GPT-5.6 Terra
GPT-5.6 Terra by OpenAI: $2 input and $12 output per 1M tokens, 400K context. Call it through the odnoga LLM gateway.
GPT-5.6 Terra is served by OpenAI and called through odnoga with the same OpenAI-compatible request shape as every other model in the catalog. 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 400K-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 | 400K tokens |
| Max output | 128K tokens |
| Capabilities | Reasoning, Vision, Tools, JSON mode, Streaming |
| 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" }],
}),
});
Same request shape for every vendor in the catalog — swap the model id and odnoga handles keys, routing, limits and cost accounting.
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.
GPT-5.6 Terra compared
| Model | Context | Input / 1M | Output / 1M | Capabilities |
|---|---|---|---|---|
| GPT-5.6 Terra | 400K | $2 | $12 | Reasoning, Vision, Tools, JSON mode, Streaming |
| ChatGPT (chat-latest) | 400K | $5 | $30 | Vision, Tools, JSON mode, Streaming |
| GPT Image 1 mini | — | $2 | $8 | Vision |
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