# gpt-oss-120b vs MiMo-V2-Omni

> MiMo-V2-Omni is the stronger model overall, scoring 43.6 to 36.3 on the Noometry Index. gpt-oss-120b costs 2.5× less per token, which makes it the better buy when MiMo-V2-Omni's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/gpt-oss-120b-vs-mimo-v2-omni
- Last updated: 2026-10-10
- Shared benchmarks: 17

## Summary

- They share 17 benchmarks with published results for both. gpt-oss-120b scores higher in 2 categories and MiMo-V2-Omni in 6 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where MiMo-V2-Omni leads 61.4 to 46.5.
- gpt-oss-120b is cheaper at $0.037 / $0.17 per million input/output tokens, against $0.14 / $0.28 for MiMo-V2-Omni.
- MiMo-V2-Omni accepts more context: 262K tokens versus 131K.
- gpt-oss-120b has downloadable open weights; the other is API-only.

## Snapshot

| | gpt-oss-120b | MiMo-V2-Omni |
|---|---|---|
| Provider | OpenAI | Xiaomi |
| Noometry Index | 36.3 | 43.6 |
| Rank | 217 | 88 |
| Context | 131K | 262K |
| Input $/M | $0.037 | $0.14 |
| Output $/M | $0.17 | $0.28 |
| Weights | Open | Proprietary |

## Coding

- gpt-oss-120b: 33.5 (#256)
- MiMo-V2-Omni: 43.3 (#89)

| Benchmark | gpt-oss-120b | MiMo-V2-Omni |
|---|---|---|
| LMArena Coding | 1380 | 1466 |
| SWE-bench Verified (bash only) | 26% | — |
| Aider Polyglot | 41.8% | — |
| SciCode | 36% | — |
| WeirdML | 48.2% | — |
| ALE-Bench | 575.62 | — |
| AlgoTune | 1.41 | — |

## Agentic & Tool Use

- gpt-oss-120b: 12.2 (#153)
- MiMo-V2-Omni: —

| Benchmark | gpt-oss-120b | MiMo-V2-Omni |
|---|---|---|
| Terminal-Bench | 18.7% | — |
| APEX-Agents | 4.4% | — |
| METR Time Horizons | 56.6% | — |
| Vending-Bench 2 | -21.53 | — |

## Reasoning

- gpt-oss-120b: 20.0 (#245)
- MiMo-V2-Omni: 29.7 (#88)

| Benchmark | gpt-oss-120b | MiMo-V2-Omni |
|---|---|---|
| LMArena Hard Prompts | 1364 | 1445 |
| SimpleBench | 22.1% | — |
| Kagi LLM Benchmark | 58.6% | — |
| CritPt | 1.1% | — |
| Chess Puzzles | 20% | — |
| Mystery Game Puzzles | 2% | — |
| DTBench | 76.3% | — |
| LMCA | 22.1% | — |
| Surface Evolver Bench | 25% | — |
| Epoch Capabilities Index | 139.93 | — |

## Math

- gpt-oss-120b: 52.5 (#50)
- MiMo-V2-Omni: 39.1 (#115)

| Benchmark | gpt-oss-120b | MiMo-V2-Omni |
|---|---|---|
| LMArena Math | 1389 | 1430 |
| OTIS Mock AIME 2024-2025 | 88.9% | — |
| Omni-MATH | 68.8% | — |

## Knowledge

- gpt-oss-120b: 42.4 (#96)
- MiMo-V2-Omni: 40.5 (#118)

| Benchmark | gpt-oss-120b | MiMo-V2-Omni |
|---|---|---|
| LMArena Expert | 1356 | 1449 |
| GPQA Diamond | 75.8% | — |
| MMLU-Pro | 79.5% | — |
| Confabulations | 15.7% | — |
| Vectara Hallucination Rate | 14.2% | — |
| GPQA (HELM) | 68.4% | — |

## Multimodal

- gpt-oss-120b: —
- MiMo-V2-Omni: 38.6 (#63)

| Benchmark | gpt-oss-120b | MiMo-V2-Omni |
|---|---|---|
| LMArena Vision | — | 1228 |

## Multilingual

- gpt-oss-120b: 48.0 (#147)
- MiMo-V2-Omni: 51.8 (#102)

| Benchmark | gpt-oss-120b | MiMo-V2-Omni |
|---|---|---|
| LMArena Non-English | 1351 | 1404 |
| LMArena Chinese | 1385 | 1465 |
| LMArena French | 1369 | 1447 |
| LMArena German | 1353 | 1399 |
| LMArena Japanese | 1331 | 1317 |
| LMArena Korean | 1282 | 1355 |
| LMArena Russian | 1343 | 1412 |
| LMArena Spanish | 1389 | 1434 |

## Instruction Following

- gpt-oss-120b: 69.3 (#173)
- MiMo-V2-Omni: 75.2 (#66)

| Benchmark | gpt-oss-120b | MiMo-V2-Omni |
|---|---|---|
| LMArena Instruction Following | 1318 | 1428 |
| IFEval | 83.6% | — |

## Long Context

- gpt-oss-120b: 31.4 (#278)
- MiMo-V2-Omni: 44.1 (#76)

| Benchmark | gpt-oss-120b | MiMo-V2-Omni |
|---|---|---|
| LMArena Longer Query | 1319 | 1442 |
| Fiction.LiveBench | 44.4% | — |

## Writing & Preference

- gpt-oss-120b: 46.5 (#217)
- MiMo-V2-Omni: 61.4 (#87)

| Benchmark | gpt-oss-120b | MiMo-V2-Omni |
|---|---|---|
| LMArena Text | 1365 | 1423 |
| LMArena Creative Writing | 1275 | 1392 |
| LMArena Multi-Turn | 1340 | 1445 |
| Short-Story Creative Writing | 77.1% | — |
| EQ-Bench Creative Writing | 961 | — |
| WildBench | 84.5% | — |

## FAQ

### Is gpt-oss-120b better than MiMo-V2-Omni?

MiMo-V2-Omni is the stronger model overall, scoring 43.6 to 36.3 on the Noometry Index. gpt-oss-120b costs 2.5× less per token, which makes it the better buy when MiMo-V2-Omni's lead doesn't matter for your workload.

### Which is cheaper, gpt-oss-120b or MiMo-V2-Omni?

gpt-oss-120b is cheaper. It lists at $0.037 per million input tokens and $0.17 per million output tokens; MiMo-V2-Omni lists at $0.14 and $0.28.

### Is gpt-oss-120b or MiMo-V2-Omni better for coding?

MiMo-V2-Omni scores higher on coding benchmarks: 43.3 versus 33.5 in the Noometry coding category.

### Which has the bigger context window?

MiMo-V2-Omni does, with 262K tokens against 131K.

### How many benchmarks do gpt-oss-120b and MiMo-V2-Omni share?

17 benchmarks have published results for both models. gpt-oss-120b has 48 scored results on Noometry and MiMo-V2-Omni has 18.