# Gemini 2.5 Flash vs gpt-oss-120b

> Gemini 2.5 Flash is the stronger model overall, scoring 39.3 to 36.3 on the Noometry Index. gpt-oss-120b costs 12× less per token, which makes it the better buy when Gemini 2.5 Flash's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/gemini-2-5-flash-vs-gpt-oss-120b
- Last updated: 2026-10-12
- Shared benchmarks: 40

## Summary

- They share 40 benchmarks with published results for both. Gemini 2.5 Flash scores higher in 6 categories and gpt-oss-120b in 3 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in agentic & tool use, where Gemini 2.5 Flash leads 30.8 to 12.2.
- The biggest single-benchmark swing is Fiction.LiveBench: 77.8% for Gemini 2.5 Flash and 44.4% for gpt-oss-120b.
- gpt-oss-120b is cheaper at $0.037 / $0.17 per million input/output tokens, against $0.30 / $2.50 for Gemini 2.5 Flash.
- Gemini 2.5 Flash accepts more context: 1.05M tokens versus 131K.
- gpt-oss-120b has downloadable open weights; the other is API-only.

## Snapshot

| | Gemini 2.5 Flash | gpt-oss-120b |
|---|---|---|
| Provider | Google | OpenAI |
| Noometry Index | 39.3 | 36.3 |
| Rank | 170 | 217 |
| Context | 1.05M | 131K |
| Input $/M | $0.30 | $0.037 |
| Output $/M | $2.50 | $0.17 |
| Weights | Proprietary | Open |

## Coding

- Gemini 2.5 Flash: 35.8 (#220)
- gpt-oss-120b: 33.5 (#256)

| Benchmark | Gemini 2.5 Flash | gpt-oss-120b |
|---|---|---|
| SWE-bench Verified (bash only) | 28.7% | 26% |
| Aider Polyglot | 55.1% | 41.8% |
| WeirdML | 41.9% | 48.2% |
| LMArena Coding | 1424 | 1380 |
| ALE-Bench | 661.88 | 575.62 |
| SciCode | — | 36% |
| AlgoTune | — | 1.41 |

## Agentic & Tool Use

- Gemini 2.5 Flash: 30.8 (#74)
- gpt-oss-120b: 12.2 (#153)

| Benchmark | Gemini 2.5 Flash | gpt-oss-120b |
|---|---|---|
| Terminal-Bench | 17.1% | 18.7% |
| Vending-Bench 2 | 548.84 | -21.53 |
| APEX-Agents | — | 4.4% |
| Berkeley Function Calling Leaderboard | 56.2% | — |
| TheAgentCompany | 41.1% | — |
| BALROG | 33.5% | — |
| METR Time Horizons | — | 56.6% |

## Reasoning

- Gemini 2.5 Flash: 18.1 (#286)
- gpt-oss-120b: 20.0 (#245)

| Benchmark | Gemini 2.5 Flash | gpt-oss-120b |
|---|---|---|
| SimpleBench | 41.2% | 22.1% |
| Kagi LLM Benchmark | 56.8% | 58.6% |
| CritPt | 1.1% | 1.1% |
| LMArena Hard Prompts | 1422 | 1364 |
| DTBench | 76.5% | 76.3% |
| LMCA | 27.5% | 22.1% |
| Epoch Capabilities Index | 143.03 | 139.93 |
| ARC-AGI-2 | 2.5% | — |
| ARC-AGI-1 | 33.3% | — |
| Chess Puzzles | — | 20% |
| EnigmaEval | 2.7% | — |
| Mystery Game Puzzles | — | 2% |
| Surface Evolver Bench | — | 25% |
| ForecastBench | 60.6 | — |

## Math

- Gemini 2.5 Flash: 39.9 (#98)
- gpt-oss-120b: 52.5 (#50)

| Benchmark | Gemini 2.5 Flash | gpt-oss-120b |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 73.1% | 88.9% |
| Omni-MATH | 38.5% | 68.8% |
| LMArena Math | 1415 | 1389 |
| FrontierMath (Feb 2025 set) | 4.8% | — |
| FrontierMath Tier 4 (v1) | 4.2% | — |

## Knowledge

- Gemini 2.5 Flash: 36.4 (#168)
- gpt-oss-120b: 42.4 (#96)

| Benchmark | Gemini 2.5 Flash | gpt-oss-120b |
|---|---|---|
| MMLU-Pro | 63.9% | 79.5% |
| Confabulations | 16.8% | 15.7% |
| Vectara Hallucination Rate | 7.8% | 14.2% |
| GPQA (HELM) | 39% | 68.4% |
| LMArena Expert | 1426 | 1356 |
| GPQA Diamond | — | 75.8% |
| Humanity's Last Exam | 12.1% | — |

## Multimodal

- Gemini 2.5 Flash: 41.8 (#32)
- gpt-oss-120b: —

| Benchmark | Gemini 2.5 Flash | gpt-oss-120b |
|---|---|---|
| LMArena Vision | 1253 | — |
| GeoBench | 76% | — |
| VPCT | 46.2% | — |
| SpatialViz-Bench | 36.9% | — |

## Multilingual

- Gemini 2.5 Flash: 52.3 (#88)
- gpt-oss-120b: 48.0 (#147)

| Benchmark | Gemini 2.5 Flash | gpt-oss-120b |
|---|---|---|
| LMArena Non-English | 1409 | 1351 |
| LMArena Chinese | 1450 | 1385 |
| LMArena French | 1433 | 1369 |
| LMArena German | 1418 | 1353 |
| LMArena Japanese | 1405 | 1331 |
| LMArena Korean | 1385 | 1282 |
| LMArena Russian | 1415 | 1343 |
| LMArena Spanish | 1421 | 1389 |

## Instruction Following

- Gemini 2.5 Flash: 75.7 (#54)
- gpt-oss-120b: 69.3 (#173)

| Benchmark | Gemini 2.5 Flash | gpt-oss-120b |
|---|---|---|
| IFEval | 89.8% | 83.6% |
| LMArena Instruction Following | 1405 | 1318 |

## Long Context

- Gemini 2.5 Flash: 47.5 (#17)
- gpt-oss-120b: 31.4 (#278)

| Benchmark | Gemini 2.5 Flash | gpt-oss-120b |
|---|---|---|
| Fiction.LiveBench | 77.8% | 44.4% |
| LMArena Longer Query | 1419 | 1319 |

## Writing & Preference

- Gemini 2.5 Flash: 53.8 (#157)
- gpt-oss-120b: 46.5 (#217)

| Benchmark | Gemini 2.5 Flash | gpt-oss-120b |
|---|---|---|
| LMArena Text | 1417 | 1365 |
| LMArena Creative Writing | 1400 | 1275 |
| Short-Story Creative Writing | 76.5% | 77.1% |
| EQ-Bench Creative Writing | 1137 | 961 |
| WildBench | 81.7% | 84.5% |
| LMArena Multi-Turn | 1408 | 1340 |

## FAQ

### Is Gemini 2.5 Flash better than gpt-oss-120b?

Gemini 2.5 Flash is the stronger model overall, scoring 39.3 to 36.3 on the Noometry Index. gpt-oss-120b costs 12× less per token, which makes it the better buy when Gemini 2.5 Flash's lead doesn't matter for your workload.

### Which is cheaper, Gemini 2.5 Flash or gpt-oss-120b?

gpt-oss-120b is cheaper. It lists at $0.037 per million input tokens and $0.17 per million output tokens; Gemini 2.5 Flash lists at $0.30 and $2.50.

### Is Gemini 2.5 Flash or gpt-oss-120b better for coding?

Gemini 2.5 Flash scores higher on coding benchmarks: 35.8 versus 33.5 in the Noometry coding category.

### Which has the bigger context window?

Gemini 2.5 Flash does, with 1.05M tokens against 131K.

### How many benchmarks do Gemini 2.5 Flash and gpt-oss-120b share?

40 benchmarks have published results for both models. Gemini 2.5 Flash has 54 scored results on Noometry and gpt-oss-120b has 48.