OpenAI: GPT-5.6 Luna Pro (batch) API Pricing
OpenAI: GPT-5.6 Luna Pro (batch) costs $0.100 per 1M input tokens and $0.600 per 1M output tokens (cache reads: $0.010). Context window: 1.1M. Prices verified 2026-10-21 · source
Alternatives to consider
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Rebuilt daily from our snapshot archive — the chart appears once a model has 5+ snapshots of history.
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Token economics
| Token type | Price | vs base input |
|---|---|---|
| Standard input | $0.100 | — |
| Cache read | $0.010 | −90% |
| Output | $0.600 | — |
| Web search tool | $10/1k searches | — |
Prompt caching stores repeated context server-side: reads cost far less, but writes carry a one-time premium.
Higher effort = deeper thinking = more reasoning tokens billed as output.
Training data ends here — newer events require retrieval or web search.
What would OpenAI: GPT-5.6 Luna Pro (batch) cost you per month?
| Workload | Requests/mo | Avg tokens (in/out) | Est. monthly cost |
|---|---|---|---|
| Light chatbot | 10,000 | 800 / 400 | $3.20 |
| Coding agent | 5,000 | 8,000 / 2,000 | $10.00 |
| RAG pipeline | 30,000 | 4,000 / 500 | $21.00 |
| Doc summarizer | 2,000 | 20,000 / 1,000 | $5.20 |
Estimates use list prices without prompt caching. Caching can cut input cost by up to 90% on repeated context.
About OpenAI: GPT-5.6 Luna Pro (batch)
GPT-5.6 Luna Pro is the same underlying model as [GPT-5.6 Luna](https://openrouter.ai/openai/gpt-5.6-luna), served with `reasoning.mode` set to `pro` for higher-quality responses on complex tasks. **Cost note:** pro mode spends far more...
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Frequently asked questions
How much does OpenAI: GPT-5.6 Luna Pro (batch) cost?
OpenAI: GPT-5.6 Luna Pro (batch) costs $0.100 per 1M input tokens and $0.600 per 1M output tokens; cached input tokens cost $0.010 per 1M. Verified 2026-10-21 — provider source.
What is the context window?
1.1M tokens, with up to 128K output tokens per response. That's roughly 1313 pages of text in a single request.
Does it support prompt caching?
Yes — cached input is $0.010/1M, about 90% cheaper than standard input. Agents and RAG workloads with repeated context benefit most.
What's a cheaper alternative?
ScaleDown: Summarize saves about 88% on a typical workload with comparable quality.
Official resources
- OpenAI: GPT-5.6 Luna Pro (batch) on OpenRouter — live endpoint, pricing source of record.
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