit in application code.
- 02Compare it against 2–8 other models on the same frozen test cases in the evaluation laboratory before you make it the production default.
- 03Keep the stable part of your prompt at the front: cached input is billed at $1 per 1M tokens instead of $10.
- 04For repeated identical deterministic calls, odnoga answer reuse returns the stored answer and bills no vendor tokens — turn it off for creative output.
- 05Ask for JSON through the response format rather than in the prompt text — the schema is enforced instead of suggested.
- 06Budget for output tokens: reasoning happens on the output side, so a short answer can still be an expensive call.
- 07Set a fallback model on the route so a vendor incident degrades quality instead of returning an error, and a per-tenant budget so one caller cannot spend the month.
What a month costs
1,000 calls a month, 10,000 input tokens and 2,000 output tokens each, at your Free price (vendor cost +7%):
| Input (10M tokens) | $108 |
|---|---|
| Output (2M tokens) | $108 |
| Your cost per month on Free | $215 |
Vendor list cost $200 + odnoga margin $15.05 (+7%). Cached input or answer reuse lowers it; odnoga records both numbers per request.
If GPT-6 Astra is not the one
Models that take the same kind of input, are priced the same way, and do the closest thing to what this one does. Not a quality ranking — odnoga does not benchmark models.
| Model | Context | Input / 1M | Output / 1M | Capabilities |
|---|---|---|---|---|
| GPT-6 Astra | 1.1M | $10 | $50 | Reasoning, Reads images, Tool calling, JSON schema, Streaming, Prompt caching, Batch |
| Claude Fable 5 | 1M | $10 | $50 | Reasoning, Reads images, Tool calling, JSON schema, Streaming, Web search, Prompt caching, Batch |
| Claude Fable 5.1 | 1M | $10 | $50 | Reasoning, Reads images, Tool calling, JSON schema, Streaming, Web search, Prompt caching, Batch |
| GPT-5.6 Sol | 1.1M | $4 | $20 | Reasoning, Reads images, Tool calling, JSON schema, Streaming, Prompt caching, Batch |
Questions
- How much does GPT-6 Astra cost per 1M tokens?
- OpenAI lists $10 / $50 per 1M input / output tokens in the odnoga catalog. Through odnoga you pay that vendor price plus your plan margin, and every request is recorded with both numbers.
- What is GPT-6 Astra best for?
- Multi-step problems where the answer has to be worked out: planning, debugging, data reconciliation, analysis with intermediate steps. Work that mixes images with text — screenshots, scanned documents, charts, product photos. Agents and workflows that call your own functions, because the model supports tool calling.
- Can I switch to GPT-6 Astra without changing my code?
- Yes. odnoga exposes one OpenAI-compatible endpoint, so switching means sending "gpt-6-astra" as the model id — or changing it in the managed prompt version, with no application deploy.
- How large is the GPT-6 Astra context window?
- 1.1M tokens of input, with up to 128K tokens of output per response.
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