Model comparison
gpt-oss-20b vs MiniMax-M2.5
MiniMax-M2.5 is the stronger model overall, scoring 38.1 to 32.1 on the Noometry Index. gpt-oss-20b costs 15× less per token, which makes it the better buy when MiniMax-M2.5's lead doesn't matter for your workload.
Last verified . 21 shared benchmarks.
MiniMaxSummary
- They share 21 benchmarks with published results for both. gpt-oss-20b scores higher in 3 categories and MiniMax-M2.5 in 6 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in agentic & tool use, where MiniMax-M2.5 leads 30.2 to 9.2.
- The biggest single-benchmark swing is Terminal-Bench: 3.4% for gpt-oss-20b and 42.7% for MiniMax-M2.5.
- gpt-oss-20b is cheaper at $0.018 / $0.09 per million input/output tokens, against $0.30 / $1.20 for MiniMax-M2.5.
- MiniMax-M2.5 accepts more context: 205K tokens versus 131K.
Side by side
| gpt-oss-20b | MiniMax-M2.5 | |
|---|---|---|
| Provider | OpenAI | MiniMax |
| Noometry Index | 32.1 | 38.1 |
| Released | 2025-08-05 | 2026-02-12 |
| 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 | 33 |
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Category by category
Coding MiniMax-M2.5 leads
gpt-oss-20b: 37.1 (#185), MiniMax-M2.5: 47.9 (#53)
| Benchmark | gpt-oss-20b | MiniMax-M2.5 |
|---|---|---|
| LMArena Coding | 1307 | 1380 |
| ALE-Bench | 566.05 | 618.17 |
| SWE-bench Verified (bash only) | — | 75.8% |
| LMArena WebDev | — | 1386 |
| SWE-bench Multilingual | — | 68.3% |
| SciCode | 34.4% | — |
| WeirdML | 40.9% | — |
Agentic & Tool Use MiniMax-M2.5 leads
gpt-oss-20b: 9.2 (#146), MiniMax-M2.5: 30.2 (#70)
| Benchmark | gpt-oss-20b | MiniMax-M2.5 |
|---|---|---|
| Terminal-Bench | 3.4% | 42.7% |
| Vending-Bench 2 | — | -23.16 |
Reasoning gpt-oss-20b leads
gpt-oss-20b: 18.8 (#251), MiniMax-M2.5: 17.6 (#281)
| Benchmark | gpt-oss-20b | MiniMax-M2.5 |
|---|---|---|
| Kagi LLM Benchmark | 53.2% | 55.2% |
| LMArena Hard Prompts | 1274 | 1372 |
| Epoch Capabilities Index | 137.81 | 146.68 |
| ARC-AGI-2 | — | 4.9% |
| NYT Connections (extended) | — | 16.8% |
| ARC-AGI-1 | — | 63.7% |
| CritPt | 1.4% | — |
| Chess Puzzles | 4% | — |
| DTBench | 68% | — |
| LMCA | 14.5% | — |
Math gpt-oss-20b leads
gpt-oss-20b: 37.6 (#93), MiniMax-M2.5: 25.8 (#242)
| Benchmark | gpt-oss-20b | MiniMax-M2.5 |
|---|---|---|
| LMArena Math | 1317 | 1378 |
| OTIS Mock AIME 2024-2025 | 65.3% | — |
| ProofBench | — | 4% |
| Omni-MATH | 56.5% | — |
Knowledge MiniMax-M2.5 leads
gpt-oss-20b: 34.6 (#186), MiniMax-M2.5: 38.6 (#132)
| Benchmark | gpt-oss-20b | MiniMax-M2.5 |
|---|---|---|
| LMArena Expert | 1258 | 1378 |
| GPQA Diamond | 60.8% | — |
| MMLU-Pro | 74% | — |
| Vectara Hallucination Rate | — | 9.1% |
| GPQA (HELM) | 59.4% | — |
Multilingual MiniMax-M2.5 leads
gpt-oss-20b: 42.4 (#192), MiniMax-M2.5: 47.3 (#147)
| Benchmark | gpt-oss-20b | MiniMax-M2.5 |
|---|---|---|
| LMArena Non-English | 1269 | 1338 |
| LMArena Chinese | 1311 | 1392 |
| LMArena German | 1249 | 1361 |
| LMArena Japanese | 1245 | 1169 |
| LMArena Korean | 1239 | 1232 |
| LMArena Russian | 1279 | 1359 |
| LMArena Spanish | 1267 | 1354 |
| LMArena French | — | 1363 |
Instruction Following MiniMax-M2.5 leads
gpt-oss-20b: 62.0 (#235), MiniMax-M2.5: 71.7 (#143)
| Benchmark | gpt-oss-20b | MiniMax-M2.5 |
|---|---|---|
| LMArena Instruction Following | 1236 | 1353 |
| IFEval | 73.2% | — |
Long Context Too close to call
gpt-oss-20b: 37.9 (#203), MiniMax-M2.5: 37.4 (#211)
| Benchmark | gpt-oss-20b | MiniMax-M2.5 |
|---|---|---|
| LMArena Longer Query | 1251 | 1367 |
| CL-bench | — | 11.4% |
| CL-bench Life | — | 6.3% |
Writing & Preference MiniMax-M2.5 leads
gpt-oss-20b: 35.8 (#260), MiniMax-M2.5: 54.1 (#149)
| Benchmark | gpt-oss-20b | MiniMax-M2.5 |
|---|---|---|
| LMArena Text | 1288 | 1359 |
| LMArena Creative Writing | 1201 | 1331 |
| EQ-Bench Creative Writing | 666 | 1361 |
| LMArena Multi-Turn | 1268 | 1364 |
| WildBench | 73.7% | — |
Frequently asked questions
Is gpt-oss-20b better than MiniMax-M2.5?
MiniMax-M2.5 is the stronger model overall, scoring 38.1 to 32.1 on the Noometry Index. gpt-oss-20b costs 15× less per token, which makes it the better buy when MiniMax-M2.5's lead doesn't matter for your workload.
Which is cheaper, gpt-oss-20b or MiniMax-M2.5?
gpt-oss-20b is cheaper. It lists at $0.018 per million input tokens and $0.09 per million output tokens; MiniMax-M2.5 lists at $0.30 and $1.20.
Is gpt-oss-20b or MiniMax-M2.5 better for coding?
MiniMax-M2.5 scores higher on coding benchmarks: 47.9 versus 37.1 in the Noometry coding category.
Which has the bigger context window?
MiniMax-M2.5 does, with 205K tokens against 131K.
How many benchmarks do gpt-oss-20b and MiniMax-M2.5 share?
21 benchmarks have published results for both models. gpt-oss-20b has 34 scored results on Noometry and MiniMax-M2.5 has 33.