Model comparison
Gemini 3.1 Pro Preview vs GPT-5.6 Sol
GPT-5.6 Sol is the stronger model overall, scoring 65.0 to 56.7 on the Noometry Index. Gemini 3.1 Pro Preview costs 1.8× less per token, which makes it the better buy when GPT-5.6 Sol's lead doesn't matter for your workload.
Last verified . 57 shared benchmarks.
Summary
- They share 57 benchmarks with published results for both. Gemini 3.1 Pro Preview scores higher in 3 categories and GPT-5.6 Sol in 7 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Sol leads 85.6 to 62.1.
- The biggest single-benchmark swing is DeepSWE: 11.7% for Gemini 3.1 Pro Preview and 72.7% for GPT-5.6 Sol.
- Gemini 3.1 Pro Preview is cheaper at $2 / $12 per million input/output tokens, against $4 / $20 for GPT-5.6 Sol.
- GPT-5.6 Sol accepts more context: 1.05M tokens versus 1.05M.
Side by side
| Gemini 3.1 Pro Preview | GPT-5.6 Sol | |
|---|---|---|
| Provider | OpenAI | |
| Noometry Index | 56.7 | 65.0 |
| Released | 2026-02-19 | 2026-07-09 |
| Weights | Proprietary | Proprietary |
| Context window | 1.05M | 1.05M |
| Max output | 66K | 128K |
| Input $ / M tokens | $2 | $4 |
| Output $ / M tokens | $12 | $20 |
| Results tracked | 71 | 65 |
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Category by category
Coding GPT-5.6 Sol leads
Gemini 3.1 Pro Preview: 42.5 (#99), GPT-5.6 Sol: 65.1 (#7)
| Benchmark | Gemini 3.1 Pro Preview | GPT-5.6 Sol |
|---|---|---|
| DeepSWE | 11.7% | 72.7% |
| LMArena WebDev | 1447 | 1618 |
| SciCode | 58.9% | 57.1% |
| GSO | 22.6% | 76.5% |
| WeirdML | 72.1% | 89.4% |
| LMArena Coding | 1484 | 1498 |
| MirrorCode | 8.9% | 20% |
| ALE-Bench | 1,161 | 2,177 |
| SWE-bench Verified | 75.6% | — |
| FrontierCode | — | 47.5% |
| CursorBench | — | 41.7% |
| FrontierSWE | — | 32.2% |
| AlgoTune | 2.02 | — |
Agentic & Tool Use GPT-5.6 Sol leads
Gemini 3.1 Pro Preview: 37.7 (#34), GPT-5.6 Sol: 50.3 (#7)
| Benchmark | Gemini 3.1 Pro Preview | GPT-5.6 Sol |
|---|---|---|
| APEX-Agents | 35.3% | 51.4% |
| τ²-bench Banking | 26% | 46.9% |
| PostTrainBench | 22% | 36.2% |
| BALROG | 57% | 60% |
| GBAEval | 0.8% | 52.6% |
| GDP.pdf | 17% | 30.7% |
| LMArena Search | 1211 | 1257 |
| Vending-Bench 2 | 3,774 | 9,619 |
| Terminal-Bench | 80.2% | — |
| OSWorld 2.0 | — | 27.3% |
| DeepResearch Bench | 47.8% | — |
| ExploitBench | 26.1% | — |
| METR Time Horizons | 77% | — |
Reasoning GPT-5.6 Sol leads
Gemini 3.1 Pro Preview: 71.7 (#12), GPT-5.6 Sol: 74.8 (#8)
| Benchmark | Gemini 3.1 Pro Preview | GPT-5.6 Sol |
|---|---|---|
| ARC-AGI-2 | 77.1% | 92.5% |
| SimpleBench | 79.6% | 71.7% |
| NYT Connections (extended) | 97.4% | 93.8% |
| ARC-AGI-1 | 98% | 97.5% |
| CritPt | 17.7% | 32.3% |
| Chess Puzzles | 55% | 64% |
| EnigmaEval | 36.8% | 37.1% |
| EBR-Bench | 14.3% | 44.8% |
| LMArena Hard Prompts | 1485 | 1484 |
| Mystery Game Puzzles | 34% | 58% |
| DTBench | 97.1% | 96% |
| LMCA | 53.8% | 59.2% |
| Epoch Capabilities Index | 154.77 | 161.66 |
| Kagi LLM Benchmark | — | 67% |
| Thematic Generalization | 79.4% | — |
| Surface Evolver Bench | — | 93.1% |
| Bench to the Future 3 | — | 0.14 |
| ForecastBench | 59 | — |
Math GPT-5.6 Sol leads
Gemini 3.1 Pro Preview: 62.1 (#34), GPT-5.6 Sol: 85.6 (#9)
| Benchmark | Gemini 3.1 Pro Preview | GPT-5.6 Sol |
|---|---|---|
| FrontierMath (Tiers 1-3) | 59.6% | 89.1% |
| FrontierMath Tier 4 | 26.8% | 82.9% |
| OTIS Mock AIME 2024-2025 | 95.6% | 100% |
| ProofBench | 26% | 83% |
| LMArena Math | 1485 | 1474 |
| MathArena Final-Answer Competitions | 86.5% | — |
| FrontierMath (Feb 2025 set) | 36.9% | — |
| FrontierMath Erdős | — | 0% |
| FrontierMath Tier 4 (v1) | 16.7% | — |
Knowledge Gemini 3.1 Pro Preview leads
Gemini 3.1 Pro Preview: 71.8 (#3), GPT-5.6 Sol: 64.3 (#18)
| Benchmark | Gemini 3.1 Pro Preview | GPT-5.6 Sol |
|---|---|---|
| GPQA Diamond | 94.4% | 93.5% |
| SimpleQA Verified | 73.5% | 69.7% |
| Vectara Hallucination Rate | 10.4% | 12.4% |
| LMArena Expert | 1485 | 1516 |
| Humanity's Last Exam | 46.4% | — |
Multimodal GPT-5.6 Sol leads
Gemini 3.1 Pro Preview: 37.9 (#69), GPT-5.6 Sol: 48.6 (#9)
| Benchmark | Gemini 3.1 Pro Preview | GPT-5.6 Sol |
|---|---|---|
| LMArena Vision | 1296 | 1281 |
| Blueprint-Bench 2 | 26.5% | 33.6% |
| Furniture Assembly | 26.7% | 56.7% |
| LMArena Document | 1444 | 1483 |
Multilingual Gemini 3.1 Pro Preview leads
Gemini 3.1 Pro Preview: 57.0 (#12), GPT-5.6 Sol: 55.3 (#32)
| Benchmark | Gemini 3.1 Pro Preview | GPT-5.6 Sol |
|---|---|---|
| LMArena Non-English | 1477 | 1452 |
| LMArena Chinese | 1529 | 1527 |
| LMArena French | 1487 | 1477 |
| LMArena German | 1491 | 1476 |
| LMArena Japanese | 1493 | 1471 |
| LMArena Korean | 1455 | 1442 |
| LMArena Russian | 1498 | 1468 |
| LMArena Spanish | 1479 | 1441 |
Instruction Following Too close to call
Gemini 3.1 Pro Preview: 77.0 (#32), GPT-5.6 Sol: 77.7 (#16)
| Benchmark | Gemini 3.1 Pro Preview | GPT-5.6 Sol |
|---|---|---|
| LMArena Instruction Following | 1466 | 1482 |
Long Context Gemini 3.1 Pro Preview leads
Gemini 3.1 Pro Preview: 47.4 (#18), GPT-5.6 Sol: 45.4 (#42)
| Benchmark | Gemini 3.1 Pro Preview | GPT-5.6 Sol |
|---|---|---|
| LMArena Longer Query | 1483 | 1480 |
| CL-bench | 20.8% | — |
| CL-bench Life | 16.9% | — |
Writing & Preference GPT-5.6 Sol leads
Gemini 3.1 Pro Preview: 66.1 (#37), GPT-5.6 Sol: 73.3 (#12)
| Benchmark | Gemini 3.1 Pro Preview | GPT-5.6 Sol |
|---|---|---|
| LMArena Text | 1481 | 1457 |
| LMArena Creative Writing | 1482 | 1448 |
| EQ-Bench Creative Writing | 1491 | 1972 |
| EQ-Bench 4 | 1142 | 1250 |
| LMArena Multi-Turn | 1488 | 1460 |
Frequently asked questions
Is Gemini 3.1 Pro Preview better than GPT-5.6 Sol?
GPT-5.6 Sol is the stronger model overall, scoring 65.0 to 56.7 on the Noometry Index. Gemini 3.1 Pro Preview costs 1.8× less per token, which makes it the better buy when GPT-5.6 Sol's lead doesn't matter for your workload.
Which is cheaper, Gemini 3.1 Pro Preview or GPT-5.6 Sol?
Gemini 3.1 Pro Preview is cheaper. It lists at $2 per million input tokens and $12 per million output tokens; GPT-5.6 Sol lists at $4 and $20.
Is Gemini 3.1 Pro Preview or GPT-5.6 Sol better for coding?
GPT-5.6 Sol scores higher on coding benchmarks: 65.1 versus 42.5 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Sol does, with 1.05M tokens against 1.05M.
How many benchmarks do Gemini 3.1 Pro Preview and GPT-5.6 Sol share?
57 benchmarks have published results for both models. Gemini 3.1 Pro Preview has 71 scored results on Noometry and GPT-5.6 Sol has 65.