Model comparison
gpt-oss-120b vs MiMo-V2-Pro
MiMo-V2-Pro is the stronger model overall, scoring 43.0 to 36.3 on the Noometry Index. gpt-oss-120b costs 7.7× less per token, which makes it the better buy when MiMo-V2-Pro's lead doesn't matter for your workload.
Last verified . 18 shared benchmarks.
Summary
- They share 18 benchmarks with published results for both. gpt-oss-120b scores higher in 2 categories and MiMo-V2-Pro in 6 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where MiMo-V2-Pro leads 62.8 to 46.5.
- gpt-oss-120b is cheaper at $0.037 / $0.17 per million input/output tokens, against $0.43 / $0.87 for MiMo-V2-Pro.
- MiMo-V2-Pro accepts more context: 1.05M tokens versus 131K.
- gpt-oss-120b has downloadable open weights; the other is API-only.
Side by side
| gpt-oss-120b | MiMo-V2-Pro | |
|---|---|---|
| Provider | OpenAI | Xiaomi |
| Noometry Index | 36.3 | 43.0 |
| Released | 2025-08-05 | 2026-03-18 |
| Weights | Open | Proprietary |
| Context window | 131K | 1.05M |
| Max output | 41K | 131K |
| Input $ / M tokens | $0.037 | $0.43 |
| Output $ / M tokens | $0.17 | $0.87 |
| Results tracked | 48 | 23 |
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Category by category
Coding MiMo-V2-Pro leads
gpt-oss-120b: 33.5 (#256), MiMo-V2-Pro: 43.8 (#83)
| Benchmark | gpt-oss-120b | MiMo-V2-Pro |
|---|---|---|
| LMArena Coding | 1380 | 1476 |
| ALE-Bench | 575.62 | 785.17 |
| SWE-bench Verified (bash only) | 26% | — |
| Aider Polyglot | 41.8% | — |
| LMArena WebDev | — | 1433 |
| SciCode | 36% | — |
| WeirdML | 48.2% | — |
| AlgoTune | 1.41 | — |
Agentic & Tool Use Not comparable
gpt-oss-120b: 12.2 (#153), MiMo-V2-Pro: —
| Benchmark | gpt-oss-120b | MiMo-V2-Pro |
|---|---|---|
| Terminal-Bench | 18.7% | — |
| APEX-Agents | 4.4% | — |
| METR Time Horizons | 56.6% | — |
| Vending-Bench 2 | -21.53 | — |
Reasoning MiMo-V2-Pro leads
gpt-oss-120b: 20.0 (#245), MiMo-V2-Pro: 22.1 (#206)
| Benchmark | gpt-oss-120b | MiMo-V2-Pro |
|---|---|---|
| LMArena Hard Prompts | 1364 | 1457 |
| SimpleBench | 22.1% | — |
| Kagi LLM Benchmark | 58.6% | — |
| NYT Connections (extended) | — | 25.8% |
| CritPt | 1.1% | — |
| Chess Puzzles | 20% | — |
| Thematic Generalization | — | 45.9% |
| Mystery Game Puzzles | 2% | — |
| DTBench | 76.3% | — |
| LMCA | 22.1% | — |
| Surface Evolver Bench | 25% | — |
| Epoch Capabilities Index | 139.93 | — |
Math gpt-oss-120b leads
gpt-oss-120b: 52.5 (#50), MiMo-V2-Pro: 39.5 (#102)
| Benchmark | gpt-oss-120b | MiMo-V2-Pro |
|---|---|---|
| LMArena Math | 1389 | 1447 |
| OTIS Mock AIME 2024-2025 | 88.9% | — |
| Omni-MATH | 68.8% | — |
Knowledge Too close to call
gpt-oss-120b: 42.4 (#96), MiMo-V2-Pro: 41.4 (#111)
| Benchmark | gpt-oss-120b | MiMo-V2-Pro |
|---|---|---|
| LMArena Expert | 1356 | 1478 |
| GPQA Diamond | 75.8% | — |
| MMLU-Pro | 79.5% | — |
| Confabulations | 15.7% | — |
| Vectara Hallucination Rate | 14.2% | — |
| GPQA (HELM) | 68.4% | — |
Multilingual MiMo-V2-Pro leads
gpt-oss-120b: 48.0 (#147), MiMo-V2-Pro: 52.7 (#81)
| Benchmark | gpt-oss-120b | MiMo-V2-Pro |
|---|---|---|
| LMArena Non-English | 1351 | 1416 |
| LMArena Chinese | 1385 | 1456 |
| LMArena French | 1369 | 1469 |
| LMArena German | 1353 | 1417 |
| LMArena Japanese | 1331 | 1366 |
| LMArena Korean | 1282 | 1400 |
| LMArena Russian | 1343 | 1427 |
| LMArena Spanish | 1389 | 1457 |
Instruction Following MiMo-V2-Pro leads
gpt-oss-120b: 69.3 (#173), MiMo-V2-Pro: 76.0 (#49)
| Benchmark | gpt-oss-120b | MiMo-V2-Pro |
|---|---|---|
| LMArena Instruction Following | 1318 | 1445 |
| IFEval | 83.6% | — |
Long Context MiMo-V2-Pro leads
gpt-oss-120b: 31.4 (#278), MiMo-V2-Pro: 41.5 (#138)
| Benchmark | gpt-oss-120b | MiMo-V2-Pro |
|---|---|---|
| LMArena Longer Query | 1319 | 1455 |
| Fiction.LiveBench | 44.4% | — |
| CL-bench | — | 15.7% |
| CL-bench Life | — | 6.9% |
Writing & Preference MiMo-V2-Pro leads
gpt-oss-120b: 46.5 (#217), MiMo-V2-Pro: 62.8 (#70)
| Benchmark | gpt-oss-120b | MiMo-V2-Pro |
|---|---|---|
| LMArena Text | 1365 | 1436 |
| LMArena Creative Writing | 1275 | 1415 |
| LMArena Multi-Turn | 1340 | 1456 |
| 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-Pro?
MiMo-V2-Pro is the stronger model overall, scoring 43.0 to 36.3 on the Noometry Index. gpt-oss-120b costs 7.7× less per token, which makes it the better buy when MiMo-V2-Pro's lead doesn't matter for your workload.
Which is cheaper, gpt-oss-120b or MiMo-V2-Pro?
gpt-oss-120b is cheaper. It lists at $0.037 per million input tokens and $0.17 per million output tokens; MiMo-V2-Pro lists at $0.43 and $0.87.
Is gpt-oss-120b or MiMo-V2-Pro better for coding?
MiMo-V2-Pro scores higher on coding benchmarks: 43.8 versus 33.5 in the Noometry coding category.
Which has the bigger context window?
MiMo-V2-Pro does, with 1.05M tokens against 131K.
How many benchmarks do gpt-oss-120b and MiMo-V2-Pro share?
18 benchmarks have published results for both models. gpt-oss-120b has 48 scored results on Noometry and MiMo-V2-Pro has 23.