GPT-5.6 Terra
GPT-5.6 Terra by OpenAI: $2 per 1M input and $12 per 1M output tokens, 1.1M context. Call it with one OpenAI-compatible API key, compare it with similar models, start free.
GPT-5.6 Terra 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 $0.2 per 1M tokens, so repeated prefixes cost less.
Specification and price
| Vendor | OpenAI |
|---|---|
| Model ID | gpt-5.6-terra |
| 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 | $2 | $2.15 |
| Output | $12 | $12.90 |
| Cached input | $0.2 | $0.215 |
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-5.6-terra",
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-5.6 Terra 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-5.6 Terra
Reviewed 2026-09-14
Best for
- Workloads that need real intelligence but have to stay cheap: support answers, document understanding, code review comments, structured extraction at scale.
- The default production tier when Sol passes your evaluation but costs more than the task justifies.
- Long-context work on a budget — the same 1,050,000-token window as the flagship tier.
- Tool-using workflows where each step is well scoped.
Not the right pick when
- The hardest reasoning in your product — measure Sol on the same cases before settling.
- Trivial, very high-frequency calls where Luna is an order of magnitude cheaper.
- Tasks your evaluation shows fail here but pass on the flagship tier: pick per prompt, not per company.
Practical tips through odnoga
- 01Use Terra as the default and escalate only the prompts that fail evaluation on it — that is where most of the saving in a multi-model setup comes from.
- 02Set a fallback to Sol on the same route so a hard request degrades to more capability instead of a bad answer.
- 03Keep reasoning effort low unless a frozen test case proves higher effort changes the result.
- 04Turn on odnoga answer reuse for deterministic calls: repeated identical requests return the stored answer with no vendor tokens billed.
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) | $21.51 |
|---|---|
| Output (2M tokens) | $25.81 |
| Your cost per month on Free | $47.31 |
Vendor list cost $44.00 + odnoga margin $3.31 (+7%). Cached input or answer reuse lowers it; odnoga records both numbers per request.
If GPT-5.6 Terra 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-5.6 Terra | 1.1M | $2 | $12 | Reasoning, Reads images, Tool calling, JSON schema, Streaming, Prompt caching, Batch |
| gpt-6-sol | 1.1M | $2 | $10 | Reasoning, Reads images, Tool calling, JSON schema, Streaming, Prompt caching, Batch |
| Claude Sonnet 5 | 1M | $2 | $10 | Reasoning, Reads images, Tool calling, JSON schema, Streaming, Web search, Prompt caching, Batch |
| Claude Sonnet 5.5 | 1M | $2 | $10 | Reasoning, Reads images, Tool calling, JSON schema, Streaming, Web search, Prompt caching, Batch |
Questions
- How does GPT-5.6 Terra differ from Sol?
- Terra is the balanced tier of the GPT-5.6 family — OpenAI describes it as designed for workloads that balance intelligence and cost, with the same 1,050,000-token context window and the same reasoning-effort range as Sol, at a lower price.
- Is Terra good enough for production?
- For many prompts, yes — but decide it with evidence, not a blog post. Freeze your test cases in the odnoga evaluation laboratory and compare Terra against Sol and Luna on pass rate, tokens, latency and cost.
- Can I switch between tiers without a deploy?
- Yes. Pin the model in the managed prompt version and switch the production label — the application keeps calling the same OpenAI-compatible endpoint.
All models · Pricing · Docs · Compare models in the evaluation lab
Sources: OpenAI — GPT-5.6 Terra model page, OpenAI — choosing a model