Command A vs Qwen3.7 Max
Updated October 21, 2026. 23 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Only Qwen3.7 Max has a coding score, 54.1 on the Vibe Coding Index; Command A has none, so the two cannot be ranked on coding.
Command A has the edge where a solved task costs $2.69 against $4.69.
- Shared results
- 23
- Command A only
- 37
- Qwen3.7 Max only
- 36
- Like-for-like categories
- 6 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, 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 13.1.
Same board, both measured
Agentic work
AA Agentic Index
Qwen3.7 Max
Qwen3.7 Max leads, 22.5 against 1.0.
Same board, both measured
Reasoning
GPQA Diamond
Qwen3.7 Max
Qwen3.7 Max leads, 90.2% against 48.5%.
Same board, both measured
Math
AIME
No call
Only Command A has a AIME result, 13.3%.
Directional only
Knowledge
MMLU Pro
Qwen3.7 Max
Qwen3.7 Max leads, 89.3% against 69.2%.
Same board, both measured
Web design
Design Arena: websites
No call
Only Qwen3.7 Max has a Design Arena: websites result, 1274.
Directional only
Long documents
Listed context window
Qwen3.7 Max
Qwen3.7 Max takes 1M tokens against 256K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.7 Max
Command A $0.0075, Qwen3.7 Max $0.0037 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
Command A
Command A $2.69, Qwen3.7 Max $4.69.
Published attempt cost and pass rate
At a glance
- Coding index
- Command A not benchmarked, Qwen3.7 Max 54.1 from 3 benchmarks.
- Widest category
- Coding, 38.6 points apart on average over 6 shared benchmarks.
- Widest single gap
- SWE-bench Verified (Vals AI): Command A 7.8% against Qwen3.7 Max 68.8%, 61.0 points apart on a 0 to 100 scale.
- Output price
- Command A $10.00 against Qwen3.7 Max $4.425 per million output tokens, 2.3x apart.
- Context window
- Command A takes 256K 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 | Command A | Bars | Qwen3.7 Max | Ties |
|---|---|---|---|---|
| Coding | 0 | 6 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 3 | ||
| Reasoning | 0 | 3 | ||
| Knowledge | 0 | 5 | ||
| General | 0 | 5 | ||
| All shared boards | 0 | 23 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index06
| Benchmark | Command A | Qwen3.7 Max | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1515 | Only Qwen3.7 Max measured |
| AA Coding Index | 27.8 | 66.0 | Qwen3.7 Max |
| LiveCodeBench (Vals AI) | 35.1% | 87.1% | Qwen3.7 Max |
| SWE-bench Verified (Vals AI) | 7.8% | 68.8% | Qwen3.7 Max |
| 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) | 17.6% | 61.0% | Qwen3.7 Max |
| LiveCodeBench (Artificial Analysis run) | 28.7% | Not measured | Only Command A measured |
| SciCode | 38.5% | 49.5% | Qwen3.7 Max |
| Terminal-Bench Hard (Artificial Analysis run) | 25.0% | 50.8% | Qwen3.7 Max |
| ALE-Bench | Not measured | 1189.4 | Only Qwen3.7 Max measured |
| LMArena Text: coding | 1330 | Not measured | Only Command A measured |
| LMArena Text: software and IT | 1336 | Not measured | Only Command A measured |
| LMArena WebDev: React | Not measured | 1514 | Only Qwen3.7 Max measured |
| LMArena WebDev: HTML | Not measured | 1512 | Only Qwen3.7 Max measured |
| Neither model has a result on the other 23 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Command A | Qwen3.7 Max | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.1 | 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 index03
| Benchmark | Command A | Qwen3.7 Max | Leader |
|---|---|---|---|
| AA Agentic Index | 1.0 | 22.5 | Qwen3.7 Max |
| 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 | 1153 | Only Qwen3.7 Max measured |
| Design Arena: agents building Android apps | Not measured | 1165 | 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 | 1158 | Only Qwen3.7 Max measured |
| Design Arena: PowerPoint slides | Not measured | 1196 | Only Qwen3.7 Max measured |
| Finance Agent (Vals AI) | 9.0% | 47.8% | Qwen3.7 Max |
| 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 | 80.7% | 94.7% | Qwen3.7 Max |
| Neither model has a result on the other 25 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | Command A | Qwen3.7 Max | Leader |
|---|---|---|---|
| GPQA Diamond | 48.5% | 90.2% | Qwen3.7 Max |
| GPQA Diamond (Artificial Analysis run) | 76.1% | 92.3% | Qwen3.7 Max |
| Humanity's Last Exam (Artificial Analysis run) | 12.0% | 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 | 1326 | Not measured | Only Command A measured |
| LMArena Text: expert | 1296 | Not measured | Only Command A measured |
| LMArena Text: hard prompts in English | 1335 | Not measured | Only Command A measured |
| Neither model has a result on the other 10 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
| Benchmark | Command A | Qwen3.7 Max | Leader |
|---|---|---|---|
| AIME | 13.3% | Not measured | Only Command A measured |
| MATH 500 (Vals AI) | 76.2% | Not measured | Only Command A measured |
| MGSM (Vals AI) | 85.7% | Not measured | Only Command A measured |
| AA Math Index | 13.0 | Not measured | Only Command A measured |
| AIME 2025 (Artificial Analysis run) | 13.0% | Not measured | Only Command A 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 | 1300 | Not measured | Only Command A measured |
| LMArena Text: mathematical work | 1312 | Not measured | Only Command A measured |
| Neither model has a result on the other 2 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | Command A | Qwen3.7 Max | Leader |
|---|---|---|---|
| MMLU Pro | 69.2% | 89.3% | Qwen3.7 Max |
| LegalBench (Vals AI) | 79.7% | 84.9% | Qwen3.7 Max |
| CaseLaw (Vals AI) | 64.5% | Not measured | Only Command A measured |
| CorpFin (Vals AI) | 46.0% | 63.7% | Qwen3.7 Max |
| TaxEval (Vals AI) | 61.4% | 75.3% | Qwen3.7 Max |
| MedQA (Vals AI) | 80.5% | Not measured | Only Command A measured |
| MedCode (Vals AI) | 19.7% | 38.8% | Qwen3.7 Max |
| MMLU Pro (Artificial Analysis run) | 71.2% | Not measured | Only Command A measured |
| SimpleQA Verified | Not measured | 55.8% | Only Qwen3.7 Max measured |
| LMArena Text: science | 1339 | Not measured | Only Command A measured |
| LMArena Text: medicine and healthcare | 1334 | Not measured | Only Command A measured |
| LMArena Text: legal and government | 1348 | Not measured | Only Command A measured |
| LMArena Text: business and finance | 1315 | Not measured | Only Command A measured |
| Neither model has a result on the other 4 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | Command A | Qwen3.7 Max | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1274 | Only Qwen3.7 Max measured |
| Design Arena: UI components | Not measured | 1270 | Only Qwen3.7 Max measured |
| Design Arena: game dev | Not measured | 1265 | Only Qwen3.7 Max measured |
| Design Arena: data viz | Not measured | 1275 | Only Qwen3.7 Max measured |
| Design Arena: 3D | Not measured | 1266 | Only Qwen3.7 Max measured |
| Design Arena: all code categories | Not measured | 1275 | Only Qwen3.7 Max measured |
| Design Arena: SVG | Not measured | 1217 | Only Qwen3.7 Max measured |
| 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 index05
| Benchmark | Command A | Qwen3.7 Max | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | 55.7% | 79.4% | Qwen3.7 Max |
| IFBench | 73.9% | 80.5% | Qwen3.7 Max |
| Long context reasoning (AA-LCR) | 52.7% | 79.0% | Qwen3.7 Max |
| DTBench | 61.3% | 92.3% | Qwen3.7 Max |
| LMCA | 10.3% | 44.0% | Qwen3.7 Max |
| LMArena Text | 1331 | Not measured | Only Command A measured |
| LMArena Text: instruction following | 1309 | Not measured | Only Command A measured |
| LMArena Text: creative writing | 1319 | Not measured | Only Command A measured |
| LMArena Text: English | 1344 | Not measured | Only Command A measured |
| LMArena Text: non-English | 1313 | Not measured | Only Command A measured |
| LMArena Text: Chinese | 1327 | Not measured | Only Command A measured |
| LMArena Text: German | 1342 | Not measured | Only Command A measured |
| LMArena Text: Japanese | 1285 | Not measured | Only Command A measured |
| LMArena Text: Korean | 1285 | Not measured | Only Command A measured |
| LMArena Text: French | 1351 | Not measured | Only Command A measured |
| LMArena Text: Spanish | 1347 | Not measured | Only Command A measured |
| LMArena Text: Russian | 1314 | Not measured | Only Command A measured |
| LMArena Text: Polish | 1324 | Not measured | Only Command A measured |
| LMArena Text: multi-turn | 1338 | Not measured | Only Command A measured |
| LMArena Text: longer queries | 1334 | Not measured | Only Command A measured |
| LMArena Text: writing and language | 1328 | Not measured | Only Command A measured |
| LMArena Text: entertainment and media | 1306 | Not measured | Only Command A measured |
| Neither model has a result on the other 24 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Command A | 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
Qwen3.7 Max costs less
Repository review
50K input + 3K output tokens
Qwen3.7 Max costs less
Agent loop
200K input + 10K output tokens
Qwen3.7 Max 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 | Cohere Command A | Qwen3.7 Max |
|---|---|---|
| Maker | Cohere | Qwen |
| Weights | Open weights | No public weights listed |
| Released | March 13, 2025 | May 21, 2026 |
| Context window | 256K | 1M |
| Input $/Mtok | $2.50 | $1.475 |
| Output $/Mtok | $10.00 | $4.425 |
| Coding index | Not benchmarked | 54.1 |
| Independent measurements behind it | 1 | 3 |
| Cost per solved task | $2.69 | $4.69 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Command A better than Qwen3.7 Max for coding?
Only Qwen3.7 Max has a coding score, 54.1 on the Vibe Coding Index; Command A has none, so the two cannot be ranked on coding. Command A has the edge where a solved task costs $2.69 against $4.69.
Which is cheaper, Command A or Qwen3.7 Max?
Command A costs $2.50 per million input tokens and $10.00 per million output tokens; Qwen3.7 Max costs $1.475 and $4.425. Per solved SWE-bench Verified task, Command A costs $2.69 and Qwen3.7 Max $4.69.
Which has the bigger context window, Command A or Qwen3.7 Max?
Qwen3.7 Max, with 1M tokens against 256K.
How many benchmarks is the Command A vs Qwen3.7 Max comparison based on?
23 benchmarks have a result for both models as of October 21, 2026. Command A'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.