GPT-5.6 Sol
GPT-5.6 Sol by OpenAI: $4 input and $20 output per 1M tokens, 400K context. Call it through the odnoga LLM gateway.
GPT-5.6 Sol 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 $0.4 per 1M tokens, so repeated prefixes cost less.
Specification and price
| Vendor | OpenAI |
|---|---|
| Model ID | gpt-5.6-sol |
| 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 | $4 | $4.30 |
| Output | $20 | $21.51 |
| Cached input | $0.4 | $0.43 |
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-sol",
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 Sol
Reviewed 2026-09-14
Best for
- Complex, multi-step work where the answer has to be reasoned out: engineering, research, analysis and cybersecurity tasks OpenAI positions this flagship tier for.
- Long inputs — the 1,050,000-token context window takes whole repositories, filings or case files in one request.
- Agentic workflows that plan, call tools and keep going across many steps.
- Work where you want to dial effort per request: reasoning effort runs from low to max, so the same model serves cheap drafts and hard problems.
Not the right pick when
- High-volume classification, extraction or routing — GPT-5.6 Terra and Luna sit in the same family at a fraction of the price.
- Latency-critical chat: reasoning adds output tokens and time before the first useful sentence.
- Budget-sensitive batch jobs where a smaller model already passes your evaluation.
Practical tips through odnoga
- 01Set reasoning effort per prompt version, not globally: low for drafts and routing, high or max only where an evaluation shows it changes the outcome.
- 02Put the stable instructions first so cached input is billed at the cache rate instead of full input price.
- 03Compare it head-to-head with GPT-5.6 Terra on your own frozen test cases in the odnoga evaluation laboratory — the cheaper tier often passes the same assertions.
- 04Give the model an explicit definition of done and the tools it may use; OpenAI prompting guidance for this family asks for well-scoped tasks rather than open-ended ones.
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) | $43.01 |
|---|---|
| Output (2M tokens) | $43.01 |
| Your cost per month on Free | $86.02 |
Vendor list cost $80.00 + odnoga margin $6.02 (+7%). Cached input or answer reuse lowers it; odnoga records both numbers per request.
GPT-5.6 Sol compared
| Model | Context | Input / 1M | Output / 1M | Capabilities |
|---|---|---|---|---|
| GPT-5.6 Sol | 400K | $4 | $20 | 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
- Is GPT-5.6 Sol the same as gpt-5.6?
- Yes — OpenAI routes the gpt-5.6 alias to GPT-5.6 Sol, the flagship tier of the GPT-5.6 family. Through odnoga you call it by the catalog model id.
- When is GPT-5.6 Terra the better choice?
- When the task is well defined and cost matters: Terra is the balanced tier of the same family at roughly half the input price. Run both on the same test cases in the evaluation laboratory and read the pass rate, tokens and cost side by side.
- How do I control what reasoning costs?
- Reasoning is billed as output tokens, so effort is the cost lever. Pin the effort in the managed prompt version, cap output length, and keep the reusable prefix at the front of the prompt so cached input applies.
All models · Pricing · Docs · Compare models in the evaluation lab
Sources: OpenAI — GPT-5.6 Sol model page, OpenAI — prompting guidance for GPT-5.6