Model comparison
gpt-oss-120b vs MiniMax M1
MiniMax M1 is the stronger model overall, scoring 39.7 to 36.0 on the Noometry Index. gpt-oss-120b costs 12× less per token, which makes it the better buy when MiniMax M1's lead doesn't matter for your workload.
Last verified . 18 shared benchmarks.
MiniMaxSummary
- They share 18 benchmarks with published results for both. gpt-oss-120b scores higher in 4 categories and MiniMax M1 in 4 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where gpt-oss-120b leads 50.6 to 34.8.
- The biggest single-benchmark swing is Fiction.LiveBench: 44.4% for gpt-oss-120b and 69.4% for MiniMax M1.
- gpt-oss-120b is cheaper at $0.037 / $0.17 per million input/output tokens, against $0.40 / $2.20 for MiniMax M1.
- MiniMax M1 accepts more context: 1M tokens versus 131K.
Side by side
| gpt-oss-120b | MiniMax M1 | |
|---|---|---|
| Provider | OpenAI | MiniMax |
| Noometry Index | 36.0 | 39.7 |
| Released | 2025-08-05 | 2025-06-13 |
| Weights | Open | Open |
| Context window | 131K | 1M |
| Max output | 41K | 40K |
| Input $ / M tokens | $0.037 | $0.40 |
| Output $ / M tokens | $0.17 | $2.20 |
| Results tracked | 48 | 18 |
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Category by category
Coding MiniMax M1 leads
gpt-oss-120b: 33.2 (#251), MiniMax M1: 39.2 (#149)
| Benchmark | gpt-oss-120b | MiniMax M1 |
|---|---|---|
| LMArena Coding | 1380 | 1359 |
| 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 Not comparable
gpt-oss-120b: 12.1 (#145), MiniMax M1: —
| Benchmark | gpt-oss-120b | MiniMax M1 |
|---|---|---|
| Terminal-Bench | 18.7% | — |
| APEX-Agents | 4.4% | — |
| METR Time Horizons | 56.6% | — |
| Vending-Bench 2 | -21.53 | — |
Reasoning MiniMax M1 leads
gpt-oss-120b: 19.5 (#236), MiniMax M1: 25.8 (#125)
| Benchmark | gpt-oss-120b | MiniMax M1 |
|---|---|---|
| LMArena Hard Prompts | 1364 | 1340 |
| 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 | 139.94 | — |
Math gpt-oss-120b leads
gpt-oss-120b: 50.6 (#43), MiniMax M1: 34.8 (#152)
| Benchmark | gpt-oss-120b | MiniMax M1 |
|---|---|---|
| LMArena Math | 1388 | 1360 |
| OTIS Mock AIME 2024-2025 | 88.9% | — |
| Omni-MATH | 68.8% | — |
Knowledge gpt-oss-120b leads
gpt-oss-120b: 42.3 (#89), MiniMax M1: 35.7 (#168)
| Benchmark | gpt-oss-120b | MiniMax M1 |
|---|---|---|
| LMArena Expert | 1356 | 1316 |
| 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: 48.2 (#142), MiniMax M1: 46.0 (#157)
| Benchmark | gpt-oss-120b | MiniMax M1 |
|---|---|---|
| LMArena Non-English | 1350 | 1320 |
| LMArena Chinese | 1384 | 1361 |
| LMArena French | 1370 | 1369 |
| LMArena German | 1355 | 1350 |
| LMArena Japanese | 1332 | 1220 |
| LMArena Korean | 1283 | 1263 |
| LMArena Russian | 1343 | 1330 |
| LMArena Spanish | 1388 | 1354 |
Instruction Following Too close to call
gpt-oss-120b: 69.5 (#168), MiniMax M1: 69.5 (#169)
| Benchmark | gpt-oss-120b | MiniMax M1 |
|---|---|---|
| LMArena Instruction Following | 1318 | 1312 |
| IFEval | 83.6% | — |
Long Context MiniMax M1 leads
gpt-oss-120b: 31.4 (#273), MiniMax M1: 41.4 (#136)
| Benchmark | gpt-oss-120b | MiniMax M1 |
|---|---|---|
| Fiction.LiveBench | 44.4% | 69.4% |
| LMArena Longer Query | 1319 | 1326 |
Writing & Preference MiniMax M1 leads
gpt-oss-120b: 46.7 (#213), MiniMax M1: 53.3 (#155)
| Benchmark | gpt-oss-120b | MiniMax M1 |
|---|---|---|
| LMArena Text | 1366 | 1343 |
| LMArena Creative Writing | 1274 | 1298 |
| LMArena Multi-Turn | 1340 | 1335 |
| Short-Story Creative Writing | 77.1% | — |
| EQ-Bench Creative Writing | 961 | — |
| WildBench | 84.5% | — |
Frequently asked questions
Is gpt-oss-120b better than MiniMax M1?
MiniMax M1 is the stronger model overall, scoring 39.7 to 36.0 on the Noometry Index. gpt-oss-120b costs 12× less per token, which makes it the better buy when MiniMax M1's lead doesn't matter for your workload.
Which is cheaper, gpt-oss-120b or MiniMax M1?
gpt-oss-120b is cheaper. It lists at $0.037 per million input tokens and $0.17 per million output tokens; MiniMax M1 lists at $0.40 and $2.20.
Is gpt-oss-120b or MiniMax M1 better for coding?
MiniMax M1 scores higher on coding benchmarks: 39.2 versus 33.2 in the Noometry coding category.
Which has the bigger context window?
MiniMax M1 does, with 1M tokens against 131K.
How many benchmarks do gpt-oss-120b and MiniMax M1 share?
18 benchmarks have published results for both models. gpt-oss-120b has 48 scored results on Noometry and MiniMax M1 has 18.