Command A vs Qwen3.8 27B
Updated October 21, 2026. 47 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Only Qwen3.8 27B has a coding score, 74.2 on the Vibe Coding Index; Command A has none, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
- Shared results
- 47
- Command A only
- 13
- Qwen3.8 27B only
- 38
- 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
Only Qwen3.8 27B has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Qwen3.8 27B
Qwen3.8 27B leads, 33.7 against 13.1.
Same board, both measured
Agentic work
AA Agentic Index
Qwen3.8 27B
Qwen3.8 27B leads, 45.8 against 1.0.
Same board, both measured
Reasoning
GPQA Diamond
Qwen3.8 27B
Qwen3.8 27B leads, 88.9% 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
Qwen3.8 27B
Qwen3.8 27B leads, 84.3% against 69.2%.
Same board, both measured
Long documents
Listed context window
Qwen3.8 27B
Qwen3.8 27B takes 1M tokens against 256K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.8 27B
Command A $0.0075, Qwen3.8 27B $0.0017 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
Qwen3.8 27B
Command A $2.69, Qwen3.8 27B $1.26.
Published attempt cost and pass rate
At a glance
- Coding index
- Command A not benchmarked, Qwen3.8 27B 74.2 from 4 benchmarks.
- Widest category
- Agentic work, 42.2 points apart on average over 2 shared benchmarks.
- Widest single gap
- SWE-bench Verified (Vals AI): Command A 7.8% against Qwen3.8 27B 86.0%, 78.2 points apart on a 0 to 100 scale.
- Output price
- Command A $10.00 against Qwen3.8 27B $2.55 per million output tokens, 3.9x apart.
- Context window
- Command A takes 256K tokens, Qwen3.8 27B 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 | Qwen3.8 27B | Ties |
|---|---|---|---|---|
| Coding | 0 | 7 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 2 | ||
| Reasoning | 0 | 6 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 8 | ||
| General | 0 | 21 | ||
| All shared boards | 0 | 47 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index07
| Benchmark | Command A | Qwen3.8 27B | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 86.8% | Only Qwen3.8 27B measured |
| LMArena WebDev | Not measured | 1593 | Only Qwen3.8 27B measured |
| AA Coding Index | 27.8 | 68.1 | Qwen3.8 27B |
| LiveCodeBench (Vals AI) | 35.1% | 84.0% | Qwen3.8 27B |
| SWE-bench Verified (Vals AI) | 7.8% | 86.0% | Qwen3.8 27B |
| Vibe Code Bench (Vals AI) | Not measured | 64.8% | Only Qwen3.8 27B measured |
| Code migration (Vals AI) | Not measured | 14.2% | Only Qwen3.8 27B measured |
| ProgramBench (Vals AI) | Not measured | 0.0% | Only Qwen3.8 27B measured |
| IOI (Vals AI) | Not measured | 39.1% | Only Qwen3.8 27B measured |
| Terminal-Bench 2.1 (Vals AI) | 17.6% | 58.4% | Qwen3.8 27B |
| SkillsBench (Vals AI) | Not measured | 38.1% | Only Qwen3.8 27B measured |
| CyberBench (Vals AI) | Not measured | 67.8% | Only Qwen3.8 27B measured |
| LiveCodeBench (Artificial Analysis run) | 28.7% | Not measured | Only Command A measured |
| SciCode | 38.5% | 46.6% | Qwen3.8 27B |
| Terminal-Bench Hard (Artificial Analysis run) | 25.0% | Not measured | Only Command A measured |
| LMArena Text: coding | 1330 | 1483 | Qwen3.8 27B |
| LMArena Text: software and IT | 1336 | 1478 | Qwen3.8 27B |
| LMArena WebDev: React | Not measured | 1602 | Only Qwen3.8 27B measured |
| LMArena WebDev: HTML | Not measured | 1570 | Only Qwen3.8 27B measured |
| LMArena WebDev: from an image | Not measured | 1577 | Only Qwen3.8 27B measured |
| Neither model has a result on the other 18 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Command A | Qwen3.8 27B | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.1 | 33.7 | Qwen3.8 27B |
| Epoch Capabilities Index | Not measured | 151.7 | Only Qwen3.8 27B measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Command A | Qwen3.8 27B | Leader |
|---|---|---|---|
| AA Agentic Index | 1.0 | 45.8 | Qwen3.8 27B |
| LiveBench research | Not measured | 59.4% | Only Qwen3.8 27B measured |
| LiveBench browser | Not measured | 32.0% | Only Qwen3.8 27B measured |
| LiveBench long agentic | Not measured | 27.9% | Only Qwen3.8 27B measured |
| Finance Agent (Vals AI) | 9.0% | 48.5% | Qwen3.8 27B |
| Tax Agent Bench (Vals AI) | Not measured | 30.9% | Only Qwen3.8 27B measured |
| Legal Research Bench (Vals AI) | Not measured | 36.1% | Only Qwen3.8 27B measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 59.7% | Only Qwen3.8 27B measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 0.0% | Only Qwen3.8 27B measured |
| τ²-Bench Telecom | 80.7% | Not measured | Only Command A measured |
| APEX-Agents | Not measured | 47.5% | Only Qwen3.8 27B measured |
| Neither model has a result on the other 28 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | Command A | Qwen3.8 27B | Leader |
|---|---|---|---|
| GPQA Diamond | 48.5% | 88.9% | Qwen3.8 27B |
| LiveBench reasoning | Not measured | 80.0% | Only Qwen3.8 27B measured |
| MMMU Pro (Vals AI) | Not measured | 83.9% | Only Qwen3.8 27B measured |
| GPQA Diamond (Artificial Analysis run) | 76.1% | 90.5% | Qwen3.8 27B |
| Humanity's Last Exam (Artificial Analysis run) | 12.0% | 33.9% | Qwen3.8 27B |
| ARC-AGI | Not measured | 87.5% | Only Qwen3.8 27B measured |
| ARC-AGI-2 | Not measured | 42.4% | Only Qwen3.8 27B measured |
| LMArena Text: hard prompts | 1326 | 1459 | Qwen3.8 27B |
| LMArena Text: expert | 1296 | 1479 | Qwen3.8 27B |
| LMArena Text: hard prompts in English | 1335 | 1461 | Qwen3.8 27B |
| LMArena Vision: diagrams | Not measured | 1285 | Only Qwen3.8 27B measured |
| LMArena Vision: homework | Not measured | 1295 | Only Qwen3.8 27B measured |
| Neither model has a result on the other 9 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Command A | Qwen3.8 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 |
| ProofBench | Not measured | 16.0% | Only Qwen3.8 27B measured |
| LMArena Text: math | 1300 | 1455 | Qwen3.8 27B |
| LMArena Text: mathematical work | 1312 | 1458 | Qwen3.8 27B |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index08
| Benchmark | Command A | Qwen3.8 27B | Leader |
|---|---|---|---|
| MMLU Pro | 69.2% | 84.3% | Qwen3.8 27B |
| LegalBench (Vals AI) | 79.7% | 82.4% | Qwen3.8 27B |
| 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% | 70.8% | Qwen3.8 27B |
| MortgageTax (Vals AI) | Not measured | 64.9% | Only Qwen3.8 27B measured |
| MedQA (Vals AI) | 80.5% | Not measured | Only Command A measured |
| MedCode (Vals AI) | 19.7% | 28.7% | Qwen3.8 27B |
| MMLU Pro (Artificial Analysis run) | 71.2% | Not measured | Only Command A measured |
| LMArena Text: science | 1339 | 1467 | Qwen3.8 27B |
| LMArena Text: medicine and healthcare | 1334 | 1437 | Qwen3.8 27B |
| LMArena Text: legal and government | 1348 | 1447 | Qwen3.8 27B |
| LMArena Text: business and finance | 1315 | 1438 | Qwen3.8 27B |
| Neither model has a result on the other 4 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Command A | Qwen3.8 27B | Leader |
|---|---|---|---|
| LiveBench design | Not measured | 75.9% | Only Qwen3.8 27B measured |
| Neither model has a result on the other 8 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index021
| Benchmark | Command A | Qwen3.8 27B | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 76.6% | Only Qwen3.8 27B measured |
| SAGE (Vals AI) | Not measured | 52.4% | Only Qwen3.8 27B measured |
| MedScribe (Vals AI) | 55.7% | 83.8% | Qwen3.8 27B |
| IFBench | 73.9% | Not measured | Only Command A measured |
| Long context reasoning (AA-LCR) | 52.7% | 82.0% | Qwen3.8 27B |
| DTBench | 61.3% | 88.0% | Qwen3.8 27B |
| LMCA | 10.3% | 41.4% | Qwen3.8 27B |
| Surface Evolver Bench | Not measured | 45.0% | Only Qwen3.8 27B measured |
| LMArena Text | 1331 | 1440 | Qwen3.8 27B |
| LMArena Text: instruction following | 1309 | 1436 | Qwen3.8 27B |
| LMArena Text: creative writing | 1319 | 1383 | Qwen3.8 27B |
| LMArena Vision | Not measured | 1269 | Only Qwen3.8 27B measured |
| LMArena Text: English | 1344 | 1448 | Qwen3.8 27B |
| LMArena Text: non-English | 1313 | 1428 | Qwen3.8 27B |
| LMArena Text: Chinese | 1327 | 1501 | Qwen3.8 27B |
| LMArena Text: German | 1342 | 1443 | Qwen3.8 27B |
| LMArena Text: Japanese | 1285 | 1387 | Qwen3.8 27B |
| LMArena Text: Korean | 1285 | 1384 | Qwen3.8 27B |
| LMArena Text: French | 1351 | 1465 | Qwen3.8 27B |
| LMArena Text: Spanish | 1347 | 1449 | Qwen3.8 27B |
| LMArena Text: Russian | 1314 | 1415 | Qwen3.8 27B |
| LMArena Text: Polish | 1324 | 1439 | Qwen3.8 27B |
| LMArena Text: multi-turn | 1338 | 1440 | Qwen3.8 27B |
| LMArena Text: longer queries | 1334 | 1448 | Qwen3.8 27B |
| LMArena Text: writing and language | 1328 | 1405 | Qwen3.8 27B |
| LMArena Text: entertainment and media | 1306 | 1393 | Qwen3.8 27B |
| LMArena Vision: English | Not measured | 1267 | Only Qwen3.8 27B measured |
| LMArena Vision: Chinese | Not measured | 1326 | Only Qwen3.8 27B measured |
| LMArena Vision: reading text | Not measured | 1281 | Only Qwen3.8 27B measured |
| LMArena Vision: humor | Not measured | 1235 | Only Qwen3.8 27B measured |
| LMArena Vision: creative writing | Not measured | 1237 | Only Qwen3.8 27B measured |
| Neither model has a result on the other 15 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Command A | Qwen3.8 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
Qwen3.8 27B costs less
Repository review
50K input + 3K output tokens
Qwen3.8 27B costs less
Agent loop
200K input + 10K output tokens
Qwen3.8 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 | Qwen3.8 27B |
|---|---|---|
| Maker | Cohere | Qwen |
| Weights | Open weights | Open weights |
| Released | March 13, 2025 | August 14, 2026 |
| Context window | 256K | 1M |
| Input $/Mtok | $2.50 | $0.425 |
| Output $/Mtok | $10.00 | $2.55 |
| Coding index | Not benchmarked | 74.2 |
| Independent measurements behind it | 1 | 4 |
| Cost per solved task | $2.69 | $1.26 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Command A better than Qwen3.8 27B for coding?
Only Qwen3.8 27B has a coding score, 74.2 on the Vibe Coding Index; Command A has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, Command A or Qwen3.8 27B?
Command A costs $2.50 per million input tokens and $10.00 per million output tokens; Qwen3.8 27B costs $0.425 and $2.55. Per solved SWE-bench Verified task, Command A costs $2.69 and Qwen3.8 27B $1.26.
Which has the bigger context window, Command A or Qwen3.8 27B?
Qwen3.8 27B, with 1M tokens against 256K.
How many benchmarks is the Command A vs Qwen3.8 27B comparison based on?
47 benchmarks have a result for both models as of October 21, 2026. Command A's coding index rests on 1 independent benchmark and Qwen3.8 27B's on 4. Where only one model has a result, the page says so instead of naming a winner.