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GPT-6 Luna vs GPT-5.6 Luna
A straight cost comparison of GPT-6 Luna (OpenAI) and GPT-5.6 Luna (OpenAI), per 1,000,000 tokens, verified October 2026.
Rate-by-rate comparison
| Metric | GPT-6 Luna | GPT-5.6 Luna | Winner |
|---|---|---|---|
| Input / 1M | $0.10 | $0.20 | GPT-6 Luna |
| Cached input / 1M | $0.01 | $0.02 | GPT-6 Luna |
| Cache write / 1M | $0.125 | $0.25 | GPT-6 Luna |
| Output / 1M | $0.50 | $1.20 | GPT-6 Luna |
| Context window | 1,050,000 | 1,050,000 | Tie |
| Batch API | Yes | Yes | Tie |
| Long-context tier | > 272,000 | > 272,000 | — |
For per-token metrics, the lower number wins. For context window and batch support, the higher or broader option wins. A dash means the provider does not publish that rate: a missing cache-write fee means writes are not billed per token, while a missing cached-input rate means cache hits bill at the full input rate.
Blended cost: 1M input + 250K output
| Model | Blended cost | Difference |
|---|---|---|
| GPT-6 Luna | $0.225 | Cheaper |
| GPT-5.6 Luna | $0.50 | +$0.275 |
Three workloads, two bills
| Workload | GPT-6 Luna | GPT-5.6 Luna | Winner |
|---|---|---|---|
| Support chat 1,000 requests · 4K input / 600 output | $0.70 | $1.52 | GPT-6 Luna |
| RAG with caching 1,000 requests · 50K input (45K cached) / 1K output | $1.46 | $3.11 | GPT-6 Luna |
| Nightly batch job 10,000 requests · 10K input / 2K output | $10 | $22 | GPT-6 Luna |
Which one should you pick?
GPT-6 Luna wins on support chat, cached RAG, nightly batch job, while GPT-5.6 Luna wins on none of the three workloads above.
Beyond price, the two differ on input rate ($0.10 vs $0.20); output rate ($0.50 vs $1.20); cached-input rate ($0.01 vs $0.02); cache-write rate ($0.125 vs $0.25).
Batch mode is available on GPT-6 Luna and GPT-5.6 Luna, which halves the nightly job. Both publish cached-input discounts, so a stable system prompt is worth caching on either.
FAQ
Is GPT-6 Luna cheaper than GPT-5.6 Luna?
On a blended 1M-input-plus-250K-output workload, GPT-6 Luna is cheaper by 55% ($0.225 vs $0.50). The gap changes for cache-heavy prompts and for batch jobs, as the workload table shows.
Which handles longer prompts, GPT-6 Luna or GPT-5.6 Luna?
Both models share the same 1,050,000-token context window. Watch the long-context threshold on GPT-6 Luna and GPT-5.6 Luna: rates rise once input passes it.
Do both models support prompt caching?
GPT-6 Luna bills cached reads at $0.01 per 1M; GPT-5.6 Luna at $0.02.