Head to head
GPT-6 Luna vs Claude Haiku 5.5
GPT-6 Luna from OpenAI against Claude Haiku 5.5 from Anthropic — specification, price and every benchmark both makers have published, in one table.
At a glance
Benchmarks
No shared benchmarks
These two models have no benchmark in common with published figures for both, so there is nothing to compare directly. The specification and price rows below are still like for like.
Price
Same price
Both cost $0.20 per million tokens on a 3:1 input-to-output mix.
What actually differs
- GPT-6 Luna takes 1.1M tokens of context against 1M — 1.1× more room for long documents or a large codebase.
Scorecard
Which is better at what
Maths, coding, reasoning and the rest — one line each, averaged over the benchmarks both models actually report.
GPT-6 Luna has no published benchmark scores
Its maker has not released figures for any of the evaluations tracked here, so there is nothing to put in a score column. Rather than estimate, infer from a sibling model, or quote the base model's numbers as if they were its own, this page leaves those rows empty and compares what genuinely can be compared: parameters, context window, modalities, licence and cost.
The moment those figures are published they go in — send them with a link to the source.
| Category | GPT-6 Luna | Claude Haiku 5.5 | Better at this |
|---|---|---|---|
| ReasoningNeither model reports this | — | — | Not comparable |
| MathsNeither model reports this | — | — | Not comparable |
| CodingOnly one model reports this | — | — | Not comparable |
| KnowledgeNeither model reports this | — | — | Not comparable |
| MultimodalNeither model reports this | — | — | Not comparable |
| Instruction followingNeither model reports this | — | — | Not comparable |
| Human preferenceNeither model reports this | — | — | Not comparable |
| Categories won | No category has a test both models report | ||
Each category averages only the benchmarks every model here reports, so no one is credited for a test the other did not run. A category with no shared test is marked Not comparable rather than guessed at.
Side by side
GPT-6 Luna and Claude Haiku 5.5, row by row
| Attribute | GPT-6 LunaOpenAI | Claude Haiku 5.5Anthropic |
|---|---|---|
| Specification | ||
| MakerWho built it | OpenAI | Anthropic |
| Released | 2026-09 | 2026-10 |
| Context windowHow much can go in at once | 1,050,000 tokens | 1,000,000 tokens |
| Max output | Not reported | 128,000 tokens |
| Input | Text, Image | Text, Image |
| ReasoningSpends extra tokens thinking before it answers | Yes | Yes |
| Tool calling | Yes | Yes |
| Knowledge cutoff | Not reported | 2026-06 |
| Licence | Proprietary | Proprietary |
| Open weightsCan you download and run it yourself | No | No |
| Price | ||
| Input priceUSD per million tokens in | $0.10 | $0.10 |
| Output priceUSD per million tokens out | $0.50 | $0.50 |
| Cached input | $0.01 | Not reported |
| Blended 3:1A 3-in-to-1-out million-token mix — a fairer single number than input price alone | $0.20 | $0.20 |
| Published benchmarks | ||
| Terminal-Bench 4.0End-to-end tasks in a real terminal — install, build, debug, run — scored on whether the machine ends up in the required state. | Not reported | |
| Links | Full page | Full page |
| Row verified | 2026-10 | 2026-10 |
Where these numbers come from
Every score on this page is a published figure, taken from the model's own card, system card, technical report or release post, or from a public leaderboard. CorX Labs did not run these evaluations. Most are self-reported by the lab that built the model, which means they were produced under that lab's own choice of prompt, scaffold and number of attempts — so treat them as a starting point for a shortlist, not as a settled ranking.
A score someone other than the model's maker measured is marked Independent and names its measurer. Those are the stronger numbers on this page — an outside harness has no reason to flatter anyone — and there are not many of them.
Where a figure has not been published, the cell reads Not reported rather than an estimate. Nothing here is inferred, interpolated or guessed. Each model records the month its row was last checked. Full method and caveats.
Questions
GPT-6 Luna or Claude Haiku 5.5?
Is GPT-6 Luna better than Claude Haiku 5.5?
These two models have no benchmark in common with published figures for both, so there is nothing to compare directly. The specification and price rows below are still like for like.
Which is cheaper, GPT-6 Luna or Claude Haiku 5.5?
Both cost $0.20 per million tokens on a 3:1 input-to-output mix.
What is the difference between GPT-6 Luna and Claude Haiku 5.5?
GPT-6 Luna takes 1.1M tokens of context against 1M — 1.1× more room for long documents or a large codebase.
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