# Gemini 3.5 Flash vs GPT-5.6 Sol

> GPT-5.6 Sol is the stronger model overall, scoring 64.4 to 54.1 on the Noometry Index. Gemini 3.5 Flash costs 2.4× less per token, which makes it the better buy when GPT-5.6 Sol's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/gemini-3-5-flash-vs-gpt-5-6-sol
- Last updated: 2026-10-10
- Shared benchmarks: 49

## Summary

- They share 49 benchmarks with published results for both. Gemini 3.5 Flash scores higher in 3 categories and GPT-5.6 Sol in 7 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in agentic & tool use, where GPT-5.6 Sol leads 50.2 to 24.8.
- The biggest single-benchmark swing is FrontierMath Tier 4: 26.8% for Gemini 3.5 Flash and 82.9% for GPT-5.6 Sol.
- Gemini 3.5 Flash is cheaper at $1.50 / $9 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.

## Snapshot

| | Gemini 3.5 Flash | GPT-5.6 Sol |
|---|---|---|
| Provider | Google | OpenAI |
| Noometry Index | 54.1 | 64.4 |
| Rank | 29 | 6 |
| Context | 1.05M | 1.05M |
| Input $/M | $1.50 | $4 |
| Output $/M | $9 | $20 |
| Weights | Proprietary | Proprietary |

## Coding

- Gemini 3.5 Flash: 49.1 (#46)
- GPT-5.6 Sol: 61.1 (#7)

| Benchmark | Gemini 3.5 Flash | GPT-5.6 Sol |
|---|---|---|
| DeepSWE | 37.4% | 72.7% |
| LMArena WebDev | 1499 | 1619 |
| SciCode | 53.1% | 57.1% |
| WeirdML | 62.6% | 89.4% |
| LMArena Coding | 1492 | 1493 |
| ALE-Bench | 911.02 | 2,177 |
| SWE-bench Verified | 79.3% | — |
| FrontierCode | — | 47.5% |
| CursorBench | — | 41.7% |
| FrontierSWE | — | 32.2% |
| MirrorCode | — | 20% |

## Agentic & Tool Use

- Gemini 3.5 Flash: 24.8 (#107)
- GPT-5.6 Sol: 50.2 (#7)

| Benchmark | Gemini 3.5 Flash | GPT-5.6 Sol |
|---|---|---|
| APEX-Agents | 27.5% | 51.4% |
| GBAEval | 6.7% | 52.6% |
| GDP.pdf | 14% | 30.7% |
| Vending-Bench 2 | 5,396 | 9,619 |
| OSWorld 2.0 | — | 27.3% |
| τ²-bench Banking | — | 46.9% |
| PostTrainBench | — | 36.2% |
| BALROG | — | 60% |
| LMArena Search | — | 1257 |

## Reasoning

- Gemini 3.5 Flash: 62.7 (#16)
- GPT-5.6 Sol: 74.7 (#6)

| Benchmark | Gemini 3.5 Flash | GPT-5.6 Sol |
|---|---|---|
| ARC-AGI-2 | 72.1% | 92.5% |
| SimpleBench | 76.7% | 71.7% |
| NYT Connections (extended) | 92.6% | 93.8% |
| ARC-AGI-1 | 92.5% | 97.5% |
| CritPt | 13.1% | 32.3% |
| Chess Puzzles | 50% | 64% |
| EnigmaEval | 25.4% | 37.1% |
| EBR-Bench | 4.8% | 44.8% |
| LMArena Hard Prompts | 1488 | 1481 |
| Mystery Game Puzzles | 32% | 58% |
| DTBench | 94.7% | 96% |
| LMCA | 47.1% | 59.2% |
| Surface Evolver Bench | 58.1% | 93.1% |
| Epoch Capabilities Index | 154.55 | 161.99 |
| Kagi LLM Benchmark | — | 67% |
| Bench to the Future 3 | — | 0.14 |
| ForecastBench | 59 | — |

## Math

- Gemini 3.5 Flash: 60.5 (#30)
- GPT-5.6 Sol: 85.5 (#7)

| Benchmark | Gemini 3.5 Flash | GPT-5.6 Sol |
|---|---|---|
| FrontierMath (Tiers 1-3) | 62.8% | 89.1% |
| FrontierMath Tier 4 | 26.8% | 82.9% |
| OTIS Mock AIME 2024-2025 | 95.6% | 100% |
| ProofBench | 31% | 83% |
| LMArena Math | 1502 | 1472 |
| MathArena Final-Answer Competitions | 76.3% | — |
| FrontierMath (Feb 2025 set) | 39% | — |
| FrontierMath Erdős | — | 0% |
| FrontierMath Tier 4 (v1) | 14.6% | — |

## Knowledge

- Gemini 3.5 Flash: 66.3 (#10)
- GPT-5.6 Sol: 64.3 (#16)

| Benchmark | Gemini 3.5 Flash | GPT-5.6 Sol |
|---|---|---|
| GPQA Diamond | 92.8% | 93.5% |
| SimpleQA Verified | 66.2% | 69.7% |
| LMArena Expert | 1493 | 1517 |
| Vectara Hallucination Rate | — | 12.4% |

## Multimodal

- Gemini 3.5 Flash: 45.7 (#13)
- GPT-5.6 Sol: 48.9 (#8)

| Benchmark | Gemini 3.5 Flash | GPT-5.6 Sol |
|---|---|---|
| LMArena Vision | 1311 | 1281 |
| Blueprint-Bench 2 | 33.6% | 33.6% |
| LMArena Document | 1463 | 1483 |
| Furniture Assembly | — | 56.7% |

## Multilingual

- Gemini 3.5 Flash: 57.1 (#12)
- GPT-5.6 Sol: 55.4 (#32)

| Benchmark | Gemini 3.5 Flash | GPT-5.6 Sol |
|---|---|---|
| LMArena Non-English | 1475 | 1450 |
| LMArena Chinese | 1525 | 1523 |
| LMArena French | 1490 | 1475 |
| LMArena German | 1500 | 1476 |
| LMArena Japanese | 1481 | 1475 |
| LMArena Korean | 1453 | 1434 |
| LMArena Russian | 1491 | 1466 |
| LMArena Spanish | 1478 | 1449 |

## Instruction Following

- Gemini 3.5 Flash: 77.2 (#29)
- GPT-5.6 Sol: 77.8 (#16)

| Benchmark | Gemini 3.5 Flash | GPT-5.6 Sol |
|---|---|---|
| LMArena Instruction Following | 1466 | 1480 |

## Long Context

- Gemini 3.5 Flash: 45.3 (#39)
- GPT-5.6 Sol: 45.3 (#42)

| Benchmark | Gemini 3.5 Flash | GPT-5.6 Sol |
|---|---|---|
| LMArena Longer Query | 1481 | 1479 |

## Writing & Preference

- Gemini 3.5 Flash: 65.5 (#46)
- GPT-5.6 Sol: 73.5 (#12)

| Benchmark | Gemini 3.5 Flash | GPT-5.6 Sol |
|---|---|---|
| LMArena Text | 1481 | 1456 |
| LMArena Creative Writing | 1467 | 1450 |
| EQ-Bench 4 | 1087 | 1250 |
| LMArena Multi-Turn | 1482 | 1456 |
| EQ-Bench Creative Writing | — | 1972 |

## FAQ

### Is Gemini 3.5 Flash better than GPT-5.6 Sol?

GPT-5.6 Sol is the stronger model overall, scoring 64.4 to 54.1 on the Noometry Index. Gemini 3.5 Flash costs 2.4× 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 3.5 Flash or GPT-5.6 Sol?

Gemini 3.5 Flash is cheaper. It lists at $1.50 per million input tokens and $9 per million output tokens; GPT-5.6 Sol lists at $4 and $20.

### Is Gemini 3.5 Flash or GPT-5.6 Sol better for coding?

GPT-5.6 Sol scores higher on coding benchmarks: 61.1 versus 49.1 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 3.5 Flash and GPT-5.6 Sol share?

49 benchmarks have published results for both models. Gemini 3.5 Flash has 54 scored results on Noometry and GPT-5.6 Sol has 64.