Model comparison
gpt-oss-20b vs MiniMax-M2.1
MiniMax-M2.1 is the stronger model overall, scoring 38.4 to 32.1 on the Noometry Index. gpt-oss-20b costs 15× less per token, which makes it the better buy when MiniMax-M2.1'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-20b scores higher in 2 categories and MiniMax-M2.1 in 7 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where MiniMax-M2.1 leads 58.5 to 35.8.
- The biggest single-benchmark swing is Terminal-Bench: 3.4% for gpt-oss-20b and 36.6% for MiniMax-M2.1.
- gpt-oss-20b is cheaper at $0.018 / $0.09 per million input/output tokens, against $0.30 / $1.20 for MiniMax-M2.1.
- MiniMax-M2.1 accepts more context: 205K tokens versus 131K.
Side by side
| gpt-oss-20b | MiniMax-M2.1 | |
|---|---|---|
| Provider | OpenAI | MiniMax |
| Noometry Index | 32.1 | 38.4 |
| Released | 2025-08-05 | 2025-12-23 |
| Weights | Open | Open |
| Context window | 131K | 205K |
| Max output | 16K | 131K |
| Input $ / M tokens | $0.018 | $0.30 |
| Output $ / M tokens | $0.09 | $1.20 |
| Results tracked | 34 | 22 |
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Category by category
Coding MiniMax-M2.1 leads
gpt-oss-20b: 37.1 (#185), MiniMax-M2.1: 40.1 (#132)
| Benchmark | gpt-oss-20b | MiniMax-M2.1 |
|---|---|---|
| LMArena Coding | 1307 | 1421 |
| ALE-Bench | 566.05 | 623.83 |
| LMArena WebDev | — | 1384 |
| SciCode | 34.4% | — |
| WeirdML | 40.9% | — |
Agentic & Tool Use MiniMax-M2.1 leads
gpt-oss-20b: 9.2 (#146), MiniMax-M2.1: 27.7 (#91)
| Benchmark | gpt-oss-20b | MiniMax-M2.1 |
|---|---|---|
| Terminal-Bench | 3.4% | 36.6% |
Reasoning gpt-oss-20b leads
gpt-oss-20b: 18.8 (#251), MiniMax-M2.1: 15.8 (#301)
| Benchmark | gpt-oss-20b | MiniMax-M2.1 |
|---|---|---|
| LMArena Hard Prompts | 1274 | 1410 |
| Kagi LLM Benchmark | 53.2% | — |
| NYT Connections (extended) | — | 11.2% |
| CritPt | 1.4% | — |
| Chess Puzzles | 4% | — |
| DTBench | 68% | — |
| LMCA | 14.5% | — |
| Epoch Capabilities Index | 137.81 | — |
Math gpt-oss-20b leads
gpt-oss-20b: 37.6 (#93), MiniMax-M2.1: 35.6 (#142)
| Benchmark | gpt-oss-20b | MiniMax-M2.1 |
|---|---|---|
| LMArena Math | 1317 | 1396 |
| OTIS Mock AIME 2024-2025 | 65.3% | — |
| Omni-MATH | 56.5% | — |
Knowledge MiniMax-M2.1 leads
gpt-oss-20b: 34.6 (#186), MiniMax-M2.1: 37.7 (#143)
| Benchmark | gpt-oss-20b | MiniMax-M2.1 |
|---|---|---|
| LMArena Expert | 1258 | 1432 |
| GPQA Diamond | 60.8% | — |
| MMLU-Pro | 74% | — |
| Vectara Hallucination Rate | — | 11.8% |
| GPQA (HELM) | 59.4% | — |
Multilingual MiniMax-M2.1 leads
gpt-oss-20b: 42.4 (#192), MiniMax-M2.1: 50.2 (#123)
| Benchmark | gpt-oss-20b | MiniMax-M2.1 |
|---|---|---|
| LMArena Non-English | 1269 | 1378 |
| LMArena Chinese | 1311 | 1430 |
| LMArena German | 1249 | 1380 |
| LMArena Japanese | 1245 | 1287 |
| LMArena Korean | 1239 | 1297 |
| LMArena Russian | 1279 | 1386 |
| LMArena Spanish | 1267 | 1396 |
| LMArena French | — | 1405 |
Instruction Following MiniMax-M2.1 leads
gpt-oss-20b: 62.0 (#235), MiniMax-M2.1: 74.0 (#108)
| Benchmark | gpt-oss-20b | MiniMax-M2.1 |
|---|---|---|
| LMArena Instruction Following | 1236 | 1399 |
| IFEval | 73.2% | — |
Long Context MiniMax-M2.1 leads
gpt-oss-20b: 37.9 (#203), MiniMax-M2.1: 43.2 (#98)
| Benchmark | gpt-oss-20b | MiniMax-M2.1 |
|---|---|---|
| LMArena Longer Query | 1251 | 1416 |
Writing & Preference MiniMax-M2.1 leads
gpt-oss-20b: 35.8 (#260), MiniMax-M2.1: 58.5 (#114)
| Benchmark | gpt-oss-20b | MiniMax-M2.1 |
|---|---|---|
| LMArena Text | 1288 | 1392 |
| LMArena Creative Writing | 1201 | 1362 |
| LMArena Multi-Turn | 1268 | 1396 |
| EQ-Bench Creative Writing | 666 | — |
| WildBench | 73.7% | — |
Frequently asked questions
Is gpt-oss-20b better than MiniMax-M2.1?
MiniMax-M2.1 is the stronger model overall, scoring 38.4 to 32.1 on the Noometry Index. gpt-oss-20b costs 15× less per token, which makes it the better buy when MiniMax-M2.1's lead doesn't matter for your workload.
Which is cheaper, gpt-oss-20b or MiniMax-M2.1?
gpt-oss-20b is cheaper. It lists at $0.018 per million input tokens and $0.09 per million output tokens; MiniMax-M2.1 lists at $0.30 and $1.20.
Is gpt-oss-20b or MiniMax-M2.1 better for coding?
MiniMax-M2.1 scores higher on coding benchmarks: 40.1 versus 37.1 in the Noometry coding category.
Which has the bigger context window?
MiniMax-M2.1 does, with 205K tokens against 131K.
How many benchmarks do gpt-oss-20b and MiniMax-M2.1 share?
18 benchmarks have published results for both models. gpt-oss-20b has 34 scored results on Noometry and MiniMax-M2.1 has 22.