Model comparison
Gemini 2.5 Flash vs GPT-6 Luna
GPT-6 Luna is the stronger model overall, scoring 53.3 to 39.3 on the Noometry Index.
Last verified . 26 shared benchmarks.
GoogleSummary
- They share 26 benchmarks with published results for both. Gemini 2.5 Flash scores higher in 3 categories and GPT-6 Luna in 7 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-6 Luna leads 76.1 to 39.9.
- The biggest single-benchmark swing is ARC-AGI-2: 2.5% for Gemini 2.5 Flash and 59.3% for GPT-6 Luna.
- GPT-6 Luna is cheaper at $0.10 / $0.50 per million input/output tokens, against $0.30 / $2.50 for Gemini 2.5 Flash.
- GPT-6 Luna accepts more context: 1.05M tokens versus 1.05M.
Side by side
| Gemini 2.5 Flash | GPT-6 Luna | |
|---|---|---|
| Provider | OpenAI | |
| Noometry Index | 39.3 | 53.3 |
| Released | 2025-04-17 | 2026-09-22 |
| Weights | Proprietary | Proprietary |
| Context window | 1.05M | 1.05M |
| Max output | 66K | 128K |
| Input $ / M tokens | $0.30 | $0.10 |
| Output $ / M tokens | $2.50 | $0.50 |
| Results tracked | 54 | 42 |
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Category by category
Coding GPT-6 Luna leads
Gemini 2.5 Flash: 35.8 (#220), GPT-6 Luna: 55.5 (#25)
| Benchmark | Gemini 2.5 Flash | GPT-6 Luna |
|---|---|---|
| LMArena Coding | 1424 | 1439 |
| ALE-Bench | 661.88 | 1,577 |
| DeepSWE | — | 66.6% |
| FrontierCode | — | 42.4% |
| SWE-bench Verified (bash only) | 28.7% | — |
| Aider Polyglot | 55.1% | — |
| LMArena WebDev | — | 1581 |
| SciCode | — | 54.6% |
| WeirdML | 41.9% | — |
Agentic & Tool Use GPT-6 Luna leads
Gemini 2.5 Flash: 30.8 (#74), GPT-6 Luna: 33.3 (#54)
| Benchmark | Gemini 2.5 Flash | GPT-6 Luna |
|---|---|---|
| Terminal-Bench | 17.1% | — |
| APEX-Agents | — | 44.3% |
| Berkeley Function Calling Leaderboard | 56.2% | — |
| TheAgentCompany | 41.1% | — |
| BALROG | 33.5% | — |
| GDP.pdf | — | 23% |
| Vending-Bench 2 | 548.84 | — |
Reasoning GPT-6 Luna leads
Gemini 2.5 Flash: 18.1 (#286), GPT-6 Luna: 48.2 (#41)
| Benchmark | Gemini 2.5 Flash | GPT-6 Luna |
|---|---|---|
| ARC-AGI-2 | 2.5% | 59.3% |
| ARC-AGI-1 | 33.3% | 86.7% |
| CritPt | 1.1% | 19.4% |
| LMArena Hard Prompts | 1422 | 1411 |
| DTBench | 76.5% | 90.1% |
| LMCA | 27.5% | 44.5% |
| Epoch Capabilities Index | 143.03 | 156.28 |
| SimpleBench | 41.2% | — |
| Kagi LLM Benchmark | 56.8% | — |
| NYT Connections (extended) | — | 68.7% |
| Chess Puzzles | — | 31% |
| EnigmaEval | 2.7% | — |
| Mystery Game Puzzles | — | 7% |
| ForecastBench | 60.6 | — |
Math GPT-6 Luna leads
Gemini 2.5 Flash: 39.9 (#98), GPT-6 Luna: 76.1 (#15)
| Benchmark | Gemini 2.5 Flash | GPT-6 Luna |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 73.1% | 98.9% |
| LMArena Math | 1415 | 1416 |
| FrontierMath (Tiers 1-3) | — | 78.9% |
| FrontierMath Tier 4 | — | 56.1% |
| ProofBench | — | 64% |
| Omni-MATH | 38.5% | — |
| FrontierMath (Feb 2025 set) | 4.8% | — |
| FrontierMath Tier 4 (v1) | 4.2% | — |
Knowledge GPT-6 Luna leads
Gemini 2.5 Flash: 36.4 (#168), GPT-6 Luna: 57.0 (#41)
| Benchmark | Gemini 2.5 Flash | GPT-6 Luna |
|---|---|---|
| LMArena Expert | 1426 | 1444 |
| GPQA Diamond | — | 90.5% |
| Humanity's Last Exam | 12.1% | — |
| SimpleQA Verified | — | 41.4% |
| MMLU-Pro | 63.9% | — |
| Confabulations | 16.8% | — |
| Vectara Hallucination Rate | 7.8% | — |
| GPQA (HELM) | 39% | — |
Multimodal Too close to call
Gemini 2.5 Flash: 41.8 (#32), GPT-6 Luna: 42.4 (#30)
| Benchmark | Gemini 2.5 Flash | GPT-6 Luna |
|---|---|---|
| LMArena Vision | 1253 | 1217 |
| GeoBench | 76% | — |
| VPCT | 46.2% | — |
| Blueprint-Bench 2 | — | 31.2% |
| Furniture Assembly | — | 44.2% |
| SpatialViz-Bench | 36.9% | — |
Multilingual Gemini 2.5 Flash leads
Gemini 2.5 Flash: 52.3 (#88), GPT-6 Luna: 50.5 (#117)
| Benchmark | Gemini 2.5 Flash | GPT-6 Luna |
|---|---|---|
| LMArena Non-English | 1409 | 1386 |
| LMArena Chinese | 1450 | 1433 |
| LMArena French | 1433 | 1420 |
| LMArena German | 1418 | 1369 |
| LMArena Japanese | 1405 | 1369 |
| LMArena Korean | 1385 | 1360 |
| LMArena Russian | 1415 | 1394 |
| LMArena Spanish | 1421 | 1393 |
Instruction Following Gemini 2.5 Flash leads
Gemini 2.5 Flash: 75.7 (#54), GPT-6 Luna: 74.3 (#99)
| Benchmark | Gemini 2.5 Flash | GPT-6 Luna |
|---|---|---|
| LMArena Instruction Following | 1405 | 1409 |
| IFEval | 89.8% | — |
Long Context Gemini 2.5 Flash leads
Gemini 2.5 Flash: 47.5 (#17), GPT-6 Luna: 43.0 (#111)
| Benchmark | Gemini 2.5 Flash | GPT-6 Luna |
|---|---|---|
| LMArena Longer Query | 1419 | 1409 |
| Fiction.LiveBench | 77.8% | — |
Writing & Preference GPT-6 Luna leads
Gemini 2.5 Flash: 53.8 (#157), GPT-6 Luna: 58.3 (#119)
| Benchmark | Gemini 2.5 Flash | GPT-6 Luna |
|---|---|---|
| LMArena Text | 1417 | 1391 |
| LMArena Creative Writing | 1400 | 1363 |
| LMArena Multi-Turn | 1408 | 1396 |
| Short-Story Creative Writing | 76.5% | — |
| EQ-Bench Creative Writing | 1137 | — |
| WildBench | 81.7% | — |
Frequently asked questions
Is Gemini 2.5 Flash better than GPT-6 Luna?
GPT-6 Luna is the stronger model overall, scoring 53.3 to 39.3 on the Noometry Index.
Which is cheaper, Gemini 2.5 Flash or GPT-6 Luna?
GPT-6 Luna is cheaper. It lists at $0.10 per million input tokens and $0.50 per million output tokens; Gemini 2.5 Flash lists at $0.30 and $2.50.
Is Gemini 2.5 Flash or GPT-6 Luna better for coding?
GPT-6 Luna scores higher on coding benchmarks: 55.5 versus 35.8 in the Noometry coding category.
Which has the bigger context window?
GPT-6 Luna does, with 1.05M tokens against 1.05M.
How many benchmarks do Gemini 2.5 Flash and GPT-6 Luna share?
26 benchmarks have published results for both models. Gemini 2.5 Flash has 54 scored results on Noometry and GPT-6 Luna has 42.