Model comparison
Gemini 3.5 Flash vs GPT-6 Luna
Gemini 3.5 Flash is the stronger model overall, scoring 54.1 to 45.1 on the Noometry Index. GPT-6 Luna costs 17× less per token, which makes it the better buy when Gemini 3.5 Flash's lead doesn't matter for your workload.
Last verified . 16 shared benchmarks.
GoogleSummary
- They share 16 benchmarks with published results for both. Gemini 3.5 Flash scores higher in 8 categories and GPT-6 Luna in 1 category; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Gemini 3.5 Flash leads 62.7 to 31.7.
- The biggest single-benchmark swing is DeepSWE: 37.4% for Gemini 3.5 Flash and 66.6% for GPT-6 Luna.
- GPT-6 Luna is cheaper at $0.10 / $0.50 per million input/output tokens, against $1.50 / $9 for Gemini 3.5 Flash.
- GPT-6 Luna accepts more context: 1.05M tokens versus 1.05M.
Side by side
| Gemini 3.5 Flash | GPT-6 Luna | |
|---|---|---|
| Provider | OpenAI | |
| Noometry Index | 54.1 | 45.1 |
| Released | 2026-05-19 | 2026-09-22 |
| Weights | Proprietary | Proprietary |
| Context window | 1.05M | 1.05M |
| Max output | 66K | 128K |
| Input $ / M tokens | $1.50 | $0.10 |
| Output $ / M tokens | $9 | $0.50 |
| Results tracked | 54 | 18 |
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Category by category
Coding GPT-6 Luna leads
Gemini 3.5 Flash: 49.1 (#46), GPT-6 Luna: 54.7 (#24)
| Benchmark | Gemini 3.5 Flash | GPT-6 Luna |
|---|---|---|
| DeepSWE | 37.4% | 66.6% |
| LMArena WebDev | 1499 | 1583 |
| LMArena Coding | 1492 | 1448 |
| SWE-bench Verified | 79.3% | — |
| FrontierCode | — | 42.4% |
| SciCode | 53.1% | — |
| WeirdML | 62.6% | — |
| ALE-Bench | 911.02 | — |
Agentic & Tool Use Not comparable
Gemini 3.5 Flash: 24.8 (#107), GPT-6 Luna: —
| Benchmark | Gemini 3.5 Flash | GPT-6 Luna |
|---|---|---|
| APEX-Agents | 27.5% | — |
| GBAEval | 6.7% | — |
| GDP.pdf | 14% | — |
| Vending-Bench 2 | 5,396 | — |
Reasoning Gemini 3.5 Flash leads
Gemini 3.5 Flash: 62.7 (#16), GPT-6 Luna: 31.7 (#68)
| Benchmark | Gemini 3.5 Flash | GPT-6 Luna |
|---|---|---|
| NYT Connections (extended) | 92.6% | 68.7% |
| LMArena Hard Prompts | 1488 | 1415 |
| ARC-AGI-2 | 72.1% | — |
| SimpleBench | 76.7% | — |
| ARC-AGI-1 | 92.5% | — |
| CritPt | 13.1% | — |
| Chess Puzzles | 50% | — |
| EnigmaEval | 25.4% | — |
| EBR-Bench | 4.8% | — |
| Mystery Game Puzzles | 32% | — |
| DTBench | 94.7% | — |
| LMCA | 47.1% | — |
| Surface Evolver Bench | 58.1% | — |
| Epoch Capabilities Index | 154.55 | — |
| ForecastBench | 59 | — |
Math Gemini 3.5 Flash leads
Gemini 3.5 Flash: 60.5 (#30), GPT-6 Luna: 35.6 (#140)
| Benchmark | Gemini 3.5 Flash | GPT-6 Luna |
|---|---|---|
| LMArena Math | 1502 | 1396 |
| FrontierMath (Tiers 1-3) | 62.8% | — |
| FrontierMath Tier 4 | 26.8% | — |
| MathArena Final-Answer Competitions | 76.3% | — |
| OTIS Mock AIME 2024-2025 | 95.6% | — |
| ProofBench | 31% | — |
| FrontierMath (Feb 2025 set) | 39% | — |
| FrontierMath Tier 4 (v1) | 14.6% | — |
Knowledge Gemini 3.5 Flash leads
Gemini 3.5 Flash: 66.3 (#10), GPT-6 Luna: 40.1 (#110)
| Benchmark | Gemini 3.5 Flash | GPT-6 Luna |
|---|---|---|
| LMArena Expert | 1493 | 1458 |
| GPQA Diamond | 92.8% | — |
| SimpleQA Verified | 66.2% | — |
Multimodal Gemini 3.5 Flash leads
Gemini 3.5 Flash: 45.7 (#13), GPT-6 Luna: 42.0 (#27)
| Benchmark | Gemini 3.5 Flash | GPT-6 Luna |
|---|---|---|
| LMArena Vision | 1311 | — |
| Blueprint-Bench 2 | 33.6% | — |
| Furniture Assembly | — | 44.2% |
| LMArena Document | 1463 | — |
Multilingual Gemini 3.5 Flash leads
Gemini 3.5 Flash: 57.1 (#12), GPT-6 Luna: 51.0 (#111)
| Benchmark | Gemini 3.5 Flash | GPT-6 Luna |
|---|---|---|
| LMArena Non-English | 1475 | 1390 |
| LMArena Chinese | 1525 | 1425 |
| LMArena Russian | 1491 | 1392 |
| LMArena Spanish | 1478 | 1416 |
| LMArena French | 1490 | — |
| LMArena German | 1500 | — |
| LMArena Japanese | 1481 | — |
| LMArena Korean | 1453 | — |
Instruction Following Gemini 3.5 Flash leads
Gemini 3.5 Flash: 77.2 (#29), GPT-6 Luna: 74.7 (#85)
| Benchmark | Gemini 3.5 Flash | GPT-6 Luna |
|---|---|---|
| LMArena Instruction Following | 1466 | 1414 |
Long Context Gemini 3.5 Flash leads
Gemini 3.5 Flash: 45.3 (#39), GPT-6 Luna: 43.3 (#95)
| Benchmark | Gemini 3.5 Flash | GPT-6 Luna |
|---|---|---|
| LMArena Longer Query | 1481 | 1417 |
Writing & Preference Gemini 3.5 Flash leads
Gemini 3.5 Flash: 65.5 (#46), GPT-6 Luna: 58.4 (#115)
| Benchmark | Gemini 3.5 Flash | GPT-6 Luna |
|---|---|---|
| LMArena Text | 1481 | 1392 |
| LMArena Creative Writing | 1467 | 1357 |
| LMArena Multi-Turn | 1482 | 1400 |
| EQ-Bench 4 | 1087 | — |
Frequently asked questions
Is Gemini 3.5 Flash better than GPT-6 Luna?
Gemini 3.5 Flash is the stronger model overall, scoring 54.1 to 45.1 on the Noometry Index. GPT-6 Luna costs 17× less per token, which makes it the better buy when Gemini 3.5 Flash's lead doesn't matter for your workload.
Which is cheaper, Gemini 3.5 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 3.5 Flash lists at $1.50 and $9.
Is Gemini 3.5 Flash or GPT-6 Luna better for coding?
GPT-6 Luna scores higher on coding benchmarks: 54.7 versus 49.1 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 3.5 Flash and GPT-6 Luna share?
16 benchmarks have published results for both models. Gemini 3.5 Flash has 54 scored results on Noometry and GPT-6 Luna has 18.