Model comparison
MiniMax-M2.7 vs Qwen2.5-Coder-32B
MiniMax-M2.7 is the stronger model overall, scoring 37.7 to 33.4 on the Noometry Index.
Last verified . 13 shared benchmarks.
Summary
- They share 13 benchmarks with published results for both. MiniMax-M2.7 scores higher in 6 categories and Qwen2.5-Coder-32B in 2 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in coding, where MiniMax-M2.7 leads 41.8 to 22.6.
- MiniMax-M2.7 is cheaper at $0.30 / $1.20 per million input/output tokens, against $0.66 / $1 for Qwen2.5-Coder-32B.
- MiniMax-M2.7 accepts more context: 205K tokens versus 33K.
Side by side
| MiniMax-M2.7 | Qwen2.5-Coder-32B | |
|---|---|---|
| Provider | MiniMax | Alibaba (Qwen) |
| Noometry Index | 37.7 | 33.4 |
| Released | 2026-03-18 | 2024-09-18 |
| Weights | Open | Open |
| Context window | 205K | 33K |
| Max output | 131K | 29K |
| Input $ / M tokens | $0.30 | $0.66 |
| Output $ / M tokens | $1.20 | $1 |
| Results tracked | 30 | 31 |
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Category by category
Coding MiniMax-M2.7 leads
MiniMax-M2.7: 41.8 (#120), Qwen2.5-Coder-32B: 22.6 (#333)
| Benchmark | MiniMax-M2.7 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Coding | 1454 | 1276 |
| SWE-bench Verified (bash only) | — | 9% |
| Aider Polyglot | — | 16.4% |
| LMArena WebDev | 1398 | — |
| SciCode | 47% | — |
| WeirdML | 37% | — |
| BigCodeBench Instruct | — | 49% |
| LiveBench Coding | — | 56.9% |
| BigCodeBench Complete | — | 58% |
| ALE-Bench | 599.25 | — |
| HumanEval+ | — | 87.2% |
| MBPP+ | — | 77% |
Agentic & Tool Use Not comparable
MiniMax-M2.7: 25.1 (#111), Qwen2.5-Coder-32B: —
| Benchmark | MiniMax-M2.7 | Qwen2.5-Coder-32B |
|---|---|---|
| Terminal-Bench | 45.1% | — |
| ExploitBench | 13.3% | — |
| GBAEval | 0% | — |
Reasoning Qwen2.5-Coder-32B leads
MiniMax-M2.7: 19.7 (#253), Qwen2.5-Coder-32B: 21.2 (#225)
| Benchmark | MiniMax-M2.7 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Hard Prompts | 1422 | 1251 |
| Epoch Capabilities Index | 145.85 | 119.49 |
| NYT Connections (extended) | 24.7% | — |
| CritPt | 0.6% | — |
| Thematic Generalization | 39.3% | — |
| LiveBench Reasoning | — | 42.1% |
| LiveBench Data Analysis | — | 49.9% |
| HellaSwag | — | 83% |
| LiveBench | — | 46.2% |
| WinoGrande | — | 80.8% |
Math Qwen2.5-Coder-32B leads
MiniMax-M2.7: 25.9 (#263), Qwen2.5-Coder-32B: 33.3 (#204)
| Benchmark | MiniMax-M2.7 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Math | 1420 | 1251 |
| ProofBench | 3% | — |
| LiveBench Math | — | 46.6% |
| GSM8K | — | 93% |
Knowledge MiniMax-M2.7 leads
MiniMax-M2.7: 37.7 (#152), Qwen2.5-Coder-32B: 33.4 (#203)
| Benchmark | MiniMax-M2.7 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Expert | 1444 | 1221 |
| Vectara Hallucination Rate | 12.9% | — |
| ARC (AI2) Challenge | — | 70.5% |
| MMLU | — | 79.1% |
Multilingual MiniMax-M2.7 leads
MiniMax-M2.7: 50.3 (#123), Qwen2.5-Coder-32B: 37.8 (#235)
| Benchmark | MiniMax-M2.7 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Non-English | 1382 | 1205 |
| LMArena Chinese | 1441 | 1222 |
| LMArena Russian | 1383 | 1228 |
| LMArena French | 1421 | — |
| LMArena German | 1398 | — |
| LMArena Japanese | 1262 | — |
| LMArena Korean | 1313 | — |
| LMArena Spanish | 1403 | — |
Instruction Following MiniMax-M2.7 leads
MiniMax-M2.7: 74.1 (#103), Qwen2.5-Coder-32B: 61.4 (#245)
| Benchmark | MiniMax-M2.7 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Instruction Following | 1405 | 1223 |
| LiveBench Instruction Following | — | 58.7% |
Long Context MiniMax-M2.7 leads
MiniMax-M2.7: 43.3 (#99), Qwen2.5-Coder-32B: 38.0 (#208)
| Benchmark | MiniMax-M2.7 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Longer Query | 1419 | 1251 |
Writing & Preference MiniMax-M2.7 leads
MiniMax-M2.7: 58.9 (#112), Qwen2.5-Coder-32B: 41.6 (#240)
| Benchmark | MiniMax-M2.7 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Text | 1405 | 1230 |
| LMArena Creative Writing | 1354 | 1174 |
| LMArena Multi-Turn | 1412 | 1222 |
| LiveBench Language | — | 23.3% |
Frequently asked questions
Is MiniMax-M2.7 better than Qwen2.5-Coder-32B?
MiniMax-M2.7 is the stronger model overall, scoring 37.7 to 33.4 on the Noometry Index.
Which is cheaper, MiniMax-M2.7 or Qwen2.5-Coder-32B?
MiniMax-M2.7 is cheaper. It lists at $0.30 per million input tokens and $1.20 per million output tokens; Qwen2.5-Coder-32B lists at $0.66 and $1.
Is MiniMax-M2.7 or Qwen2.5-Coder-32B better for coding?
MiniMax-M2.7 scores higher on coding benchmarks: 41.8 versus 22.6 in the Noometry coding category.
Which has the bigger context window?
MiniMax-M2.7 does, with 205K tokens against 33K.
How many benchmarks do MiniMax-M2.7 and Qwen2.5-Coder-32B share?
13 benchmarks have published results for both models. MiniMax-M2.7 has 30 scored results on Noometry and Qwen2.5-Coder-32B has 31.