Command A vs Qwen3.5-27B
Updated October 21, 2026. 37 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
Command A has the edge where what matters is Terminal-Bench Hard (Artificial Analysis run) or LMArena Text: coding, where it leads.
- Shared results
- 37
- Command A only
- 23
- Qwen3.5-27B only
- 16
- 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, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
Qwen3.5-27B
Qwen3.5-27B leads, 22.9 against 13.1.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Command A has a AA Agentic Index result, 1.0.
Directional only
Reasoning
GPQA Diamond
No call
Only Command A has a GPQA Diamond result, 48.5%.
Directional only
Math
AIME
No call
Only Command A has a AIME result, 13.3%.
Directional only
Knowledge
MMLU Pro
No call
Only Command A has a MMLU Pro result, 69.2%.
Directional only
Long documents
Listed context window
Qwen3.5-27B
Qwen3.5-27B takes 262K tokens against 256K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.5-27B
Command A $0.0075, Qwen3.5-27B $0.0010 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Command A has the number, $2.69.
Directional only
At a glance
- Coding index
- Command A not benchmarked, Qwen3.5-27B not benchmarked.
- Widest category
- Math, 20.5 points apart on average over 2 shared benchmarks.
- Widest single gap
- LMArena Text: Chinese: Command A 1327 against Qwen3.5-27B 1478, 25.1 points apart on a 0 to 100 scale.
- Output price
- Command A $10.00 against Qwen3.5-27B $1.56 per million output tokens, 6.4x apart.
- Context window
- Command A takes 256K tokens, Qwen3.5-27B 262K.
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 | Qwen3.5-27B | Ties |
|---|---|---|---|---|
| Coding | 0 | 3 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 5 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 4 | ||
| General | 0 | 21 | ||
| All shared boards | 0 | 37 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
| Benchmark | Command A | Qwen3.5-27B | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1358 | Only Qwen3.5-27B measured |
| AA Coding Index | 27.8 | Not measured | Only Command A measured |
| LiveCodeBench (Vals AI) | 35.1% | Not measured | Only Command A measured |
| SWE-bench Verified (Vals AI) | 7.8% | Not measured | Only Command A measured |
| Terminal-Bench 2.1 (Vals AI) | 17.6% | Not measured | Only Command A measured |
| LiveCodeBench (Artificial Analysis run) | 28.7% | Not measured | Only Command A measured |
| SciCode | 38.5% | Not measured | Only Command A measured |
| Terminal-Bench Hard (Artificial Analysis run) | 25.0% | 32.6% | Qwen3.5-27B |
| WeirdML | Not measured | 39.5% | Only Qwen3.5-27B measured |
| ALE-Bench | Not measured | 349.4 | Only Qwen3.5-27B measured |
| LMArena Text: coding | 1330 | 1427 | Qwen3.5-27B |
| LMArena Text: software and IT | 1336 | 1431 | Qwen3.5-27B |
| LMArena WebDev: React | Not measured | 1346 | Only Qwen3.5-27B measured |
| LMArena WebDev: HTML | Not measured | 1386 | Only Qwen3.5-27B measured |
| Neither model has a result on the other 24 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Command A | Qwen3.5-27B | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.1 | 22.9 | Qwen3.5-27B |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Command A | Qwen3.5-27B | Leader |
|---|---|---|---|
| AA Agentic Index | 1.0 | Not measured | Only Command A measured |
| Finance Agent (Vals AI) | 9.0% | Not measured | Only Command A measured |
| τ²-Bench Telecom | 80.7% | 93.9% | Qwen3.5-27B |
| Vending-Bench 2 | Not measured | $202 | Only Qwen3.5-27B measured |
| Neither model has a result on the other 35 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | Command A | Qwen3.5-27B | Leader |
|---|---|---|---|
| GPQA Diamond | 48.5% | Not measured | Only Command A measured |
| GPQA Diamond (Artificial Analysis run) | 76.1% | 85.8% | Qwen3.5-27B |
| Humanity's Last Exam (Artificial Analysis run) | 12.0% | 23.9% | Qwen3.5-27B |
| LMArena Text: hard prompts | 1326 | 1415 | Qwen3.5-27B |
| LMArena Text: expert | 1296 | 1427 | Qwen3.5-27B |
| LMArena Text: hard prompts in English | 1335 | 1429 | Qwen3.5-27B |
| LMArena Vision: diagrams | Not measured | 1249 | Only Qwen3.5-27B measured |
| LMArena Vision: homework | Not measured | 1256 | Only Qwen3.5-27B measured |
| Neither model has a result on the other 13 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Command A | Qwen3.5-27B | 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 |
| LMArena Text: math | 1300 | 1429 | Qwen3.5-27B |
| LMArena Text: mathematical work | 1312 | 1429 | Qwen3.5-27B |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | Command A | Qwen3.5-27B | Leader |
|---|---|---|---|
| MMLU Pro | 69.2% | Not measured | Only Command A measured |
| LegalBench (Vals AI) | 79.7% | Not measured | Only Command A measured |
| CaseLaw (Vals AI) | 64.5% | Not measured | Only Command A measured |
| CorpFin (Vals AI) | 46.0% | Not measured | Only Command A measured |
| TaxEval (Vals AI) | 61.4% | Not measured | Only Command A measured |
| MedQA (Vals AI) | 80.5% | Not measured | Only Command A measured |
| MedCode (Vals AI) | 19.7% | Not measured | Only Command A measured |
| MMLU Pro (Artificial Analysis run) | 71.2% | Not measured | Only Command A measured |
| LMArena Text: science | 1339 | 1437 | Qwen3.5-27B |
| LMArena Text: medicine and healthcare | 1334 | 1425 | Qwen3.5-27B |
| LMArena Text: legal and government | 1348 | 1405 | Qwen3.5-27B |
| LMArena Text: business and finance | 1315 | 1399 | Qwen3.5-27B |
| Neither model has a result on the other 5 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Command A | Qwen3.5-27B | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index021
| Benchmark | Command A | Qwen3.5-27B | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | 55.7% | Not measured | Only Command A measured |
| IFBench | 73.9% | 75.6% | Qwen3.5-27B |
| Long context reasoning (AA-LCR) | 52.7% | 77.7% | Qwen3.5-27B |
| DTBench | 61.3% | 82.4% | Qwen3.5-27B |
| LMCA | 10.3% | 34.0% | Qwen3.5-27B |
| LMArena Text | 1331 | 1409 | Qwen3.5-27B |
| LMArena Text: instruction following | 1309 | 1393 | Qwen3.5-27B |
| LMArena Text: creative writing | 1319 | 1362 | Qwen3.5-27B |
| LMArena Vision | Not measured | 1241 | Only Qwen3.5-27B measured |
| LMArena Text: English | 1344 | 1425 | Qwen3.5-27B |
| LMArena Text: non-English | 1313 | 1390 | Qwen3.5-27B |
| LMArena Text: Chinese | 1327 | 1478 | Qwen3.5-27B |
| LMArena Text: German | 1342 | 1392 | Qwen3.5-27B |
| LMArena Text: Japanese | 1285 | 1346 | Qwen3.5-27B |
| LMArena Text: Korean | 1285 | 1359 | Qwen3.5-27B |
| LMArena Text: French | 1351 | 1410 | Qwen3.5-27B |
| LMArena Text: Spanish | 1347 | 1407 | Qwen3.5-27B |
| LMArena Text: Russian | 1314 | 1390 | Qwen3.5-27B |
| LMArena Text: Polish | 1324 | 1375 | Qwen3.5-27B |
| LMArena Text: multi-turn | 1338 | 1410 | Qwen3.5-27B |
| LMArena Text: longer queries | 1334 | 1413 | Qwen3.5-27B |
| LMArena Text: writing and language | 1328 | 1380 | Qwen3.5-27B |
| LMArena Text: entertainment and media | 1306 | 1363 | Qwen3.5-27B |
| LMArena Vision: English | Not measured | 1246 | Only Qwen3.5-27B measured |
| LMArena Vision: Chinese | Not measured | 1301 | Only Qwen3.5-27B measured |
| LMArena Vision: reading text | Not measured | 1246 | Only Qwen3.5-27B measured |
| LMArena Vision: humor | Not measured | 1227 | Only Qwen3.5-27B measured |
| LMArena Vision: captioning | Not measured | 1170 | Only Qwen3.5-27B measured |
| LMArena Vision: recognizing things | Not measured | 1210 | Only Qwen3.5-27B measured |
| LMArena Vision: creative writing | Not measured | 1224 | Only Qwen3.5-27B measured |
| Neither model has a result on the other 16 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Command A | Qwen3.5-27B | 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
Qwen3.5-27B costs less
Repository review
50K input + 3K output tokens
Qwen3.5-27B costs less
Agent loop
200K input + 10K output tokens
Qwen3.5-27B 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 | Qwen3.5-27B |
|---|---|---|
| Maker | Cohere | Qwen |
| Weights | Open weights | Open weights |
| Released | March 13, 2025 | February 25, 2026 |
| Context window | 256K | 262K |
| Input $/Mtok | $2.50 | $0.195 |
| Output $/Mtok | $10.00 | $1.56 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | $2.69 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Command A better than Qwen3.5-27B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Command A has the edge where what matters is Terminal-Bench Hard (Artificial Analysis run) or LMArena Text: coding, where it leads.
Which is cheaper, Command A or Qwen3.5-27B?
Command A costs $2.50 per million input tokens and $10.00 per million output tokens; Qwen3.5-27B costs $0.195 and $1.56. Cost per solved task is known for Command A only.
Which has the bigger context window, Command A or Qwen3.5-27B?
Qwen3.5-27B, with 262K tokens against 256K.
How many benchmarks is the Command A vs Qwen3.5-27B comparison based on?
37 benchmarks have a result for both models as of October 21, 2026. Command A's coding index rests on 1 independent benchmark and Qwen3.5-27B's on 1. Where only one model has a result, the page says so instead of naming a winner.