OpenAI · language model
GPT-5.6 Luna Pro: pricing, context and benchmarks
GPT-5.6 Luna Pro is OpenAI's model released on July 9, 2026. It costs $0.2 per million input tokens and $1.2 per million output tokens, with a 1.1M-token context window. No independent score has been published for it so far.
| Price | 18/30 | |
|---|---|---|
| Context | 15/15 | |
| Features | 11/15 |
What you need to know about GPT-5.6 Luna Pro
OpenAI, based in United States, put GPT-5.6 Luna Pro online on July 9, 2026. It accepts files, images, and text as input and produces text. Its context window reaches 1,050,000 tokens, larger than 99 % of the models in the registry, and it can write up to 128,000 tokens in one go.
It has been available for about 4 months, long enough for its prices and scores to have settled.
GPT-5.6 Luna Pro pricing
On OpenRouter, GPT-5.6 Luna Pro costs $0.2 per million input tokens and $1.2 per million output tokens. Counting three tokens read for every token written, its average price is $0.45 per million, more expensive than 39 % of paid models (1.7 times cheaper than the median of $0.76).
A concrete example: a task that sends 2,000 tokens (about 1,500 words) and receives a 500-token answer costs $0.001. At 1,000 tasks a day, budget $30 a month. The price calculator redoes this with your own volumes.
Tokens already sent and cached are billed at $0.02 per million. With batch processing (delayed answers), the price drops to $0.1 input and $0.6 output.
Performance and benchmarks
Epoch AI does not yet publish a capabilities index for GPT-5.6 Luna Pro. We therefore show no made-up performance rating: only price, context and features count towards its score.
Features, privacy and local use
- It supports tool calling, so it can drive an AI agent, call APIs or MCP servers.
- It can answer in an enforced JSON format (structured outputs).
- It is a reasoning model: it "thinks" before answering, which helps on hard tasks but uses more output tokens.
- Its knowledge stops at February 2026.
Its weights are not published: it can only be used through the OpenAI API or a broker such as OpenRouter, which means sending your data off your machines.
Other OpenAI models
| Model | Released | Avg. price | ECI |
|---|---|---|---|
| GPT-6.1 Sol | Sep 29, 2026 | $4 | 168.4 |
| GPT-6.1 Sol Pro | Sep 29, 2026 | $4 | — |
| GPT-6 Sol | Sep 22, 2026 | $4 | 163.7 |
| GPT-6 Luna | Sep 22, 2026 | $0.2 | 156.8 |
| GPT-6 Luna Pro | Sep 22, 2026 | $0.2 | — |
| GPT-6 Sol Pro | Sep 22, 2026 | $4 | — |
| GPT-6 Astra | Sep 4, 2026 | $20 | 168.8 |
| GPT-6 Astra Pro | Sep 4, 2026 | $20 | — |
Verdict
Computed score: 73/100, from public data (performance, price, context, features).
Strengths
- Very large context (1.1M tokens): long documents or a whole codebase fit in one request.
- Agent-ready: tool calling and structured JSON output.
- Discounted batch pricing.
Limitations
- No independent score published: performance cannot be verified.
Avoid if…
- your data must not leave your servers.
Alternatives
- MiMo-V2.6-Pro$0.54
- Beam 1.0 501B A23B$0.44
- MiMo-V2.5-Pro$0.38
- MiniMax M3$0.53
- LongCat 2.0$0.53
- GLM 5.3$0.41
Comparisons and tools
Learn more
Frequently asked questions
How much does GPT-5.6 Luna Pro cost?
$0.2 per million input tokens and $1.2 per million output tokens on OpenRouter (checked October 25, 2026). A task with 2,000 tokens read and 500 written costs $0.001.
How big is GPT-5.6 Luna Pro's context window?
1,050,000 tokens, roughly 788,000 words, with a maximum of 128,000 output tokens.
Can GPT-5.6 Luna Pro run locally?
No, OpenAI does not publish its weights: it is API-only.
Is GPT-5.6 Luna Pro suitable for an AI agent?
Technically yes: it supports tool calling and structured outputs. Then check its cost for your volume.
What are the alternatives to GPT-5.6 Luna Pro?
The closest models on price and performance are MiMo-V2.6-Pro, Beam 1.0 501B A23B, and MiMo-V2.5-Pro.
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Neutrality. No affiliate links. No vendor pays to be reviewed, rated or ranked. Reviews are directed by François Chollet, neutral and factual, and every data point is dated and sourced. How we score