GPT-5.6 Luna
GPT-5.6 Luna by OpenAI: $0.2 input and $1.20 output per 1M tokens, 400K context. Call it through the odnoga LLM gateway.
GPT-5.6 Luna 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 $0.02 per 1M tokens, so repeated prefixes cost less.
Specification and price
| Vendor | OpenAI |
|---|---|
| Model ID | gpt-5.6-luna |
| 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 | $0.2 | $0.215 |
| Output | $1.20 | $1.29 |
| Cached input | $0.02 | $0.022 |
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-luna",
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 Luna
Reviewed 2026-09-14
Best for
- Cost-sensitive, high-volume workloads — the tier OpenAI positions exactly there.
- Classification, tagging, routing, moderation pre-checks and short rewrites measured in millions of calls.
- Cheap first-pass work in a two-stage pipeline, with a stronger model handling only what Luna flags.
- Long-context reads on a tight budget: the window is the same 1,050,000 tokens as the rest of the family.
Not the right pick when
- Open-ended reasoning, architecture decisions or long agent runs — use Terra or Sol.
- Customer-visible writing where tone and nuance matter.
- Anything your evaluation shows it fails; cheap is only cheap when the answer is usable.
Practical tips through odnoga
- 01Give it one narrow job per prompt and enforce JSON output — small models are most reliable when the task is closed.
- 02Use it as the first stage and escalate on low confidence; odnoga records the cost of both stages per request so you can see whether the split actually pays.
- 03Batch similar calls behind one managed prompt so a change is one version bump instead of scattered edits.
- 04Watch the per-tenant budget: high volume is exactly where a runaway loop becomes expensive.
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) | $2.15 |
|---|---|
| Output (2M tokens) | $2.58 |
| Your cost per month on Free | $4.73 |
Vendor list cost $4.40 + odnoga margin $0.331 (+7%). Cached input or answer reuse lowers it; odnoga records both numbers per request.
GPT-5.6 Luna compared
| Model | Context | Input / 1M | Output / 1M | Capabilities |
|---|---|---|---|---|
| GPT-5.6 Luna | 400K | $0.2 | $1.20 | 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
- What is GPT-5.6 Luna for?
- OpenAI positions Luna for cost-sensitive, high-volume workloads — the nano tier of the GPT-5.6 family, with the same context window and reasoning-effort range as its larger siblings.
- Is Luna accurate enough?
- For closed tasks with a clear output schema it often is. Prove it on your own frozen test cases in the evaluation laboratory rather than assuming; the matrix shows exactly which cases fail.
- How much can it save?
- Compare the worked monthly cost on this page with the same volume on Sol. odnoga records the real per-request cost afterwards, so the saving you model is the one you can audit.
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