# Gemini 3.7 Flash vs GPT-5 Nano

> Gemini 3.7 Flash is the stronger model overall, scoring 59.8 to 33.4 on the Noometry Index. GPT-5 Nano costs 11× less per token, which makes it the better buy when Gemini 3.7 Flash's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/gemini-3-7-flash-vs-gpt-5-nano
- Last updated: 2026-10-10
- Shared benchmarks: 31

## Summary

- They share 31 benchmarks with published results for both. Gemini 3.7 Flash scores higher in 10 categories and GPT-5 Nano in 0 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Gemini 3.7 Flash leads 70.1 to 16.4.
- The biggest single-benchmark swing is ARC-AGI-2: 84.6% for Gemini 3.7 Flash and 2.6% for GPT-5 Nano.
- GPT-5 Nano is cheaper at $0.05 / $0.40 per million input/output tokens, against $0.75 / $3.75 for Gemini 3.7 Flash.
- Gemini 3.7 Flash accepts more context: 1.05M tokens versus 400K.

## Snapshot

| | Gemini 3.7 Flash | GPT-5 Nano |
|---|---|---|
| Provider | Google | OpenAI |
| Noometry Index | 59.8 | 33.4 |
| Rank | 11 | 234 |
| Context | 1.05M | 400K |
| Input $/M | $0.75 | $0.05 |
| Output $/M | $3.75 | $0.40 |
| Weights | Proprietary | Proprietary |

## Coding

- Gemini 3.7 Flash: 55.7 (#19)
- GPT-5 Nano: 33.3 (#248)

| Benchmark | Gemini 3.7 Flash | GPT-5 Nano |
|---|---|---|
| LMArena Coding | 1499 | 1351 |
| ALE-Bench | 904.3 | 718.67 |
| DeepSWE | 65.5% | — |
| FrontierCode | 43.6% | — |
| SWE-bench Verified (bash only) | — | 34.8% |
| LMArena WebDev | 1593 | — |
| FrontierSWE | 20.3% | — |
| SciCode | 59.8% | — |
| WeirdML | — | 38.1% |

## Agentic & Tool Use

- Gemini 3.7 Flash: 42.2 (#18)
- GPT-5 Nano: 25.6 (#99)

| Benchmark | Gemini 3.7 Flash | GPT-5 Nano |
|---|---|---|
| Terminal-Bench | — | 21.8% |
| APEX-Agents | 67.8% | — |
| Berkeley Function Calling Leaderboard | — | 51.5% |
| Remote Labor Index | 5% | — |
| GDP.pdf | 23.8% | — |

## Reasoning

- Gemini 3.7 Flash: 70.1 (#12)
- GPT-5 Nano: 16.4 (#291)

| Benchmark | Gemini 3.7 Flash | GPT-5 Nano |
|---|---|---|
| ARC-AGI-2 | 84.6% | 2.6% |
| ARC-AGI-1 | 95.5% | 20.7% |
| Chess Puzzles | 47% | 27% |
| LMArena Hard Prompts | 1497 | 1329 |
| Mystery Game Puzzles | 37% | 9% |
| DTBench | 96.8% | 62.7% |
| LMCA | 50.4% | 7.9% |
| Epoch Capabilities Index | 157.72 | 139.39 |
| Kagi LLM Benchmark | — | 62.2% |
| NYT Connections (extended) | 94% | — |
| CritPt | 14.3% | — |
| ForecastBench | — | 59.1 |

## Math

- Gemini 3.7 Flash: 69.5 (#18)
- GPT-5 Nano: 29.1 (#221)

| Benchmark | Gemini 3.7 Flash | GPT-5 Nano |
|---|---|---|
| FrontierMath (Tiers 1-3) | 71.6% | 20% |
| FrontierMath Tier 4 | 36.6% | 2.4% |
| OTIS Mock AIME 2024-2025 | 97.2% | 81.1% |
| ProofBench | 58% | 12% |
| LMArena Math | 1511 | 1318 |
| Omni-MATH | — | 54.6% |
| MATH Level 5 | — | 95.2% |
| FrontierMath (Feb 2025 set) | — | 8.3% |
| FrontierMath Tier 4 (v1) | — | 2.1% |

## Knowledge

- Gemini 3.7 Flash: 69.8 (#4)
- GPT-5 Nano: 35.8 (#166)

| Benchmark | Gemini 3.7 Flash | GPT-5 Nano |
|---|---|---|
| GPQA Diamond | 94.8% | 69.4% |
| SimpleQA Verified | 69.2% | 11.7% |
| LMArena Expert | 1508 | 1321 |
| MMLU-Pro | — | 77.8% |
| Vectara Hallucination Rate | — | 10.5% |
| GPQA (HELM) | — | 67.9% |

## Multimodal

- Gemini 3.7 Flash: 36.9 (#69)
- GPT-5 Nano: 30.7 (#102)

| Benchmark | Gemini 3.7 Flash | GPT-5 Nano |
|---|---|---|
| LMArena Vision | 1314 | 1159 |
| VPCT | — | 37.2% |
| Furniture Assembly | 26.7% | — |

## Multilingual

- Gemini 3.7 Flash: 57.8 (#7)
- GPT-5 Nano: 45.5 (#167)

| Benchmark | Gemini 3.7 Flash | GPT-5 Nano |
|---|---|---|
| LMArena Non-English | 1485 | 1313 |
| LMArena Chinese | 1553 | 1355 |
| LMArena German | 1485 | 1329 |
| LMArena Korean | 1486 | 1269 |
| LMArena Russian | 1519 | 1296 |
| LMArena Spanish | 1507 | 1360 |
| LMArena French | 1512 | — |
| LMArena Japanese | 1506 | — |

## Instruction Following

- Gemini 3.7 Flash: 78.0 (#12)
- GPT-5 Nano: 75.2 (#72)

| Benchmark | Gemini 3.7 Flash | GPT-5 Nano |
|---|---|---|
| LMArena Instruction Following | 1486 | 1306 |
| IFEval | — | 93.2% |

## Long Context

- Gemini 3.7 Flash: 45.8 (#28)
- GPT-5 Nano: 31.3 (#275)

| Benchmark | Gemini 3.7 Flash | GPT-5 Nano |
|---|---|---|
| LMArena Longer Query | 1493 | 1313 |
| Fiction.LiveBench | — | 44.4% |

## Writing & Preference

- Gemini 3.7 Flash: 71.4 (#19)
- GPT-5 Nano: 39.4 (#244)

| Benchmark | Gemini 3.7 Flash | GPT-5 Nano |
|---|---|---|
| LMArena Text | 1487 | 1320 |
| LMArena Creative Writing | 1490 | 1250 |
| EQ-Bench Creative Writing | 1723 | 705 |
| LMArena Multi-Turn | 1495 | 1311 |
| WildBench | — | 80.6% |

## FAQ

### Is Gemini 3.7 Flash better than GPT-5 Nano?

Gemini 3.7 Flash is the stronger model overall, scoring 59.8 to 33.4 on the Noometry Index. GPT-5 Nano costs 11× less per token, which makes it the better buy when Gemini 3.7 Flash's lead doesn't matter for your workload.

### Which is cheaper, Gemini 3.7 Flash or GPT-5 Nano?

GPT-5 Nano is cheaper. It lists at $0.05 per million input tokens and $0.40 per million output tokens; Gemini 3.7 Flash lists at $0.75 and $3.75.

### Is Gemini 3.7 Flash or GPT-5 Nano better for coding?

Gemini 3.7 Flash scores higher on coding benchmarks: 55.7 versus 33.3 in the Noometry coding category.

### Which has the bigger context window?

Gemini 3.7 Flash does, with 1.05M tokens against 400K.

### How many benchmarks do Gemini 3.7 Flash and GPT-5 Nano share?

31 benchmarks have published results for both models. Gemini 3.7 Flash has 44 scored results on Noometry and GPT-5 Nano has 48.