Model comparison
MiniMax-M2.7 vs o3-mini
MiniMax-M2.7 is the stronger model overall, scoring 37.7 to 36.7 on the Noometry Index.
Last verified . 21 shared benchmarks.
MiniMaxSummary
- They share 21 benchmarks with published results for both. MiniMax-M2.7 scores higher in 5 categories and o3-mini in 4 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in long context, where MiniMax-M2.7 leads 43.3 to 33.8.
- The biggest single-benchmark swing is SciCode: 47% for MiniMax-M2.7 and 39.8% for o3-mini.
- MiniMax-M2.7 is cheaper at $0.30 / $1.20 per million input/output tokens, against $1.10 / $4.40 for o3-mini.
- MiniMax-M2.7 accepts more context: 205K tokens versus 200K.
- MiniMax-M2.7 has downloadable open weights; the other is API-only.
Side by side
| MiniMax-M2.7 | o3-mini | |
|---|---|---|
| Provider | MiniMax | OpenAI |
| Noometry Index | 37.7 | 36.7 |
| Released | 2026-03-18 | 2024-12-20 |
| Weights | Open | Proprietary |
| Context window | 205K | 200K |
| Max output | 131K | 100K |
| Input $ / M tokens | $0.30 | $1.10 |
| Output $ / M tokens | $1.20 | $4.40 |
| Results tracked | 30 | 51 |
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Category by category
Coding Too close to call
MiniMax-M2.7: 41.8 (#120), o3-mini: 40.8 (#132)
| Benchmark | MiniMax-M2.7 | o3-mini |
|---|---|---|
| SciCode | 47% | 39.8% |
| WeirdML | 37% | 43.7% |
| LMArena Coding | 1454 | 1378 |
| Aider Polyglot | — | 60.4% |
| LMArena WebDev | 1398 | — |
| GSO | — | 1.3% |
| LiveBench Coding | — | 82.7% |
| CadEval | — | 54% |
| ALE-Bench | 599.25 | — |
Agentic & Tool Use o3-mini leads
MiniMax-M2.7: 25.1 (#111), o3-mini: 29.6 (#84)
| Benchmark | MiniMax-M2.7 | o3-mini |
|---|---|---|
| Terminal-Bench | 45.1% | — |
| Cybench | — | 22.5% |
| ExploitBench | 13.3% | — |
| GBAEval | 0% | — |
Reasoning MiniMax-M2.7 leads
MiniMax-M2.7: 19.7 (#253), o3-mini: 16.3 (#305)
| Benchmark | MiniMax-M2.7 | o3-mini |
|---|---|---|
| CritPt | 0.6% | 0.3% |
| LMArena Hard Prompts | 1422 | 1366 |
| Epoch Capabilities Index | 145.85 | 140.34 |
| ARC-AGI-2 | — | 3% |
| SimpleBench | — | 22.8% |
| NYT Connections (extended) | 24.7% | — |
| ARC-AGI-1 | — | 34.5% |
| Chess Puzzles | — | 17% |
| Thematic Generalization | 39.3% | — |
| LiveBench Reasoning | — | 89.6% |
| Mystery Game Puzzles | — | 7% |
| DTBench | — | 68.8% |
| LiveBench Data Analysis | — | 70.6% |
| LMCA | — | 19% |
| ForecastBench | — | 59.6 |
| LiveBench | — | 75.9% |
Math o3-mini leads
MiniMax-M2.7: 25.9 (#263), o3-mini: 28.1 (#244)
| Benchmark | MiniMax-M2.7 | o3-mini |
|---|---|---|
| LMArena Math | 1420 | 1396 |
| FrontierMath (Tiers 1-3) | — | 18.6% |
| FrontierMath Tier 4 | — | 0% |
| OTIS Mock AIME 2024-2025 | — | 76.9% |
| ProofBench | 3% | — |
| LiveBench Math | — | 77.3% |
| MATH Level 5 | — | 96.5% |
| FrontierMath (Feb 2025 set) | — | 12.4% |
| FrontierMath Tier 4 (v1) | — | 4.2% |
Knowledge Too close to call
MiniMax-M2.7: 37.7 (#152), o3-mini: 38.3 (#146)
| Benchmark | MiniMax-M2.7 | o3-mini |
|---|---|---|
| LMArena Expert | 1444 | 1364 |
| GPQA Diamond | — | 77% |
| SimpleQA Verified | — | 15.3% |
| Confabulations | — | 17.9% |
| Vectara Hallucination Rate | 12.9% | — |
Multilingual MiniMax-M2.7 leads
MiniMax-M2.7: 50.3 (#123), o3-mini: 45.7 (#164)
| Benchmark | MiniMax-M2.7 | o3-mini |
|---|---|---|
| LMArena Non-English | 1382 | 1319 |
| LMArena Chinese | 1441 | 1379 |
| LMArena French | 1421 | 1334 |
| LMArena German | 1398 | 1303 |
| LMArena Japanese | 1262 | 1286 |
| LMArena Korean | 1313 | 1314 |
| LMArena Russian | 1383 | 1304 |
| LMArena Spanish | 1403 | 1321 |
Instruction Following o3-mini leads
MiniMax-M2.7: 74.1 (#103), o3-mini: 75.1 (#72)
| Benchmark | MiniMax-M2.7 | o3-mini |
|---|---|---|
| LMArena Instruction Following | 1405 | 1337 |
| LiveBench Instruction Following | — | 84.4% |
Long Context MiniMax-M2.7 leads
MiniMax-M2.7: 43.3 (#99), o3-mini: 33.8 (#256)
| Benchmark | MiniMax-M2.7 | o3-mini |
|---|---|---|
| LMArena Longer Query | 1419 | 1343 |
| Fiction.LiveBench | — | 50% |
Writing & Preference MiniMax-M2.7 leads
MiniMax-M2.7: 58.9 (#112), o3-mini: 50.3 (#182)
| Benchmark | MiniMax-M2.7 | o3-mini |
|---|---|---|
| LMArena Text | 1405 | 1337 |
| LMArena Creative Writing | 1354 | 1286 |
| LMArena Multi-Turn | 1412 | 1320 |
| Short-Story Creative Writing | — | 61.7% |
| LiveBench Language | — | 50.7% |
Frequently asked questions
Is MiniMax-M2.7 better than o3-mini?
MiniMax-M2.7 is the stronger model overall, scoring 37.7 to 36.7 on the Noometry Index.
Which is cheaper, MiniMax-M2.7 or o3-mini?
MiniMax-M2.7 is cheaper. It lists at $0.30 per million input tokens and $1.20 per million output tokens; o3-mini lists at $1.10 and $4.40.
Is MiniMax-M2.7 or o3-mini better for coding?
They score almost the same on coding (41.8 vs 40.8); test both on your own repository before choosing.
Which has the bigger context window?
MiniMax-M2.7 does, with 205K tokens against 200K.
How many benchmarks do MiniMax-M2.7 and o3-mini share?
21 benchmarks have published results for both models. MiniMax-M2.7 has 30 scored results on Noometry and o3-mini has 51.