Head to head
GPT-5.6 Luna vs Gemini 3.5 Flash
GPT-5.6 Luna from OpenAI against Gemini 3.5 Flash from Google DeepMind — specification, price and every benchmark both makers have published, in one table.
At a glance
Benchmarks
GPT-5.6 Luna leads
GPT-5.6 Luna wins 2 of the 2 benchmarks both models report, Gemini 3.5 Flash wins 0, by a wide margin. The average gap across shared tests is 8.1 points.
Price
GPT-5.6 Luna is cheaper
On a 3:1 input-to-output mix, GPT-5.6 Luna costs $0.45 per million tokens against $3.38 for Gemini 3.5 Flash — about 7.5× less. Remember that a reasoning model bills its thinking as output, so cost per answer can diverge much further than cost per token.
What actually differs
- These two are closely matched on the specification side — same broad capabilities, same licensing posture. The decision comes down to the benchmark rows and the price.
Scorecard
Which is better at what
Maths, coding, reasoning and the rest — one line each, averaged over the benchmarks both models actually report.
| Category | GPT-5.6 Luna | Gemini 3.5 Flash | Better at this |
|---|---|---|---|
| ReasoningOnly one model reports this | — | — | Not comparable |
| MathsNeither model reports this | — | — | Not comparable |
| CodingSWE-bench Pro, Terminal-Bench 2.1 | 73.7% | 65.7% | GPT-5.6 Luna+8.0 |
| 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 wonOut of 1 comparable | 1 | 0 | GPT-5.6 Luna |
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-5.6 Luna and Gemini 3.5 Flash, row by row
| Attribute | GPT-5.6 LunaOpenAI | Gemini 3.5 FlashGoogle DeepMind |
|---|---|---|
| Specification | ||
| MakerWho built it | OpenAI | Google DeepMind |
| Released | 2026-07 | 2026-05 |
| Context windowHow much can go in at once | Not reported | 1,048,576 tokens |
| Max output | Not reported | 65,536 tokens |
| Input | Text, Image | Text, Image, Audio, Video |
| ReasoningSpends extra tokens thinking before it answers | Yes | Yes |
| Tool calling | Yes | Yes |
| Licence | Proprietary | Proprietary |
| Open weightsCan you download and run it yourself | No | No |
| Price | ||
| Input priceUSD per million tokens in | $0.20 | $1.50 |
| Output priceUSD per million tokens out | $1.20 | $9.00 |
| Cached input | $0.02 | Not reported |
| Blended 3:1A 3-in-to-1-out million-token mix — a fairer single number than input price alone | $0.45Cheapest | $3.38 |
| Published benchmarks | ||
| GPQA Diamond198 graduate-level physics, chemistry and biology questions written to be Google-proof. PhD holders in the matching field score about 65%. | Not reported | |
| SWE-bench ProA harder, contamination-resistant successor to SWE-bench Verified, drawn from commercial and copyleft repositories that were never public training data. | ||
| Terminal-Bench 2.1The 2.1 revision of the terminal agent benchmark. Scores on it are not comparable with Terminal-Bench 2.0 or 4.0 — the task set changed. | ||
| 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-5.6 Luna or Gemini 3.5 Flash?
Is GPT-5.6 Luna better than Gemini 3.5 Flash?
GPT-5.6 Luna wins 2 of the 2 benchmarks both models report, Gemini 3.5 Flash wins 0, by a wide margin. The average gap across shared tests is 8.1 points.
Which is cheaper, GPT-5.6 Luna or Gemini 3.5 Flash?
On a 3:1 input-to-output mix, GPT-5.6 Luna costs $0.45 per million tokens against $3.38 for Gemini 3.5 Flash — about 7.5× less. Remember that a reasoning model bills its thinking as output, so cost per answer can diverge much further than cost per token.
What is the difference between GPT-5.6 Luna and Gemini 3.5 Flash?
These two are closely matched on the specification side — same broad capabilities, same licensing posture. The decision comes down to the benchmark rows and the price.
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