Model comparison
Gemini 3.8 Flash vs GPT-5.6 Luna
Gemini 3.8 Flash is the stronger model overall, scoring 61.8 to 54.6 on the Noometry Index. GPT-5.6 Luna costs 3.3× less per token, which makes it the better buy when Gemini 3.8 Flash's lead doesn't matter for your workload.
Last verified . 47 shared benchmarks.
GoogleSummary
- They share 47 benchmarks with published results for both. Gemini 3.8 Flash scores higher in 8 categories and GPT-5.6 Luna in 2 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Gemini 3.8 Flash leads 76.9 to 47.6.
- The biggest single-benchmark swing is FrontierMath Tier 4: 22% for Gemini 3.8 Flash and 61% for GPT-5.6 Luna.
- GPT-5.6 Luna is cheaper at $0.20 / $1.20 per million input/output tokens, against $0.75 / $3.75 for Gemini 3.8 Flash.
- GPT-5.6 Luna accepts more context: 1.05M tokens versus 1.05M.
Side by side
| Gemini 3.8 Flash | GPT-5.6 Luna | |
|---|---|---|
| Provider | OpenAI | |
| Noometry Index | 61.8 | 54.6 |
| Released | 2026-09-02 | 2026-07-09 |
| Weights | Proprietary | Proprietary |
| Context window | 1.05M | 1.05M |
| Max output | 66K | 128K |
| Input $ / M tokens | $0.75 | $0.20 |
| Output $ / M tokens | $3.75 | $1.20 |
| Results tracked | 50 | 52 |
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Category by category
Coding Gemini 3.8 Flash leads
Gemini 3.8 Flash: 59.2 (#15), GPT-5.6 Luna: 54.5 (#28)
| Benchmark | Gemini 3.8 Flash | GPT-5.6 Luna |
|---|---|---|
| DeepSWE | 73.8% | 67.2% |
| FrontierCode | 41.2% | 39.8% |
| CursorBench | 39.6% | 35.9% |
| LMArena WebDev | 1584 | 1519 |
| SciCode | 56.6% | 53.6% |
| WeirdML | 84.8% | 60.9% |
| LMArena Coding | 1510 | 1466 |
| ALE-Bench | 1,270 | 1,667 |
| FrontierSWE | 19.6% | — |
Agentic & Tool Use Gemini 3.8 Flash leads
Gemini 3.8 Flash: 41.8 (#21), GPT-5.6 Luna: 34.4 (#45)
| Benchmark | Gemini 3.8 Flash | GPT-5.6 Luna |
|---|---|---|
| APEX-Agents | 64.3% | 43% |
| GDP.pdf | 23.4% | 22.7% |
| Vending-Bench 2 | 5,094 | 4,095 |
| Remote Labor Index | 5.8% | — |
| BALROG | — | 45.6% |
Reasoning Gemini 3.8 Flash leads
Gemini 3.8 Flash: 76.9 (#5), GPT-5.6 Luna: 47.6 (#43)
| Benchmark | Gemini 3.8 Flash | GPT-5.6 Luna |
|---|---|---|
| ARC-AGI-2 | 89.2% | 59.5% |
| NYT Connections (extended) | 97.4% | 69.4% |
| ARC-AGI-1 | 98.5% | 88% |
| CritPt | 18.3% | 20.6% |
| Chess Puzzles | 61% | 40% |
| LMArena Hard Prompts | 1508 | 1451 |
| Mystery Game Puzzles | 47% | 21% |
| DTBench | 95.7% | 89.1% |
| LMCA | 52.9% | 48.5% |
| Surface Evolver Bench | 76.9% | 61.9% |
| Epoch Capabilities Index | 156.71 | 156.39 |
| SimpleBench | — | 46.8% |
| Kagi LLM Benchmark | — | 49.1% |
Math GPT-5.6 Luna leads
Gemini 3.8 Flash: 65.3 (#28), GPT-5.6 Luna: 77.7 (#14)
| Benchmark | Gemini 3.8 Flash | GPT-5.6 Luna |
|---|---|---|
| FrontierMath (Tiers 1-3) | 68.4% | 82.1% |
| FrontierMath Tier 4 | 22% | 61% |
| OTIS Mock AIME 2024-2025 | 98.9% | 98.3% |
| ProofBench | 48% | 60% |
| LMArena Math | 1528 | 1458 |
Knowledge Gemini 3.8 Flash leads
Gemini 3.8 Flash: 74.8 (#2), GPT-5.6 Luna: 58.5 (#34)
| Benchmark | Gemini 3.8 Flash | GPT-5.6 Luna |
|---|---|---|
| GPQA Diamond | 95.4% | 91.6% |
| SimpleQA Verified | 69.7% | 41% |
| LMArena Expert | 1524 | 1478 |
| Humanity's Last Exam | 44.5% | — |
Multimodal GPT-5.6 Luna leads
Gemini 3.8 Flash: 40.7 (#45), GPT-5.6 Luna: 42.7 (#28)
| Benchmark | Gemini 3.8 Flash | GPT-5.6 Luna |
|---|---|---|
| LMArena Vision | 1314 | 1258 |
| Blueprint-Bench 2 | 38.6% | 22.6% |
| Furniture Assembly | 31.7% | 42.5% |
| LMArena Document | — | 1457 |
Multilingual Gemini 3.8 Flash leads
Gemini 3.8 Flash: 58.0 (#5), GPT-5.6 Luna: 52.8 (#78)
| Benchmark | Gemini 3.8 Flash | GPT-5.6 Luna |
|---|---|---|
| LMArena Non-English | 1491 | 1417 |
| LMArena Chinese | 1554 | 1470 |
| LMArena French | 1498 | 1456 |
| LMArena German | 1493 | 1454 |
| LMArena Japanese | 1502 | 1411 |
| LMArena Korean | 1459 | 1415 |
| LMArena Russian | 1515 | 1428 |
| LMArena Spanish | 1485 | 1448 |
Instruction Following Gemini 3.8 Flash leads
Gemini 3.8 Flash: 78.0 (#13), GPT-5.6 Luna: 75.6 (#57)
| Benchmark | Gemini 3.8 Flash | GPT-5.6 Luna |
|---|---|---|
| LMArena Instruction Following | 1490 | 1437 |
Long Context Gemini 3.8 Flash leads
Gemini 3.8 Flash: 46.3 (#24), GPT-5.6 Luna: 43.9 (#82)
| Benchmark | Gemini 3.8 Flash | GPT-5.6 Luna |
|---|---|---|
| LMArena Longer Query | 1508 | 1436 |
Writing & Preference Gemini 3.8 Flash leads
Gemini 3.8 Flash: 72.2 (#15), GPT-5.6 Luna: 68.0 (#29)
| Benchmark | Gemini 3.8 Flash | GPT-5.6 Luna |
|---|---|---|
| LMArena Text | 1499 | 1431 |
| LMArena Creative Writing | 1492 | 1396 |
| EQ-Bench Creative Writing | 1748 | 1829 |
| LMArena Multi-Turn | 1501 | 1434 |
| EQ-Bench 4 | — | 1156 |
Frequently asked questions
Is Gemini 3.8 Flash better than GPT-5.6 Luna?
Gemini 3.8 Flash is the stronger model overall, scoring 61.8 to 54.6 on the Noometry Index. GPT-5.6 Luna costs 3.3× less per token, which makes it the better buy when Gemini 3.8 Flash's lead doesn't matter for your workload.
Which is cheaper, Gemini 3.8 Flash or GPT-5.6 Luna?
GPT-5.6 Luna is cheaper. It lists at $0.20 per million input tokens and $1.20 per million output tokens; Gemini 3.8 Flash lists at $0.75 and $3.75.
Is Gemini 3.8 Flash or GPT-5.6 Luna better for coding?
Gemini 3.8 Flash scores higher on coding benchmarks: 59.2 versus 54.5 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Luna does, with 1.05M tokens against 1.05M.
How many benchmarks do Gemini 3.8 Flash and GPT-5.6 Luna share?
47 benchmarks have published results for both models. Gemini 3.8 Flash has 50 scored results on Noometry and GPT-5.6 Luna has 52.