# Gemini 3.8 Flash vs GPT-5 Nano

> Gemini 3.8 Flash is the stronger model overall, scoring 59.5 to 33.4 on the Noometry Index. GPT-5 Nano costs 11× less per token, which makes it the better buy when Gemini 3.8 Flash's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/gemini-3-8-flash-vs-gpt-5-nano
- Last updated: 2026-10-10
- Shared benchmarks: 29

## Summary

- They share 29 benchmarks with published results for both. Gemini 3.8 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 3.8 Flash leads 64.7 to 16.4.
- The biggest single-benchmark swing is SimpleQA Verified: 69.7% for Gemini 3.8 Flash and 11.7% 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 3.8 Flash.
- Gemini 3.8 Flash accepts more context: 1.05M tokens versus 400K.

## Snapshot

| | Gemini 3.8 Flash | GPT-5 Nano |
|---|---|---|
| Provider | Google | OpenAI |
| Noometry Index | 59.5 | 33.4 |
| Rank | 13 | 234 |
| Context | 1.05M | 400K |
| Input $/M | $0.75 | $0.05 |
| Output $/M | $3.75 | $0.40 |
| Weights | Proprietary | Proprietary |

## Coding

- Gemini 3.8 Flash: 56.4 (#17)
- GPT-5 Nano: 33.3 (#248)

| Benchmark | Gemini 3.8 Flash | GPT-5 Nano |
|---|---|---|
| LMArena Coding | 1512 | 1351 |
| ALE-Bench | 1,270 | 718.67 |
| DeepSWE | 73.8% | — |
| FrontierCode | 41.2% | — |
| SWE-bench Verified (bash only) | — | 34.8% |
| CursorBench | 39.6% | — |
| LMArena WebDev | 1581 | — |
| FrontierSWE | 19.6% | — |
| SciCode | 56.6% | — |
| WeirdML | — | 38.1% |

## Agentic & Tool Use

- Gemini 3.8 Flash: 40.3 (#22)
- GPT-5 Nano: 25.6 (#99)

| Benchmark | Gemini 3.8 Flash | GPT-5 Nano |
|---|---|---|
| Terminal-Bench | — | 21.8% |
| APEX-Agents | 64.3% | — |
| Berkeley Function Calling Leaderboard | — | 51.5% |
| GDP.pdf | 23.4% | — |
| Vending-Bench 2 | 5,094 | — |

## Reasoning

- Gemini 3.8 Flash: 64.7 (#13)
- GPT-5 Nano: 16.4 (#291)

| Benchmark | Gemini 3.8 Flash | GPT-5 Nano |
|---|---|---|
| Chess Puzzles | 61% | 27% |
| LMArena Hard Prompts | 1506 | 1329 |
| Mystery Game Puzzles | 47% | 9% |
| DTBench | 95.7% | 62.7% |
| LMCA | 52.9% | 7.9% |
| Epoch Capabilities Index | 157.13 | 139.39 |
| ARC-AGI-2 | — | 2.6% |
| Kagi LLM Benchmark | — | 62.2% |
| NYT Connections (extended) | 97.4% | — |
| ARC-AGI-1 | — | 20.7% |
| CritPt | 18.3% | — |
| Surface Evolver Bench | 76.9% | — |
| ForecastBench | — | 59.1 |

## Math

- Gemini 3.8 Flash: 65.2 (#22)
- GPT-5 Nano: 29.1 (#221)

| Benchmark | Gemini 3.8 Flash | GPT-5 Nano |
|---|---|---|
| FrontierMath (Tiers 1-3) | 68.4% | 20% |
| FrontierMath Tier 4 | 22% | 2.4% |
| OTIS Mock AIME 2024-2025 | 98.9% | 81.1% |
| ProofBench | 48% | 12% |
| LMArena Math | 1528 | 1318 |
| Omni-MATH | — | 54.6% |
| MATH Level 5 | — | 95.2% |
| FrontierMath (Feb 2025 set) | — | 8.3% |
| FrontierMath Tier 4 (v1) | — | 2.1% |

## Knowledge

- Gemini 3.8 Flash: 74.9 (#2)
- GPT-5 Nano: 35.8 (#166)

| Benchmark | Gemini 3.8 Flash | GPT-5 Nano |
|---|---|---|
| GPQA Diamond | 95.4% | 69.4% |
| SimpleQA Verified | 69.7% | 11.7% |
| LMArena Expert | 1529 | 1321 |
| Humanity's Last Exam | 44.5% | — |
| MMLU-Pro | — | 77.8% |
| Vectara Hallucination Rate | — | 10.5% |
| GPQA (HELM) | — | 67.9% |

## Multimodal

- Gemini 3.8 Flash: 40.9 (#33)
- GPT-5 Nano: 30.7 (#102)

| Benchmark | Gemini 3.8 Flash | GPT-5 Nano |
|---|---|---|
| LMArena Vision | 1312 | 1159 |
| VPCT | — | 37.2% |
| Blueprint-Bench 2 | 38.6% | — |
| Furniture Assembly | 31.7% | — |

## Multilingual

- Gemini 3.8 Flash: 57.9 (#5)
- GPT-5 Nano: 45.5 (#167)

| Benchmark | Gemini 3.8 Flash | GPT-5 Nano |
|---|---|---|
| LMArena Non-English | 1486 | 1313 |
| LMArena Chinese | 1547 | 1355 |
| LMArena German | 1470 | 1329 |
| LMArena Korean | 1479 | 1269 |
| LMArena Russian | 1507 | 1296 |
| LMArena Spanish | 1492 | 1360 |
| LMArena French | 1502 | — |
| LMArena Japanese | 1505 | — |

## Instruction Following

- Gemini 3.8 Flash: 78.2 (#11)
- GPT-5 Nano: 75.2 (#72)

| Benchmark | Gemini 3.8 Flash | GPT-5 Nano |
|---|---|---|
| LMArena Instruction Following | 1489 | 1306 |
| IFEval | — | 93.2% |

## Long Context

- Gemini 3.8 Flash: 46.2 (#23)
- GPT-5 Nano: 31.3 (#275)

| Benchmark | Gemini 3.8 Flash | GPT-5 Nano |
|---|---|---|
| LMArena Longer Query | 1506 | 1313 |
| Fiction.LiveBench | — | 44.4% |

## Writing & Preference

- Gemini 3.8 Flash: 72.1 (#16)
- GPT-5 Nano: 39.4 (#244)

| Benchmark | Gemini 3.8 Flash | GPT-5 Nano |
|---|---|---|
| LMArena Text | 1494 | 1320 |
| LMArena Creative Writing | 1485 | 1250 |
| EQ-Bench Creative Writing | 1748 | 705 |
| LMArena Multi-Turn | 1503 | 1311 |
| WildBench | — | 80.6% |

## FAQ

### Is Gemini 3.8 Flash better than GPT-5 Nano?

Gemini 3.8 Flash is the stronger model overall, scoring 59.5 to 33.4 on the Noometry Index. GPT-5 Nano costs 11× 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-5 Nano?

GPT-5 Nano is cheaper. It lists at $0.05 per million input tokens and $0.40 per million output tokens; Gemini 3.8 Flash lists at $0.75 and $3.75.

### Is Gemini 3.8 Flash or GPT-5 Nano better for coding?

Gemini 3.8 Flash scores higher on coding benchmarks: 56.4 versus 33.3 in the Noometry coding category.

### Which has the bigger context window?

Gemini 3.8 Flash does, with 1.05M tokens against 400K.

### How many benchmarks do Gemini 3.8 Flash and GPT-5 Nano share?

29 benchmarks have published results for both models. Gemini 3.8 Flash has 46 scored results on Noometry and GPT-5 Nano has 48.