MiniMax M2 vs Qwen3.7 Max
Qwen3.7 Max leads MiniMax M2 on the Artificial Analysis Intelligence Index, 29.5 to 18.6, and MiniMax M2 costs 4.3x less per output token ($1.02 vs $4.425 per million). Updated October 22, 2026; 16 benchmarks have a result for both.
MiniMax M2 or Qwen3.7 Max: which to pick
Only Qwen3.7 Max has a coding score, 54.1 on the Vibe Coding Index; MiniMax M2 has none, so the two cannot be ranked on coding.
Pick MiniMax M2 for output tokens 4.3x cheaper ($1.02 vs $4.425 per million) and a lower cost per solved task ($0.702 against $4.69).
- Shared results
- 16
- MiniMax M2 only
- 34
- Qwen3.7 Max only
- 43
- Like-for-like categories
- 6 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
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, six public coding benchmarks
No call
Only Qwen3.7 Max has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Qwen3.7 Max
Qwen3.7 Max leads, 29.5 against 18.6.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Qwen3.7 Max has a AA Agentic Index result, 22.5.
Directional only
Reasoning
GPQA Diamond
No call
Only Qwen3.7 Max has a GPQA Diamond result, 90.2%.
Directional only
Knowledge
MMLU Pro
No call
Only Qwen3.7 Max has a MMLU Pro result, 89.3%.
Directional only
Web design
Design Arena: websites
Qwen3.7 Max
Qwen3.7 Max leads, 1274 against 1143.
Same board, both measured
Long documents
Listed context window
Qwen3.7 Max
Qwen3.7 Max takes 1M tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
MiniMax M2
MiniMax M2 $0.0008, Qwen3.7 Max $0.0037 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
MiniMax M2
MiniMax M2 $0.702, Qwen3.7 Max $4.69.
Published attempt cost and pass rate
At a glance
- Coding index
- MiniMax M2 not benchmarked, Qwen3.7 Max 54.1 from 3 benchmarks.
- Widest category
- Coding, 30.1 points apart on average over 3 shared benchmarks.
- Widest single gap
- LMArena WebDev: HTML: MiniMax M2 1306 against Qwen3.7 Max 1512, 34.2 points apart on a 0 to 100 scale.
- Output price
- MiniMax M2 $1.02 against Qwen3.7 Max $4.425 per million output tokens, 4.3x apart.
- Context window
- MiniMax M2 takes 205K tokens, Qwen3.7 Max 1M.
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 | Bars | Qwen3.7 Max | Ties |
|---|---|---|---|---|
| Coding | 0 | 3 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 2 | ||
| Web design | 0 | 7 | ||
| General | 0 | 2 | ||
| All shared boards | 0 | 16 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
| Benchmark | MiniMax M2 | Qwen3.7 Max | Leader |
|---|---|---|---|
| SWE-bench Verified | 61.0% | Not measured | Only MiniMax M2 measured |
| LMArena WebDev | 1297 | 1515 | Qwen3.7 Max |
| AA Coding Index | Not measured | 66.0 | Only Qwen3.7 Max measured |
| LiveCodeBench (Vals AI) | Not measured | 87.1% | Only Qwen3.7 Max measured |
| SWE-bench Verified (Vals AI) | Not measured | 68.8% | Only Qwen3.7 Max measured |
| Vibe Code Bench (Vals AI) | Not measured | 47.7% | Only Qwen3.7 Max measured |
| Code migration (Vals AI) | Not measured | 13.1% | Only Qwen3.7 Max measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 61.0% | Only Qwen3.7 Max measured |
| LiveCodeBench (Artificial Analysis run) | 82.6% | Not measured | Only MiniMax M2 measured |
| SciCode | Not measured | 49.5% | Only Qwen3.7 Max measured |
| Terminal-Bench Hard (Artificial Analysis run) | 25.8% | 50.8% | Qwen3.7 Max |
| Terminal-Bench | 30.0% | Not measured | Only MiniMax M2 measured |
| ALE-Bench | Not measured | 1189.4 | Only Qwen3.7 Max measured |
| LMArena Text: coding | 1371 | Not measured | Only MiniMax M2 measured |
| LMArena Text: software and IT | 1373 | Not measured | Only MiniMax M2 measured |
| LMArena WebDev: React | Not measured | 1514 | Only Qwen3.7 Max measured |
| LMArena WebDev: HTML | 1306 | 1512 | Qwen3.7 Max |
| Neither model has a result on the other 22 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | MiniMax M2 | Qwen3.7 Max | Leader |
|---|---|---|---|
| AA Intelligence Index | 18.6 | 29.5 | Qwen3.7 Max |
| Epoch Capabilities Index | Not measured | 154.0 | Only Qwen3.7 Max measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | MiniMax M2 | Qwen3.7 Max | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 22.5 | Only Qwen3.7 Max measured |
| Design Arena: agents building web apps | Not measured | 1208 | Only Qwen3.7 Max measured |
| Design Arena: agents building full-stack apps | Not measured | 1191 | Only Qwen3.7 Max measured |
| Design Arena: agents building mobile apps | Not measured | 1155 | Only Qwen3.7 Max measured |
| Design Arena: agents building Android apps | Not measured | 1159 | Only Qwen3.7 Max measured |
| Design Arena: agents building Godot games | Not measured | 1187 | Only Qwen3.7 Max measured |
| Design Arena: agents building games | Not measured | 1161 | Only Qwen3.7 Max measured |
| Design Arena: HTML slides | Not measured | 1108 | Only Qwen3.7 Max measured |
| Design Arena: PowerPoint slides | Not measured | 1171 | Only Qwen3.7 Max measured |
| Finance Agent (Vals AI) | Not measured | 47.8% | Only Qwen3.7 Max measured |
| Tax Agent Bench (Vals AI) | Not measured | 15.8% | Only Qwen3.7 Max measured |
| Legal Research Bench (Vals AI) | Not measured | 25.5% | Only Qwen3.7 Max measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 57.0% | Only Qwen3.7 Max measured |
| τ²-Bench Telecom | 86.8% | 94.7% | Qwen3.7 Max |
| Vending-Bench 2 | $161 | Not measured | Only MiniMax M2 measured |
| Neither model has a result on the other 26 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | MiniMax M2 | Qwen3.7 Max | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 90.2% | Only Qwen3.7 Max measured |
| GPQA Diamond (Artificial Analysis run) | 77.7% | 92.3% | Qwen3.7 Max |
| Humanity's Last Exam (Artificial Analysis run) | 13.7% | 40.5% | Qwen3.7 Max |
| GPQA Diamond (Epoch AI run) | Not measured | 90.9% | Only Qwen3.7 Max measured |
| SimpleBench | Not measured | 70.4% | Only Qwen3.7 Max measured |
| Chess puzzles | Not measured | 26.0% | Only Qwen3.7 Max measured |
| Mystery game puzzles | Not measured | 32.0% | Only Qwen3.7 Max measured |
| EBR-bench | Not measured | 9.5% | Only Qwen3.7 Max measured |
| LMArena Text: hard prompts | 1357 | Not measured | Only MiniMax M2 measured |
| LMArena Text: expert | 1338 | Not measured | Only MiniMax M2 measured |
| LMArena Text: hard prompts in English | 1376 | Not measured | Only MiniMax M2 measured |
| Kagi LLM Benchmark | 57.8% | Not measured | Only MiniMax M2 measured |
| Neither model has a result on the other 10 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | MiniMax M2 | Qwen3.7 Max | Leader |
|---|---|---|---|
| AA Math Index | 78.3 | Not measured | Only MiniMax M2 measured |
| AIME 2025 (Artificial Analysis run) | 78.3% | Not measured | Only MiniMax M2 measured |
| OTIS Mock AIME | Not measured | 95.6% | Only Qwen3.7 Max measured |
| FrontierMath, tiers 1 to 3 | Not measured | 64.6% | Only Qwen3.7 Max measured |
| FrontierMath, tier 4 | Not measured | 34.1% | Only Qwen3.7 Max measured |
| ProofBench | Not measured | 26.0% | Only Qwen3.7 Max measured |
| LMArena Text: math | 1351 | Not measured | Only MiniMax M2 measured |
| LMArena Text: mathematical work | 1368 | Not measured | Only MiniMax M2 measured |
| Neither model has a result on the other 5 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
| Benchmark | MiniMax M2 | Qwen3.7 Max | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 89.3% | Only Qwen3.7 Max measured |
| LegalBench (Vals AI) | Not measured | 84.9% | Only Qwen3.7 Max measured |
| CorpFin (Vals AI) | Not measured | 63.7% | Only Qwen3.7 Max measured |
| TaxEval (Vals AI) | Not measured | 75.3% | Only Qwen3.7 Max measured |
| MedCode (Vals AI) | Not measured | 38.8% | Only Qwen3.7 Max measured |
| MMLU Pro (Artificial Analysis run) | 82.0% | Not measured | Only MiniMax M2 measured |
| SimpleQA Verified | Not measured | 55.8% | Only Qwen3.7 Max measured |
| LMArena Text: science | 1348 | Not measured | Only MiniMax M2 measured |
| LMArena Text: medicine and healthcare | 1367 | Not measured | Only MiniMax M2 measured |
| LMArena Text: legal and government | 1349 | Not measured | Only MiniMax M2 measured |
| LMArena Text: business and finance | 1336 | Not measured | Only MiniMax M2 measured |
| Neither model has a result on the other 7 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index07
| Benchmark | MiniMax M2 | Qwen3.7 Max | Leader |
|---|---|---|---|
| Design Arena: websites | 1143 | 1274 | Qwen3.7 Max |
| Design Arena: UI components | 1137 | 1267 | Qwen3.7 Max |
| Design Arena: game dev | 1118 | 1264 | Qwen3.7 Max |
| Design Arena: data viz | 1140 | 1275 | Qwen3.7 Max |
| Design Arena: 3D | 1098 | 1265 | Qwen3.7 Max |
| Design Arena: all code categories | 1135 | 1275 | Qwen3.7 Max |
| Design Arena: SVG | 1100 | 1216 | Qwen3.7 Max |
| Design Arena: ASCII art | Not measured | 1211 | Only Qwen3.7 Max measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | MiniMax M2 | Qwen3.7 Max | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | Not measured | 79.4% | Only Qwen3.7 Max measured |
| IFBench | 72.3% | 80.5% | Qwen3.7 Max |
| Long context reasoning (AA-LCR) | 64.3% | 79.0% | Qwen3.7 Max |
| DTBench | Not measured | 92.3% | Only Qwen3.7 Max measured |
| LMCA | Not measured | 44.0% | Only Qwen3.7 Max measured |
| LMArena Text | 1340 | Not measured | Only MiniMax M2 measured |
| LMArena Text: instruction following | 1329 | Not measured | Only MiniMax M2 measured |
| LMArena Text: creative writing | 1286 | Not measured | Only MiniMax M2 measured |
| LMArena Text: English | 1364 | Not measured | Only MiniMax M2 measured |
| LMArena Text: non-English | 1313 | Not measured | Only MiniMax M2 measured |
| LMArena Text: Chinese | 1366 | Not measured | Only MiniMax M2 measured |
| LMArena Text: German | 1357 | Not measured | Only MiniMax M2 measured |
| LMArena Text: French | 1336 | Not measured | Only MiniMax M2 measured |
| LMArena Text: Spanish | 1326 | Not measured | Only MiniMax M2 measured |
| LMArena Text: Russian | 1330 | Not measured | Only MiniMax M2 measured |
| LMArena Text: Polish | 1312 | Not measured | Only MiniMax M2 measured |
| LMArena Text: multi-turn | 1361 | Not measured | Only MiniMax M2 measured |
| LMArena Text: longer queries | 1331 | Not measured | Only MiniMax M2 measured |
| LMArena Text: writing and language | 1295 | Not measured | Only MiniMax M2 measured |
| LMArena Text: entertainment and media | 1298 | Not measured | Only MiniMax M2 measured |
| Neither model has a result on the other 26 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | MiniMax M2 | Qwen3.7 Max | 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
MiniMax M2 costs less
Repository review
50K input + 3K output tokens
MiniMax M2 costs less
Agent loop
200K input + 10K output tokens
MiniMax M2 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 | Qwen3.7 Max |
|---|---|---|
| Maker | MiniMax | Qwen |
| Weights | Open weights | No public weights listed |
| Released | October 23, 2025 | May 21, 2026 |
| Context window | 205K | 1M |
| Input $/Mtok | $0.255 | $1.475 |
| Output $/Mtok | $1.02 | $4.425 |
| Coding index | Not benchmarked | 54.1 |
| Independent measurements behind it | 1 | 3 |
| Cost per solved task | $0.702 | $4.69 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is MiniMax M2 better than Qwen3.7 Max for coding?
Only Qwen3.7 Max has a coding score, 54.1 on the Vibe Coding Index; MiniMax M2 has none, so the two cannot be ranked on coding. Pick MiniMax M2 for output tokens 4.3x cheaper ($1.02 vs $4.425 per million) and a lower cost per solved task ($0.702 against $4.69).
Which is cheaper, MiniMax M2 or Qwen3.7 Max?
MiniMax M2 costs $0.255 per million input tokens and $1.02 per million output tokens; Qwen3.7 Max costs $1.475 and $4.425. Per solved SWE-bench Verified task, MiniMax M2 costs $0.702 and Qwen3.7 Max $4.69.
Which has the bigger context window, MiniMax M2 or Qwen3.7 Max?
Qwen3.7 Max, with 1M tokens against 205K.
How many benchmarks is the MiniMax M2 vs Qwen3.7 Max comparison based on?
16 benchmarks have a result for both models as of October 22, 2026. MiniMax M2's coding index rests on 1 independent benchmark and Qwen3.7 Max's on 3. Where only one model has a result, the page says so instead of naming a winner.