Model comparison
gpt-oss-120b vs MiniMax-M2
MiniMax-M2 is the stronger model overall, scoring 37.4 to 36.3 on the Noometry Index. gpt-oss-120b costs 7.5× less per token, which makes it the better buy when MiniMax-M2's lead doesn't matter for your workload.
Last verified . 19 shared benchmarks.
MiniMaxSummary
- They share 19 benchmarks with published results for both. gpt-oss-120b scores higher in 4 categories and MiniMax-M2 in 5 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where gpt-oss-120b leads 52.5 to 37.3.
- The biggest single-benchmark swing is SWE-bench Verified (bash only): 26% for gpt-oss-120b and 61% for MiniMax-M2.
- gpt-oss-120b is cheaper at $0.037 / $0.17 per million input/output tokens, against $0.30 / $1.20 for MiniMax-M2.
- MiniMax-M2 accepts more context: 205K tokens versus 131K.
Side by side
| gpt-oss-120b | MiniMax-M2 | |
|---|---|---|
| Provider | OpenAI | MiniMax |
| Noometry Index | 36.3 | 37.4 |
| Released | 2025-08-05 | 2025-10-27 |
| Weights | Open | Open |
| Context window | 131K | 205K |
| Max output | 41K | 131K |
| Input $ / M tokens | $0.037 | $0.30 |
| Output $ / M tokens | $0.17 | $1.20 |
| Results tracked | 48 | 21 |
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Category by category
Coding MiniMax-M2 leads
gpt-oss-120b: 33.5 (#256), MiniMax-M2: 39.3 (#159)
| Benchmark | gpt-oss-120b | MiniMax-M2 |
|---|---|---|
| SWE-bench Verified (bash only) | 26% | 61% |
| LMArena Coding | 1380 | 1370 |
| Aider Polyglot | 41.8% | — |
| LMArena WebDev | — | 1297 |
| SciCode | 36% | — |
| WeirdML | 48.2% | — |
| ALE-Bench | 575.62 | — |
| AlgoTune | 1.41 | — |
Agentic & Tool Use MiniMax-M2 leads
gpt-oss-120b: 12.2 (#153), MiniMax-M2: 25.1 (#109)
| Benchmark | gpt-oss-120b | MiniMax-M2 |
|---|---|---|
| Terminal-Bench | 18.7% | 30% |
| Vending-Bench 2 | -21.53 | 160.6 |
| APEX-Agents | 4.4% | — |
| METR Time Horizons | 56.6% | — |
Reasoning Too close to call
gpt-oss-120b: 20.0 (#245), MiniMax-M2: 19.4 (#258)
| Benchmark | gpt-oss-120b | MiniMax-M2 |
|---|---|---|
| Kagi LLM Benchmark | 58.6% | 57.8% |
| LMArena Hard Prompts | 1364 | 1357 |
| SimpleBench | 22.1% | — |
| NYT Connections (extended) | — | 14.8% |
| 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 leads
gpt-oss-120b: 52.5 (#50), MiniMax-M2: 37.3 (#160)
| Benchmark | gpt-oss-120b | MiniMax-M2 |
|---|---|---|
| LMArena Math | 1389 | 1352 |
| OTIS Mock AIME 2024-2025 | 88.9% | — |
| Omni-MATH | 68.8% | — |
Knowledge gpt-oss-120b leads
gpt-oss-120b: 42.4 (#96), MiniMax-M2: 37.0 (#163)
| Benchmark | gpt-oss-120b | MiniMax-M2 |
|---|---|---|
| LMArena Expert | 1356 | 1337 |
| GPQA Diamond | 75.8% | — |
| MMLU-Pro | 79.5% | — |
| Confabulations | 15.7% | — |
| Vectara Hallucination Rate | 14.2% | — |
| GPQA (HELM) | 68.4% | — |
Multilingual gpt-oss-120b leads
gpt-oss-120b: 48.0 (#147), MiniMax-M2: 45.3 (#171)
| Benchmark | gpt-oss-120b | MiniMax-M2 |
|---|---|---|
| LMArena Non-English | 1351 | 1313 |
| LMArena Chinese | 1385 | 1366 |
| LMArena French | 1369 | 1335 |
| LMArena German | 1353 | 1355 |
| LMArena Russian | 1343 | 1331 |
| LMArena Spanish | 1389 | 1326 |
| LMArena Japanese | 1331 | — |
| LMArena Korean | 1282 | — |
Instruction Following Too close to call
gpt-oss-120b: 69.3 (#173), MiniMax-M2: 70.2 (#166)
| Benchmark | gpt-oss-120b | MiniMax-M2 |
|---|---|---|
| LMArena Instruction Following | 1318 | 1328 |
| IFEval | 83.6% | — |
Long Context MiniMax-M2 leads
gpt-oss-120b: 31.4 (#278), MiniMax-M2: 40.5 (#153)
| Benchmark | gpt-oss-120b | MiniMax-M2 |
|---|---|---|
| LMArena Longer Query | 1319 | 1331 |
| Fiction.LiveBench | 44.4% | — |
Writing & Preference MiniMax-M2 leads
gpt-oss-120b: 46.5 (#217), MiniMax-M2: 53.0 (#162)
| Benchmark | gpt-oss-120b | MiniMax-M2 |
|---|---|---|
| LMArena Text | 1365 | 1340 |
| LMArena Creative Writing | 1275 | 1286 |
| LMArena Multi-Turn | 1340 | 1361 |
| Short-Story Creative Writing | 77.1% | — |
| EQ-Bench Creative Writing | 961 | — |
| WildBench | 84.5% | — |
Frequently asked questions
Is gpt-oss-120b better than MiniMax-M2?
MiniMax-M2 is the stronger model overall, scoring 37.4 to 36.3 on the Noometry Index. gpt-oss-120b costs 7.5× less per token, which makes it the better buy when MiniMax-M2's lead doesn't matter for your workload.
Which is cheaper, gpt-oss-120b or MiniMax-M2?
gpt-oss-120b is cheaper. It lists at $0.037 per million input tokens and $0.17 per million output tokens; MiniMax-M2 lists at $0.30 and $1.20.
Is gpt-oss-120b or MiniMax-M2 better for coding?
MiniMax-M2 scores higher on coding benchmarks: 39.3 versus 33.5 in the Noometry coding category.
Which has the bigger context window?
MiniMax-M2 does, with 205K tokens against 131K.
How many benchmarks do gpt-oss-120b and MiniMax-M2 share?
19 benchmarks have published results for both models. gpt-oss-120b has 48 scored results on Noometry and MiniMax-M2 has 21.