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Command A vs Qwen3.6 27B

Updated October 21, 2026. 16 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace Command A Replace Qwen3.6 27B
Command ACohere
Coding
No data
Overall
13.1
Output $/M
$10.00
2
Qwen3.6 27BQwen
Coding
36.8
Overall
21.4
Output $/M
$2.70
14
vs boards won of 16 shared

Decision reading

Only Qwen3.6 27B has a coding score, 36.8 on the Vibe Coding Index; Command A has none, so the two cannot be ranked on coding.

Command A has the edge where what matters is CaseLaw (Vals AI) or IFBench, where it leads.

Model ACommand ACohereNot benchmarked1 independent benchmark$2.69 per solved taskModel BQwen3.6 27BQwen36.8/1002 independent benchmarks$1.30 per solved task
Shared results
16
Command A only
44
Qwen3.6 27B only
8
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.

At a glance

Coding index
Command A not benchmarked, Qwen3.6 27B 36.8 from 2 benchmarks.
Widest category
Coding, 25.6 points apart on average over 4 shared benchmarks.
Widest single gap
SWE-bench Verified (Vals AI): Command A 7.8% against Qwen3.6 27B 70.0%, 62.2 points apart on a 0 to 100 scale.
Output price
Command A $10.00 against Qwen3.6 27B $2.70 per million output tokens, 3.7x apart.
Context window
Command A takes 256K tokens, Qwen3.6 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.

Boards won, where both have a result
CategoryCommand ABarsQwen3.6 27BTies
Coding04
Intelligence01
Agentic work02
Reasoning02
Knowledge12
General13
All shared boards214
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
Coding benchmarks for Command A and Qwen3.6 27B
BenchmarkCommand AQwen3.6 27BLeader
AA Coding Index27.853.7Qwen3.6 27B
LiveCodeBench (Vals AI)35.1%Not measuredOnly Command A measured
SWE-bench Verified (Vals AI)7.8%70.0%Qwen3.6 27B
Vibe Code Bench (Vals AI)Not measured11.9%Only Qwen3.6 27B measured
Terminal-Bench 2.1 (Vals AI)17.6%Not measuredOnly Command A measured
LiveCodeBench (Artificial Analysis run)28.7%Not measuredOnly Command A measured
SciCode38.5%42.8%Qwen3.6 27B
Terminal-Bench Hard (Artificial Analysis run)25.0%34.8%Qwen3.6 27B
LMArena Text: coding1330Not measuredOnly Command A measured
LMArena Text: software and IT1336Not measuredOnly Command A measured
Neither model has a result on the other 28 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for Command A and Qwen3.6 27B
BenchmarkCommand AQwen3.6 27BLeader
AA Intelligence Index13.121.4Qwen3.6 27B
Epoch Capabilities IndexNot measured146.2Only Qwen3.6 27B measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index02
Agentic work benchmarks for Command A and Qwen3.6 27B
BenchmarkCommand AQwen3.6 27BLeader
AA Agentic Index1.018.5Qwen3.6 27B
Finance Agent (Vals AI)9.0%Not measuredOnly Command A measured
τ²-Bench Telecom80.7%94.2%Qwen3.6 27B
Neither model has a result on the other 36 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
Reasoning benchmarks for Command A and Qwen3.6 27B
BenchmarkCommand AQwen3.6 27BLeader
GPQA Diamond48.5%Not measuredOnly Command A measured
GPQA Diamond (Artificial Analysis run)76.1%84.2%Qwen3.6 27B
Humanity's Last Exam (Artificial Analysis run)12.0%23.1%Qwen3.6 27B
GPQA Diamond (Epoch AI run)Not measured85.9%Only Qwen3.6 27B measured
Mystery game puzzlesNot measured7.0%Only Qwen3.6 27B measured
LMArena Text: hard prompts1326Not measuredOnly Command A measured
LMArena Text: expert1296Not measuredOnly Command A measured
LMArena Text: hard prompts in English1335Not measuredOnly Command A measured
Neither model has a result on the other 13 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
Math benchmarks for Command A and Qwen3.6 27B
BenchmarkCommand AQwen3.6 27BLeader
AIME13.3%Not measuredOnly Command A measured
MATH 500 (Vals AI)76.2%Not measuredOnly Command A measured
MGSM (Vals AI)85.7%Not measuredOnly Command A measured
AA Math Index13.0Not measuredOnly Command A measured
AIME 2025 (Artificial Analysis run)13.0%Not measuredOnly Command A measured
OTIS Mock AIMENot measured91.1%Only Qwen3.6 27B measured
FrontierMath, tiers 1 to 3Not measured35.1%Only Qwen3.6 27B measured
LMArena Text: math1300Not measuredOnly Command A measured
LMArena Text: mathematical work1312Not measuredOnly Command A measured
Neither model has a result on the other 4 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index12
Knowledge benchmarks for Command A and Qwen3.6 27B
BenchmarkCommand AQwen3.6 27BLeader
MMLU Pro69.2%Not measuredOnly Command A measured
LegalBench (Vals AI)79.7%Not measuredOnly Command A measured
CaseLaw (Vals AI)64.5%53.2%Command A
CorpFin (Vals AI)46.0%62.3%Qwen3.6 27B
TaxEval (Vals AI)61.4%71.3%Qwen3.6 27B
MortgageTax (Vals AI)Not measured68.3%Only Qwen3.6 27B measured
MedQA (Vals AI)80.5%Not measuredOnly Command A measured
MedCode (Vals AI)19.7%Not measuredOnly Command A measured
MMLU Pro (Artificial Analysis run)71.2%Not measuredOnly Command A measured
LMArena Text: science1339Not measuredOnly Command A measured
LMArena Text: medicine and healthcare1334Not measuredOnly Command A measured
LMArena Text: legal and government1348Not measuredOnly Command A measured
LMArena Text: business and finance1315Not measuredOnly Command A measured
Neither model has a result on the other 4 boards here
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Web design benchmarks for Command A and Qwen3.6 27B
BenchmarkCommand AQwen3.6 27BLeader
Neither model has a result on 9 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index13
General benchmarks for Command A and Qwen3.6 27B
BenchmarkCommand AQwen3.6 27BLeader
SAGE (Vals AI)Not measured45.6%Only Qwen3.6 27B measured
MedScribe (Vals AI)55.7%Not measuredOnly Command A measured
IFBench73.9%67.6%Command A
Long context reasoning (AA-LCR)52.7%77.3%Qwen3.6 27B
DTBench61.3%78.1%Qwen3.6 27B
LMCA10.3%34.5%Qwen3.6 27B
LMArena Text1331Not measuredOnly Command A measured
LMArena Text: instruction following1309Not measuredOnly Command A measured
LMArena Text: creative writing1319Not measuredOnly Command A measured
LMArena Text: English1344Not measuredOnly Command A measured
LMArena Text: non-English1313Not measuredOnly Command A measured
LMArena Text: Chinese1327Not measuredOnly Command A measured
LMArena Text: German1342Not measuredOnly Command A measured
LMArena Text: Japanese1285Not measuredOnly Command A measured
LMArena Text: Korean1285Not measuredOnly Command A measured
LMArena Text: French1351Not measuredOnly Command A measured
LMArena Text: Spanish1347Not measuredOnly Command A measured
LMArena Text: Russian1314Not measuredOnly Command A measured
LMArena Text: Polish1324Not measuredOnly Command A measured
LMArena Text: multi-turn1338Not measuredOnly Command A measured
LMArena Text: longer queries1334Not measuredOnly Command A measured
LMArena Text: writing and language1328Not measuredOnly Command A measured
LMArena Text: entertainment and media1306Not measuredOnly Command A measured
Neither model has a result on the other 23 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Command A and Qwen3.6 27B
BenchmarkCommand AQwen3.6 27BLeader
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

Command A$0.0075Fits in one request
Qwen3.6 27B$0.0018Fits in one request

Qwen3.6 27B costs less

Repository review

50K input + 3K output tokens

Command A$0.155Fits in one request
Qwen3.6 27B$0.031Fits in one request

Qwen3.6 27B costs less

Agent loop

200K input + 10K output tokens

Command A$0.600Fits in one request
Qwen3.6 27B$0.117Fits in one request

Qwen3.6 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.

Specifications of Command A and Qwen3.6 27B
SpecificationCohere Command AQwen3.6 27B
MakerCohereQwen
WeightsOpen weightsOpen weights
ReleasedMarch 13, 2025April 27, 2026
Context window256K262K
Input $/Mtok$2.50$0.45
Output $/Mtok$10.00$2.70
Coding indexNot benchmarked36.8
Independent measurements behind it12
Cost per solved task$2.69$1.30
SourceOpenRouter listingOpenRouter listing

Questions

Is Command A better than Qwen3.6 27B for coding?

Only Qwen3.6 27B has a coding score, 36.8 on the Vibe Coding Index; Command A has none, so the two cannot be ranked on coding. Command A has the edge where what matters is CaseLaw (Vals AI) or IFBench, where it leads.

Which is cheaper, Command A or Qwen3.6 27B?

Command A costs $2.50 per million input tokens and $10.00 per million output tokens; Qwen3.6 27B costs $0.45 and $2.70. Per solved SWE-bench Verified task, Command A costs $2.69 and Qwen3.6 27B $1.30.

Which has the bigger context window, Command A or Qwen3.6 27B?

Qwen3.6 27B, with 262K tokens against 256K.

How many benchmarks is the Command A vs Qwen3.6 27B comparison based on?

16 benchmarks have a result for both models as of October 21, 2026. Command A's coding index rests on 1 independent benchmark and Qwen3.6 27B's on 2. Where only one model has a result, the page says so instead of naming a winner.

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