Model comparison
gpt-oss-120b vs MiMo-V2-Flash
MiMo-V2-Flash is the stronger model overall, scoring 40.8 to 36.0 on the Noometry Index. gpt-oss-120b costs 2.5× less per token, which makes it the better buy when MiMo-V2-Flash's lead doesn't matter for your workload.
Last verified . 20 shared benchmarks.
Summary
- They share 20 benchmarks with published results for both. gpt-oss-120b scores higher in 2 categories and MiMo-V2-Flash in 6 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where gpt-oss-120b leads 50.6 to 35.6.
- The biggest single-benchmark swing is SciCode: 36% for gpt-oss-120b and 25.9% for MiMo-V2-Flash.
- gpt-oss-120b is cheaper at $0.037 / $0.17 per million input/output tokens, against $0.14 / $0.28 for MiMo-V2-Flash.
- MiMo-V2-Flash accepts more context: 262K tokens versus 131K.
Side by side
| gpt-oss-120b | MiMo-V2-Flash | |
|---|---|---|
| Provider | OpenAI | Xiaomi |
| Noometry Index | 36.0 | 40.8 |
| Released | 2025-08-05 | 2025-12-16 |
| Weights | Open | Open |
| Context window | 131K | 262K |
| Max output | 41K | 66K |
| Input $ / M tokens | $0.037 | $0.14 |
| Output $ / M tokens | $0.17 | $0.28 |
| Results tracked | 48 | 21 |
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Category by category
Coding MiMo-V2-Flash leads
gpt-oss-120b: 33.2 (#251), MiMo-V2-Flash: 35.8 (#205)
| Benchmark | gpt-oss-120b | MiMo-V2-Flash |
|---|---|---|
| SciCode | 36% | 25.9% |
| LMArena Coding | 1380 | 1443 |
| ALE-Bench | 575.62 | 737.95 |
| SWE-bench Verified (bash only) | 26% | — |
| Aider Polyglot | 41.8% | — |
| LMArena WebDev | — | 1330 |
| WeirdML | 48.2% | — |
| AlgoTune | 1.41 | — |
Agentic & Tool Use Not comparable
gpt-oss-120b: 12.1 (#145), MiMo-V2-Flash: —
| Benchmark | gpt-oss-120b | MiMo-V2-Flash |
|---|---|---|
| Terminal-Bench | 18.7% | — |
| APEX-Agents | 4.4% | — |
| METR Time Horizons | 56.6% | — |
| Vending-Bench 2 | -21.53 | — |
Reasoning MiMo-V2-Flash leads
gpt-oss-120b: 19.5 (#236), MiMo-V2-Flash: 24.1 (#155)
| Benchmark | gpt-oss-120b | MiMo-V2-Flash |
|---|---|---|
| CritPt | 1.1% | 0% |
| LMArena Hard Prompts | 1364 | 1420 |
| SimpleBench | 22.1% | — |
| Kagi LLM Benchmark | 58.6% | — |
| Chess Puzzles | 20% | — |
| Mystery Game Puzzles | 2% | — |
| DTBench | 76.3% | — |
| LMCA | 22.1% | — |
| Surface Evolver Bench | 25% | — |
| Epoch Capabilities Index | 139.94 | — |
Math gpt-oss-120b leads
gpt-oss-120b: 50.6 (#43), MiMo-V2-Flash: 35.6 (#141)
| Benchmark | gpt-oss-120b | MiMo-V2-Flash |
|---|---|---|
| LMArena Math | 1388 | 1396 |
| OTIS Mock AIME 2024-2025 | 88.9% | — |
| Omni-MATH | 68.8% | — |
Knowledge gpt-oss-120b leads
gpt-oss-120b: 42.3 (#89), MiMo-V2-Flash: 39.1 (#126)
| Benchmark | gpt-oss-120b | MiMo-V2-Flash |
|---|---|---|
| LMArena Expert | 1356 | 1425 |
| GPQA Diamond | 75.8% | — |
| MMLU-Pro | 79.5% | — |
| Confabulations | 15.7% | — |
| Vectara Hallucination Rate | 14.2% | — |
| GPQA (HELM) | 68.4% | — |
Multilingual MiMo-V2-Flash leads
gpt-oss-120b: 48.2 (#142), MiMo-V2-Flash: 51.1 (#109)
| Benchmark | gpt-oss-120b | MiMo-V2-Flash |
|---|---|---|
| LMArena Non-English | 1350 | 1391 |
| LMArena Chinese | 1384 | 1463 |
| LMArena French | 1370 | 1429 |
| LMArena German | 1355 | 1395 |
| LMArena Japanese | 1332 | 1324 |
| LMArena Korean | 1283 | 1358 |
| LMArena Russian | 1343 | 1387 |
| LMArena Spanish | 1388 | 1420 |
Instruction Following MiMo-V2-Flash leads
gpt-oss-120b: 69.5 (#168), MiMo-V2-Flash: 73.7 (#115)
| Benchmark | gpt-oss-120b | MiMo-V2-Flash |
|---|---|---|
| LMArena Instruction Following | 1318 | 1392 |
| IFEval | 83.6% | — |
Long Context MiMo-V2-Flash leads
gpt-oss-120b: 31.4 (#273), MiMo-V2-Flash: 43.0 (#106)
| Benchmark | gpt-oss-120b | MiMo-V2-Flash |
|---|---|---|
| LMArena Longer Query | 1319 | 1409 |
| Fiction.LiveBench | 44.4% | — |
Writing & Preference MiMo-V2-Flash leads
gpt-oss-120b: 46.7 (#213), MiMo-V2-Flash: 59.9 (#101)
| Benchmark | gpt-oss-120b | MiMo-V2-Flash |
|---|---|---|
| LMArena Text | 1366 | 1411 |
| LMArena Creative Writing | 1274 | 1375 |
| LMArena Multi-Turn | 1340 | 1404 |
| Short-Story Creative Writing | 77.1% | — |
| EQ-Bench Creative Writing | 961 | — |
| WildBench | 84.5% | — |
Frequently asked questions
Is gpt-oss-120b better than MiMo-V2-Flash?
MiMo-V2-Flash is the stronger model overall, scoring 40.8 to 36.0 on the Noometry Index. gpt-oss-120b costs 2.5× less per token, which makes it the better buy when MiMo-V2-Flash's lead doesn't matter for your workload.
Which is cheaper, gpt-oss-120b or MiMo-V2-Flash?
gpt-oss-120b is cheaper. It lists at $0.037 per million input tokens and $0.17 per million output tokens; MiMo-V2-Flash lists at $0.14 and $0.28.
Is gpt-oss-120b or MiMo-V2-Flash better for coding?
MiMo-V2-Flash scores higher on coding benchmarks: 35.8 versus 33.2 in the Noometry coding category.
Which has the bigger context window?
MiMo-V2-Flash does, with 262K tokens against 131K.
How many benchmarks do gpt-oss-120b and MiMo-V2-Flash share?
20 benchmarks have published results for both models. gpt-oss-120b has 48 scored results on Noometry and MiMo-V2-Flash has 21.