Model comparison
gpt-oss-120b vs MiMo-V2.6-Flash
MiMo-V2.6-Flash is the stronger model overall, scoring 48.5 to 36.3 on the Noometry Index. gpt-oss-120b costs 2.5× less per token, which makes it the better buy when MiMo-V2.6-Flash's lead doesn't matter for your workload.
Last verified . 16 shared benchmarks.
Summary
- They share 16 benchmarks with published results for both. gpt-oss-120b scores higher in 2 categories and MiMo-V2.6-Flash in 6 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in coding, where MiMo-V2.6-Flash leads 53.4 to 33.5.
- The biggest single-benchmark swing is SciCode: 36% for gpt-oss-120b and 51.3% for MiMo-V2.6-Flash.
- gpt-oss-120b is cheaper at $0.037 / $0.17 per million input/output tokens, against $0.14 / $0.28 for MiMo-V2.6-Flash.
- MiMo-V2.6-Flash accepts more context: 1.05M tokens versus 131K.
Side by side
| gpt-oss-120b | MiMo-V2.6-Flash | |
|---|---|---|
| Provider | OpenAI | Xiaomi |
| Noometry Index | 36.3 | 48.5 |
| Released | 2025-08-05 | 2026-09-21 |
| Weights | Open | Open |
| Context window | 131K | 1.05M |
| Max output | 41K | 131K |
| Input $ / M tokens | $0.037 | $0.14 |
| Output $ / M tokens | $0.17 | $0.28 |
| Results tracked | 48 | 19 |
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Category by category
Coding MiMo-V2.6-Flash leads
gpt-oss-120b: 33.5 (#256), MiMo-V2.6-Flash: 53.4 (#30)
| Benchmark | gpt-oss-120b | MiMo-V2.6-Flash |
|---|---|---|
| SciCode | 36% | 51.3% |
| LMArena Coding | 1380 | 1504 |
| SWE-bench Verified (bash only) | 26% | — |
| Aider Polyglot | 41.8% | — |
| LMArena WebDev | — | 1637 |
| WeirdML | 48.2% | — |
| ALE-Bench | 575.62 | — |
| AlgoTune | 1.41 | — |
Agentic & Tool Use Not comparable
gpt-oss-120b: 12.2 (#153), MiMo-V2.6-Flash: —
| Benchmark | gpt-oss-120b | MiMo-V2.6-Flash |
|---|---|---|
| Terminal-Bench | 18.7% | — |
| APEX-Agents | 4.4% | — |
| METR Time Horizons | 56.6% | — |
| Vending-Bench 2 | -21.53 | — |
Reasoning MiMo-V2.6-Flash leads
gpt-oss-120b: 20.0 (#245), MiMo-V2.6-Flash: 36.5 (#66)
| Benchmark | gpt-oss-120b | MiMo-V2.6-Flash |
|---|---|---|
| CritPt | 1.1% | 12% |
| LMArena Hard Prompts | 1364 | 1482 |
| 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 | 139.93 | — |
Math Too close to call
gpt-oss-120b: 52.5 (#50), MiMo-V2.6-Flash: 51.9 (#52)
| Benchmark | gpt-oss-120b | MiMo-V2.6-Flash |
|---|---|---|
| LMArena Math | 1389 | 1468 |
| OTIS Mock AIME 2024-2025 | 88.9% | — |
| ProofBench | — | 63% |
| Omni-MATH | 68.8% | — |
Knowledge Too close to call
gpt-oss-120b: 42.4 (#96), MiMo-V2.6-Flash: 42.2 (#99)
| Benchmark | gpt-oss-120b | MiMo-V2.6-Flash |
|---|---|---|
| LMArena Expert | 1356 | 1501 |
| GPQA Diamond | 75.8% | — |
| MMLU-Pro | 79.5% | — |
| Confabulations | 15.7% | — |
| Vectara Hallucination Rate | 14.2% | — |
| GPQA (HELM) | 68.4% | — |
Multimodal Not comparable
gpt-oss-120b: —, MiMo-V2.6-Flash: 40.5 (#47)
| Benchmark | gpt-oss-120b | MiMo-V2.6-Flash |
|---|---|---|
| LMArena Vision | — | 1259 |
Multilingual MiMo-V2.6-Flash leads
gpt-oss-120b: 48.0 (#147), MiMo-V2.6-Flash: 54.0 (#51)
| Benchmark | gpt-oss-120b | MiMo-V2.6-Flash |
|---|---|---|
| LMArena Non-English | 1351 | 1434 |
| LMArena Chinese | 1385 | 1511 |
| LMArena French | 1369 | 1475 |
| LMArena Russian | 1343 | 1409 |
| LMArena Spanish | 1389 | 1456 |
| LMArena German | 1353 | — |
| LMArena Japanese | 1331 | — |
| LMArena Korean | 1282 | — |
Instruction Following MiMo-V2.6-Flash leads
gpt-oss-120b: 69.3 (#173), MiMo-V2.6-Flash: 76.8 (#35)
| Benchmark | gpt-oss-120b | MiMo-V2.6-Flash |
|---|---|---|
| LMArena Instruction Following | 1318 | 1463 |
| IFEval | 83.6% | — |
Long Context MiMo-V2.6-Flash leads
gpt-oss-120b: 31.4 (#278), MiMo-V2.6-Flash: 44.8 (#57)
| Benchmark | gpt-oss-120b | MiMo-V2.6-Flash |
|---|---|---|
| LMArena Longer Query | 1319 | 1463 |
| Fiction.LiveBench | 44.4% | — |
Writing & Preference MiMo-V2.6-Flash leads
gpt-oss-120b: 46.5 (#217), MiMo-V2.6-Flash: 63.1 (#67)
| Benchmark | gpt-oss-120b | MiMo-V2.6-Flash |
|---|---|---|
| LMArena Text | 1365 | 1455 |
| LMArena Creative Writing | 1275 | 1400 |
| LMArena Multi-Turn | 1340 | 1451 |
| Short-Story Creative Writing | 77.1% | — |
| EQ-Bench Creative Writing | 961 | — |
| WildBench | 84.5% | — |
Frequently asked questions
Is gpt-oss-120b better than MiMo-V2.6-Flash?
MiMo-V2.6-Flash is the stronger model overall, scoring 48.5 to 36.3 on the Noometry Index. gpt-oss-120b costs 2.5× less per token, which makes it the better buy when MiMo-V2.6-Flash's lead doesn't matter for your workload.
Which is cheaper, gpt-oss-120b or MiMo-V2.6-Flash?
gpt-oss-120b is cheaper. It lists at $0.037 per million input tokens and $0.17 per million output tokens; MiMo-V2.6-Flash lists at $0.14 and $0.28.
Is gpt-oss-120b or MiMo-V2.6-Flash better for coding?
MiMo-V2.6-Flash scores higher on coding benchmarks: 53.4 versus 33.5 in the Noometry coding category.
Which has the bigger context window?
MiMo-V2.6-Flash does, with 1.05M tokens against 131K.
How many benchmarks do gpt-oss-120b and MiMo-V2.6-Flash share?
16 benchmarks have published results for both models. gpt-oss-120b has 48 scored results on Noometry and MiMo-V2.6-Flash has 19.