MiniMax M2.7 vs Qwen3 32B
Updated October 21, 2026. 32 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick Qwen3 32B for coding: it scores 40.0 on the Vibe Coding Index against 28.4 for MiniMax M2.7.
Pick MiniMax M2.7 when the context window is larger, 205K against 131K tokens, or when what matters is SciCode or Terminal-Bench Hard (Artificial Analysis run), where it leads.
- Shared results
- 32
- MiniMax M2.7 only
- 42
- Qwen3 32B only
- 10
- Like-for-like categories
- 7 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, four public coding benchmarks
Qwen3 32B
Qwen3 32B scores 40.0 against 28.4.
One benchmark behind each score
Intelligence
AA Intelligence Index
MiniMax M2.7
MiniMax M2.7 leads, 22.8 against 7.3.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only MiniMax M2.7 has a AA Agentic Index result, 15.3.
Directional only
Reasoning
GPQA Diamond
No call
Only MiniMax M2.7 has a GPQA Diamond result, 86.6%.
Directional only
Math
AIME
No call
Only MiniMax M2.7 has a AIME result, 91.0%.
Directional only
Knowledge
MMLU Pro
No call
Only MiniMax M2.7 has a MMLU Pro result, 80.4%.
Directional only
Web design
Design Arena: websites
No call
Only MiniMax M2.7 has a Design Arena: websites result, 1243.
Directional only
Long documents
Listed context window
MiniMax M2.7
MiniMax M2.7 takes 205K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 32B
MiniMax M2.7 $0.0009, Qwen3 32B $0.0002 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only MiniMax M2.7 has the number, $0.64.
Directional only
At a glance
- Coding index
- MiniMax M2.7 28.4 from 1 benchmark, Qwen3 32B 40.0 from 1 benchmark.
- Widest category
- Agentic work, 55.0 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): MiniMax M2.7 78.3% against Qwen3 32B 0.0%, 78.3 points apart on a 0 to 100 scale.
- Output price
- MiniMax M2.7 $1.20 against Qwen3 32B $0.28 per million output tokens, 4.3x apart.
- Context window
- MiniMax M2.7 takes 205K tokens, Qwen3 32B 131K.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | MiniMax M2.7 | Bars | Qwen3 32B | Ties |
|---|---|---|---|---|
| Coding | 4 | 0 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 5 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 4 | 0 | ||
| General | 14 | 0 | ||
| All shared boards | 32 | 0 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index40
| Benchmark | MiniMax M2.7 | Qwen3 32B | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 40.0% | Only Qwen3 32B measured |
| LMArena WebDev | 1399 | Not measured | Only MiniMax M2.7 measured |
| AA Coding Index | 52.6 | Not measured | Only MiniMax M2.7 measured |
| LiveCodeBench (Vals AI) | 79.9% | Not measured | Only MiniMax M2.7 measured |
| SWE-bench Verified (Vals AI) | 73.8% | Not measured | Only MiniMax M2.7 measured |
| Vibe Code Bench (Vals AI) | 11.9% | Not measured | Only MiniMax M2.7 measured |
| Code migration (Vals AI) | 8.7% | Not measured | Only MiniMax M2.7 measured |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only MiniMax M2.7 measured |
| Terminal-Bench 2.1 (Vals AI) | 48.7% | Not measured | Only MiniMax M2.7 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 54.6% | Only Qwen3 32B measured |
| SciCode | 50.1% | 36.0% | MiniMax M2.7 |
| Terminal-Bench Hard (Artificial Analysis run) | 39.4% | 3.0% | MiniMax M2.7 |
| Terminal-Bench | 45.1% | Not measured | Only MiniMax M2.7 measured |
| WeirdML | 37.0% | Not measured | Only MiniMax M2.7 measured |
| ALE-Bench | 599.3 | Not measured | Only MiniMax M2.7 measured |
| LMArena Text: coding | 1454 | 1357 | MiniMax M2.7 |
| LMArena Text: software and IT | 1446 | 1366 | MiniMax M2.7 |
| LMArena WebDev: React | 1388 | Not measured | Only MiniMax M2.7 measured |
| LMArena WebDev: HTML | 1401 | Not measured | Only MiniMax M2.7 measured |
| Neither model has a result on the other 19 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | MiniMax M2.7 | Qwen3 32B | Leader |
|---|---|---|---|
| AA Intelligence Index | 22.8 | 7.3 | MiniMax M2.7 |
| Epoch Capabilities Index | 145.8 | 138.4 | MiniMax M2.7 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | MiniMax M2.7 | Qwen3 32B | Leader |
|---|---|---|---|
| AA Agentic Index | 15.3 | Not measured | Only MiniMax M2.7 measured |
| Finance Agent (Vals AI) | 27.9% | Not measured | Only MiniMax M2.7 measured |
| Tax Agent Bench (Vals AI) | 6.9% | Not measured | Only MiniMax M2.7 measured |
| Legal Research Bench (Vals AI) | 10.6% | Not measured | Only MiniMax M2.7 measured |
| Excel Modeling Benchmark (Vals AI) | 28.4% | Not measured | Only MiniMax M2.7 measured |
| τ²-Bench Telecom | 84.8% | 29.8% | MiniMax M2.7 |
| Neither model has a result on the other 33 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | MiniMax M2.7 | Qwen3 32B | Leader |
|---|---|---|---|
| GPQA Diamond | 86.6% | Not measured | Only MiniMax M2.7 measured |
| GPQA Diamond (Artificial Analysis run) | 87.4% | 66.8% | MiniMax M2.7 |
| Humanity's Last Exam (Artificial Analysis run) | 29.6% | 7.4% | MiniMax M2.7 |
| GPQA Diamond (Epoch AI run) | Not measured | 65.7% | Only Qwen3 32B measured |
| LMArena Text: hard prompts | 1422 | 1334 | MiniMax M2.7 |
| LMArena Text: expert | 1443 | 1362 | MiniMax M2.7 |
| LMArena Text: hard prompts in English | 1441 | 1350 | MiniMax M2.7 |
| Neither model has a result on the other 14 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | MiniMax M2.7 | Qwen3 32B | Leader |
|---|---|---|---|
| AIME | 91.0% | Not measured | Only MiniMax M2.7 measured |
| AA Math Index | Not measured | 73.0 | Only Qwen3 32B measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 73.0% | Only Qwen3 32B measured |
| OTIS Mock AIME | Not measured | 66.9% | Only Qwen3 32B measured |
| ProofBench | 3.0% | Not measured | Only MiniMax M2.7 measured |
| LMArena Text: math | 1421 | 1399 | MiniMax M2.7 |
| LMArena Text: mathematical work | 1437 | 1420 | MiniMax M2.7 |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | MiniMax M2.7 | Qwen3 32B | Leader |
|---|---|---|---|
| MMLU Pro | 80.4% | Not measured | Only MiniMax M2.7 measured |
| LegalBench (Vals AI) | 84.0% | Not measured | Only MiniMax M2.7 measured |
| CaseLaw (Vals AI) | 60.9% | Not measured | Only MiniMax M2.7 measured |
| CorpFin (Vals AI) | 61.2% | Not measured | Only MiniMax M2.7 measured |
| TaxEval (Vals AI) | 66.6% | Not measured | Only MiniMax M2.7 measured |
| MedCode (Vals AI) | 34.4% | Not measured | Only MiniMax M2.7 measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 79.8% | Only Qwen3 32B measured |
| LMArena Text: science | 1416 | 1372 | MiniMax M2.7 |
| LMArena Text: medicine and healthcare | 1409 | 1368 | MiniMax M2.7 |
| LMArena Text: legal and government | 1403 | 1324 | MiniMax M2.7 |
| LMArena Text: business and finance | 1407 | 1318 | MiniMax M2.7 |
| Neither model has a result on the other 6 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | MiniMax M2.7 | Qwen3 32B | Leader |
|---|---|---|---|
| Design Arena: websites | 1243 | Not measured | Only MiniMax M2.7 measured |
| Design Arena: UI components | 1210 | Not measured | Only MiniMax M2.7 measured |
| Design Arena: game dev | 1210 | Not measured | Only MiniMax M2.7 measured |
| Design Arena: data viz | 1233 | Not measured | Only MiniMax M2.7 measured |
| Design Arena: 3D | 1195 | Not measured | Only MiniMax M2.7 measured |
| Design Arena: all code categories | 1233 | Not measured | Only MiniMax M2.7 measured |
| Design Arena: SVG | 1136 | Not measured | Only MiniMax M2.7 measured |
| Design Arena: ASCII art | 1136 | Not measured | Only MiniMax M2.7 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index140
| Benchmark | MiniMax M2.7 | Qwen3 32B | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | 79.9% | Not measured | Only MiniMax M2.7 measured |
| IFBench | 75.7% | 36.3% | MiniMax M2.7 |
| Long context reasoning (AA-LCR) | 78.3% | 0.0% | MiniMax M2.7 |
| DTBench | Not measured | 67.5% | Only Qwen3 32B measured |
| LMCA | Not measured | 17.3% | Only Qwen3 32B measured |
| Fiction.LiveBench | Not measured | 74.2% | Only Qwen3 32B measured |
| ExploitBench | 13.3% | Not measured | Only MiniMax M2.7 measured |
| LMArena Text | 1405 | 1340 | MiniMax M2.7 |
| LMArena Text: instruction following | 1405 | 1305 | MiniMax M2.7 |
| LMArena Text: creative writing | 1354 | 1297 | MiniMax M2.7 |
| LMArena Text: English | 1426 | 1359 | MiniMax M2.7 |
| LMArena Text: non-English | 1382 | 1317 | MiniMax M2.7 |
| LMArena Text: Chinese | 1442 | 1357 | MiniMax M2.7 |
| LMArena Text: German | 1398 | 1341 | MiniMax M2.7 |
| LMArena Text: Japanese | 1260 | Not measured | Only MiniMax M2.7 measured |
| LMArena Text: Korean | 1314 | Not measured | Only MiniMax M2.7 measured |
| LMArena Text: French | 1424 | Not measured | Only MiniMax M2.7 measured |
| LMArena Text: Spanish | 1402 | Not measured | Only MiniMax M2.7 measured |
| LMArena Text: Russian | 1383 | 1311 | MiniMax M2.7 |
| LMArena Text: Polish | 1387 | Not measured | Only MiniMax M2.7 measured |
| LMArena Text: multi-turn | 1412 | 1331 | MiniMax M2.7 |
| LMArena Text: longer queries | 1418 | 1327 | MiniMax M2.7 |
| LMArena Text: writing and language | 1376 | 1298 | MiniMax M2.7 |
| LMArena Text: entertainment and media | 1360 | 1279 | MiniMax M2.7 |
| Neither model has a result on the other 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | MiniMax M2.7 | Qwen3 32B | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
Qwen3 32B costs less
Repository review
50K input + 3K output tokens
Qwen3 32B costs less
Agent loop
200K input + 10K output tokens
Qwen3 32B costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | MiniMax M2.7 | Qwen3 32B |
|---|---|---|
| Maker | MiniMax | Qwen |
| Weights | Open weights | Open weights |
| Released | March 18, 2026 | April 28, 2025 |
| Context window | 205K | 131K |
| Input $/Mtok | $0.30 | $0.08 |
| Output $/Mtok | $1.20 | $0.28 |
| Coding index | 28.4 | 40.0 |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | $0.64 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is MiniMax M2.7 better than Qwen3 32B for coding?
Pick Qwen3 32B for coding: it scores 40.0 on the Vibe Coding Index against 28.4 for MiniMax M2.7. Pick MiniMax M2.7 when the context window is larger, 205K against 131K tokens, or when what matters is SciCode or Terminal-Bench Hard (Artificial Analysis run), where it leads.
Which is cheaper, MiniMax M2.7 or Qwen3 32B?
MiniMax M2.7 costs $0.30 per million input tokens and $1.20 per million output tokens; Qwen3 32B costs $0.08 and $0.28. Cost per solved task is known for MiniMax M2.7 only.
Which has the bigger context window, MiniMax M2.7 or Qwen3 32B?
MiniMax M2.7, with 205K tokens against 131K.
How many benchmarks is the MiniMax M2.7 vs Qwen3 32B comparison based on?
32 benchmarks have a result for both models as of October 21, 2026. MiniMax M2.7's coding index rests on 1 independent benchmark and Qwen3 32B's on 1. Where only one model has a result, the page says so instead of naming a winner.