Model comparison
Gemini 3.8 Flash vs GPT-6.1 Sol
GPT-6.1 Sol is the stronger model overall, scoring 65.6 to 61.8 on the Noometry Index. Gemini 3.8 Flash costs 2.7× less per token, which makes it the better buy when GPT-6.1 Sol's lead doesn't matter for your workload.
Last verified . 33 shared benchmarks.
GoogleSummary
- They share 33 benchmarks with published results for both. Gemini 3.8 Flash scores higher in 6 categories and GPT-6.1 Sol in 4 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-6.1 Sol leads 93.7 to 65.3.
- The biggest single-benchmark swing is FrontierMath Tier 4: 22% for Gemini 3.8 Flash and 100% for GPT-6.1 Sol.
- Gemini 3.8 Flash is cheaper at $0.75 / $3.75 per million input/output tokens, against $2 / $10 for GPT-6.1 Sol.
- GPT-6.1 Sol accepts more context: 1.05M tokens versus 1.05M.
Side by side
| Gemini 3.8 Flash | GPT-6.1 Sol | |
|---|---|---|
| Provider | OpenAI | |
| Noometry Index | 61.8 | 65.6 |
| Released | 2026-09-02 | 2026-09-29 |
| Weights | Proprietary | Proprietary |
| Context window | 1.05M | 1.05M |
| Max output | 66K | 128K |
| Input $ / M tokens | $0.75 | $2 |
| Output $ / M tokens | $3.75 | $10 |
| Results tracked | 50 | 34 |
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Category by category
Coding GPT-6.1 Sol leads
Gemini 3.8 Flash: 59.2 (#15), GPT-6.1 Sol: 63.2 (#8)
| Benchmark | Gemini 3.8 Flash | GPT-6.1 Sol |
|---|---|---|
| DeepSWE | 73.8% | 75.2% |
| FrontierCode | 41.2% | 50.2% |
| LMArena WebDev | 1584 | 1755 |
| SciCode | 56.6% | 55.8% |
| LMArena Coding | 1510 | 1487 |
| CursorBench | 39.6% | — |
| FrontierSWE | 19.6% | — |
| WeirdML | 84.8% | — |
| ALE-Bench | 1,270 | — |
Agentic & Tool Use Gemini 3.8 Flash leads
Gemini 3.8 Flash: 41.8 (#21), GPT-6.1 Sol: 39.6 (#26)
| Benchmark | Gemini 3.8 Flash | GPT-6.1 Sol |
|---|---|---|
| APEX-Agents | 64.3% | 60% |
| GDP.pdf | 23.4% | 32% |
| Remote Labor Index | 5.8% | — |
| Vending-Bench 2 | 5,094 | — |
Reasoning GPT-6.1 Sol leads
Gemini 3.8 Flash: 76.9 (#5), GPT-6.1 Sol: 81.9 (#2)
| Benchmark | Gemini 3.8 Flash | GPT-6.1 Sol |
|---|---|---|
| ARC-AGI-2 | 89.2% | 94.2% |
| NYT Connections (extended) | 97.4% | 95.5% |
| ARC-AGI-1 | 98.5% | 98.5% |
| CritPt | 18.3% | 31.7% |
| Chess Puzzles | 61% | 61% |
| LMArena Hard Prompts | 1508 | 1466 |
| Mystery Game Puzzles | 47% | 80% |
| Epoch Capabilities Index | 156.71 | 166.09 |
| EBR-Bench | — | 54.3% |
| DTBench | 95.7% | — |
| LMCA | 52.9% | — |
| Surface Evolver Bench | 76.9% | — |
Math GPT-6.1 Sol leads
Gemini 3.8 Flash: 65.3 (#28), GPT-6.1 Sol: 93.7 (#1)
| Benchmark | Gemini 3.8 Flash | GPT-6.1 Sol |
|---|---|---|
| FrontierMath (Tiers 1-3) | 68.4% | 93.7% |
| FrontierMath Tier 4 | 22% | 100% |
| OTIS Mock AIME 2024-2025 | 98.9% | 100% |
| ProofBench | 48% | 99% |
| LMArena Math | 1528 | 1464 |
Knowledge Gemini 3.8 Flash leads
Gemini 3.8 Flash: 74.8 (#2), GPT-6.1 Sol: 71.8 (#4)
| Benchmark | Gemini 3.8 Flash | GPT-6.1 Sol |
|---|---|---|
| GPQA Diamond | 95.4% | 95.4% |
| SimpleQA Verified | 69.7% | 73.9% |
| LMArena Expert | 1524 | 1502 |
| Humanity's Last Exam | 44.5% | — |
Multimodal GPT-6.1 Sol leads
Gemini 3.8 Flash: 40.7 (#45), GPT-6.1 Sol: 52.7 (#5)
| Benchmark | Gemini 3.8 Flash | GPT-6.1 Sol |
|---|---|---|
| LMArena Vision | 1314 | 1288 |
| Furniture Assembly | 31.7% | 80% |
| Blueprint-Bench 2 | 38.6% | — |
Multilingual Gemini 3.8 Flash leads
Gemini 3.8 Flash: 58.0 (#5), GPT-6.1 Sol: 54.3 (#46)
| Benchmark | Gemini 3.8 Flash | GPT-6.1 Sol |
|---|---|---|
| LMArena Non-English | 1491 | 1438 |
| LMArena Chinese | 1554 | 1477 |
| LMArena Russian | 1515 | 1455 |
| LMArena French | 1498 | — |
| LMArena German | 1493 | — |
| LMArena Japanese | 1502 | — |
| LMArena Korean | 1459 | — |
| LMArena Spanish | 1485 | — |
Instruction Following Gemini 3.8 Flash leads
Gemini 3.8 Flash: 78.0 (#13), GPT-6.1 Sol: 77.0 (#29)
| Benchmark | Gemini 3.8 Flash | GPT-6.1 Sol |
|---|---|---|
| LMArena Instruction Following | 1490 | 1468 |
Long Context Gemini 3.8 Flash leads
Gemini 3.8 Flash: 46.3 (#24), GPT-6.1 Sol: 44.9 (#54)
| Benchmark | Gemini 3.8 Flash | GPT-6.1 Sol |
|---|---|---|
| LMArena Longer Query | 1508 | 1465 |
Writing & Preference Gemini 3.8 Flash leads
Gemini 3.8 Flash: 72.2 (#15), GPT-6.1 Sol: 63.6 (#63)
| Benchmark | Gemini 3.8 Flash | GPT-6.1 Sol |
|---|---|---|
| LMArena Text | 1499 | 1447 |
| LMArena Creative Writing | 1492 | 1432 |
| LMArena Multi-Turn | 1501 | 1449 |
| EQ-Bench Creative Writing | 1748 | — |
Frequently asked questions
Is Gemini 3.8 Flash better than GPT-6.1 Sol?
GPT-6.1 Sol is the stronger model overall, scoring 65.6 to 61.8 on the Noometry Index. Gemini 3.8 Flash costs 2.7× less per token, which makes it the better buy when GPT-6.1 Sol's lead doesn't matter for your workload.
Which is cheaper, Gemini 3.8 Flash or GPT-6.1 Sol?
Gemini 3.8 Flash is cheaper. It lists at $0.75 per million input tokens and $3.75 per million output tokens; GPT-6.1 Sol lists at $2 and $10.
Is Gemini 3.8 Flash or GPT-6.1 Sol better for coding?
GPT-6.1 Sol scores higher on coding benchmarks: 63.2 versus 59.2 in the Noometry coding category.
Which has the bigger context window?
GPT-6.1 Sol does, with 1.05M tokens against 1.05M.
How many benchmarks do Gemini 3.8 Flash and GPT-6.1 Sol share?
33 benchmarks have published results for both models. Gemini 3.8 Flash has 50 scored results on Noometry and GPT-6.1 Sol has 34.