Model comparison
Gemini 3.8 Flash vs GPT-6 Luna
Gemini 3.8 Flash is the stronger model overall, scoring 59.5 to 45.1 on the Noometry Index. GPT-6 Luna costs 7.5× less per token, which makes it the better buy when Gemini 3.8 Flash's lead doesn't matter for your workload.
Last verified . 18 shared benchmarks.
GoogleSummary
- They share 18 benchmarks with published results for both. Gemini 3.8 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 knowledge, where Gemini 3.8 Flash leads 74.9 to 40.1.
- The biggest single-benchmark swing is NYT Connections (extended): 97.4% for Gemini 3.8 Flash and 68.7% for GPT-6 Luna.
- GPT-6 Luna is cheaper at $0.10 / $0.50 per million input/output tokens, against $0.75 / $3.75 for Gemini 3.8 Flash.
- GPT-6 Luna accepts more context: 1.05M tokens versus 1.05M.
Side by side
| Gemini 3.8 Flash | GPT-6 Luna | |
|---|---|---|
| Provider | OpenAI | |
| Noometry Index | 59.5 | 45.1 |
| Released | 2026-09-02 | 2026-09-22 |
| Weights | Proprietary | Proprietary |
| Context window | 1.05M | 1.05M |
| Max output | 66K | 128K |
| Input $ / M tokens | $0.75 | $0.10 |
| Output $ / M tokens | $3.75 | $0.50 |
| Results tracked | 46 | 18 |
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Category by category
Coding Gemini 3.8 Flash leads
Gemini 3.8 Flash: 56.4 (#17), GPT-6 Luna: 54.7 (#24)
| Benchmark | Gemini 3.8 Flash | GPT-6 Luna |
|---|---|---|
| DeepSWE | 73.8% | 66.6% |
| FrontierCode | 41.2% | 42.4% |
| LMArena WebDev | 1581 | 1583 |
| LMArena Coding | 1512 | 1448 |
| CursorBench | 39.6% | — |
| FrontierSWE | 19.6% | — |
| SciCode | 56.6% | — |
| ALE-Bench | 1,270 | — |
Agentic & Tool Use Not comparable
Gemini 3.8 Flash: 40.3 (#22), GPT-6 Luna: —
| Benchmark | Gemini 3.8 Flash | GPT-6 Luna |
|---|---|---|
| APEX-Agents | 64.3% | — |
| GDP.pdf | 23.4% | — |
| Vending-Bench 2 | 5,094 | — |
Reasoning Gemini 3.8 Flash leads
Gemini 3.8 Flash: 64.7 (#13), GPT-6 Luna: 31.7 (#68)
| Benchmark | Gemini 3.8 Flash | GPT-6 Luna |
|---|---|---|
| NYT Connections (extended) | 97.4% | 68.7% |
| LMArena Hard Prompts | 1506 | 1415 |
| CritPt | 18.3% | — |
| Chess Puzzles | 61% | — |
| Mystery Game Puzzles | 47% | — |
| DTBench | 95.7% | — |
| LMCA | 52.9% | — |
| Surface Evolver Bench | 76.9% | — |
| Epoch Capabilities Index | 157.13 | — |
Math Gemini 3.8 Flash leads
Gemini 3.8 Flash: 65.2 (#22), GPT-6 Luna: 35.6 (#140)
| Benchmark | Gemini 3.8 Flash | GPT-6 Luna |
|---|---|---|
| LMArena Math | 1528 | 1396 |
| FrontierMath (Tiers 1-3) | 68.4% | — |
| FrontierMath Tier 4 | 22% | — |
| OTIS Mock AIME 2024-2025 | 98.9% | — |
| ProofBench | 48% | — |
Knowledge Gemini 3.8 Flash leads
Gemini 3.8 Flash: 74.9 (#2), GPT-6 Luna: 40.1 (#110)
| Benchmark | Gemini 3.8 Flash | GPT-6 Luna |
|---|---|---|
| LMArena Expert | 1529 | 1458 |
| GPQA Diamond | 95.4% | — |
| Humanity's Last Exam | 44.5% | — |
| SimpleQA Verified | 69.7% | — |
Multimodal GPT-6 Luna leads
Gemini 3.8 Flash: 40.9 (#33), GPT-6 Luna: 42.0 (#27)
| Benchmark | Gemini 3.8 Flash | GPT-6 Luna |
|---|---|---|
| Furniture Assembly | 31.7% | 44.2% |
| LMArena Vision | 1312 | — |
| Blueprint-Bench 2 | 38.6% | — |
Multilingual Gemini 3.8 Flash leads
Gemini 3.8 Flash: 57.9 (#5), GPT-6 Luna: 51.0 (#111)
| Benchmark | Gemini 3.8 Flash | GPT-6 Luna |
|---|---|---|
| LMArena Non-English | 1486 | 1390 |
| LMArena Chinese | 1547 | 1425 |
| LMArena Russian | 1507 | 1392 |
| LMArena Spanish | 1492 | 1416 |
| LMArena French | 1502 | — |
| LMArena German | 1470 | — |
| LMArena Japanese | 1505 | — |
| LMArena Korean | 1479 | — |
Instruction Following Gemini 3.8 Flash leads
Gemini 3.8 Flash: 78.2 (#11), GPT-6 Luna: 74.7 (#85)
| Benchmark | Gemini 3.8 Flash | GPT-6 Luna |
|---|---|---|
| LMArena Instruction Following | 1489 | 1414 |
Long Context Gemini 3.8 Flash leads
Gemini 3.8 Flash: 46.2 (#23), GPT-6 Luna: 43.3 (#95)
| Benchmark | Gemini 3.8 Flash | GPT-6 Luna |
|---|---|---|
| LMArena Longer Query | 1506 | 1417 |
Writing & Preference Gemini 3.8 Flash leads
Gemini 3.8 Flash: 72.1 (#16), GPT-6 Luna: 58.4 (#115)
| Benchmark | Gemini 3.8 Flash | GPT-6 Luna |
|---|---|---|
| LMArena Text | 1494 | 1392 |
| LMArena Creative Writing | 1485 | 1357 |
| LMArena Multi-Turn | 1503 | 1400 |
| EQ-Bench Creative Writing | 1748 | — |
Frequently asked questions
Is Gemini 3.8 Flash better than GPT-6 Luna?
Gemini 3.8 Flash is the stronger model overall, scoring 59.5 to 45.1 on the Noometry Index. GPT-6 Luna costs 7.5× 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-6 Luna?
GPT-6 Luna is cheaper. It lists at $0.10 per million input tokens and $0.50 per million output tokens; Gemini 3.8 Flash lists at $0.75 and $3.75.
Is Gemini 3.8 Flash or GPT-6 Luna better for coding?
Gemini 3.8 Flash scores higher on coding benchmarks: 56.4 versus 54.7 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 3.8 Flash and GPT-6 Luna share?
18 benchmarks have published results for both models. Gemini 3.8 Flash has 46 scored results on Noometry and GPT-6 Luna has 18.