Model comparison
MiniMax-M2.7 vs Qwen3-30B-A3B
Qwen3-30B-A3B is the stronger model overall, scoring 38.5 to 37.4 on the Noometry Index.
Last verified . 21 shared benchmarks.
MiniMax
Alibaba (Qwen)Summary
- They share 21 benchmarks with published results for both. MiniMax-M2.7 scores higher in 5 categories and Qwen3-30B-A3B in 4 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in long context, where MiniMax-M2.7 leads 43.3 to 31.0.
- The biggest single-benchmark swing is SciCode: 47% for MiniMax-M2.7 and 33.3% for Qwen3-30B-A3B.
- Qwen3-30B-A3B is cheaper at $0.12 / $0.50 per million input/output tokens, against $0.30 / $1.20 for MiniMax-M2.7.
- MiniMax-M2.7 accepts more context: 205K tokens versus 41K.
Side by side
| MiniMax-M2.7 | Qwen3-30B-A3B | |
|---|---|---|
| Provider | MiniMax | Alibaba (Qwen) |
| Noometry Index | 37.4 | 38.5 |
| Released | 2026-03-18 | 2025-04-28 |
| Weights | Open | Open |
| Context window | 205K | 41K |
| Max output | 131K | 16K |
| Input $ / M tokens | $0.30 | $0.12 |
| Output $ / M tokens | $1.20 | $0.50 |
| Results tracked | 30 | 32 |
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Category by category
Coding MiniMax-M2.7 leads
MiniMax-M2.7: 41.4 (#115), Qwen3-30B-A3B: 37.0 (#188)
| Benchmark | MiniMax-M2.7 | Qwen3-30B-A3B |
|---|---|---|
| SciCode | 47% | 33.3% |
| WeirdML | 37% | 29.8% |
| LMArena Coding | 1454 | 1416 |
| LMArena WebDev | 1397 | — |
| ALE-Bench | 599.25 | — |
Agentic & Tool Use Qwen3-30B-A3B leads
MiniMax-M2.7: 24.9 (#103), Qwen3-30B-A3B: 29.5 (#75)
| Benchmark | MiniMax-M2.7 | Qwen3-30B-A3B |
|---|---|---|
| Terminal-Bench | 45.1% | — |
| Berkeley Function Calling Leaderboard | — | 41.4% |
| ExploitBench | 13.3% | — |
| GBAEval | 0% | — |
Reasoning Qwen3-30B-A3B leads
MiniMax-M2.7: 19.0 (#248), Qwen3-30B-A3B: 21.6 (#193)
| Benchmark | MiniMax-M2.7 | Qwen3-30B-A3B |
|---|---|---|
| CritPt | 0.6% | 0.3% |
| LMArena Hard Prompts | 1422 | 1398 |
| Epoch Capabilities Index | 145.84 | 139.63 |
| Kagi LLM Benchmark | — | 54.9% |
| NYT Connections (extended) | 24.7% | — |
| Chess Puzzles | — | 8% |
| Thematic Generalization | 39.3% | — |
| DTBench | — | 69.3% |
| LMCA | — | 22.4% |
Math Qwen3-30B-A3B leads
MiniMax-M2.7: 24.9 (#254), Qwen3-30B-A3B: 36.0 (#133)
| Benchmark | MiniMax-M2.7 | Qwen3-30B-A3B |
|---|---|---|
| LMArena Math | 1420 | 1394 |
| MathArena Final-Answer Competitions | — | 47.8% |
| OTIS Mock AIME 2024-2025 | — | 70.3% |
| ProofBench | 3% | — |
Knowledge Qwen3-30B-A3B leads
MiniMax-M2.7: 37.1 (#151), Qwen3-30B-A3B: 41.7 (#96)
| Benchmark | MiniMax-M2.7 | Qwen3-30B-A3B |
|---|---|---|
| LMArena Expert | 1443 | 1395 |
| GPQA Diamond | — | 70.1% |
| Confabulations | — | 12.3% |
| Vectara Hallucination Rate | 12.9% | — |
Multilingual Too close to call
MiniMax-M2.7: 50.5 (#120), Qwen3-30B-A3B: 49.8 (#126)
| Benchmark | MiniMax-M2.7 | Qwen3-30B-A3B |
|---|---|---|
| LMArena Non-English | 1382 | 1372 |
| LMArena Chinese | 1439 | 1434 |
| LMArena French | 1422 | 1419 |
| LMArena German | 1398 | 1379 |
| LMArena Japanese | 1261 | 1338 |
| LMArena Korean | 1313 | 1333 |
| LMArena Russian | 1383 | 1369 |
| LMArena Spanish | 1403 | 1403 |
Instruction Following MiniMax-M2.7 leads
MiniMax-M2.7: 74.2 (#101), Qwen3-30B-A3B: 72.2 (#137)
| Benchmark | MiniMax-M2.7 | Qwen3-30B-A3B |
|---|---|---|
| LMArena Instruction Following | 1404 | 1364 |
Long Context MiniMax-M2.7 leads
MiniMax-M2.7: 43.3 (#94), Qwen3-30B-A3B: 31.0 (#278)
| Benchmark | MiniMax-M2.7 | Qwen3-30B-A3B |
|---|---|---|
| LMArena Longer Query | 1419 | 1379 |
| Fiction.LiveBench | — | 40.6% |
Writing & Preference MiniMax-M2.7 leads
MiniMax-M2.7: 59.1 (#107), Qwen3-30B-A3B: 55.9 (#138)
| Benchmark | MiniMax-M2.7 | Qwen3-30B-A3B |
|---|---|---|
| LMArena Text | 1404 | 1384 |
| LMArena Creative Writing | 1354 | 1318 |
| LMArena Multi-Turn | 1413 | 1378 |
| Short-Story Creative Writing | — | 75.3% |
Frequently asked questions
Is MiniMax-M2.7 better than Qwen3-30B-A3B?
Qwen3-30B-A3B is the stronger model overall, scoring 38.5 to 37.4 on the Noometry Index.
Which is cheaper, MiniMax-M2.7 or Qwen3-30B-A3B?
Qwen3-30B-A3B is cheaper. It lists at $0.12 per million input tokens and $0.50 per million output tokens; MiniMax-M2.7 lists at $0.30 and $1.20.
Is MiniMax-M2.7 or Qwen3-30B-A3B better for coding?
MiniMax-M2.7 scores higher on coding benchmarks: 41.4 versus 37.0 in the Noometry coding category.
Which has the bigger context window?
MiniMax-M2.7 does, with 205K tokens against 41K.
How many benchmarks do MiniMax-M2.7 and Qwen3-30B-A3B share?
21 benchmarks have published results for both models. MiniMax-M2.7 has 30 scored results on Noometry and Qwen3-30B-A3B has 32.