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Command A vs Qwen3.7 Plus

Qwen3.7 Plus leads Command A on the Artificial Analysis Intelligence Index, 25.2 to 13.1, and Qwen3.7 Plus costs 7.8x less per output token ($1.28 vs $10.00 per million). Updated October 21, 2026; 42 benchmarks have a result for both.

Replace Command A Replace Qwen3.7 Plus
Command ACohere
Coding
No data
Overall
No data
Output $/M
$10.00
0
Qwen3.7 PlusQwen
Coding
33.2
Overall
54.0
Output $/M
$1.28
42
vs boards won of 42 shared

Command A or Qwen3.7 Plus: which to pick

Only Qwen3.7 Plus has a coding score, 33.2 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.

Model ACommand ACohereNot benchmarked1 independent benchmark$2.69 per solved taskModel BQwen3.7 PlusQwen33.2/1002 independent benchmarksNo cost per solved task
Shared results
42
Command A only
20
Qwen3.7 Plus only
33
Like-for-like categories
7 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.

At a glance

Coding index
Command A not benchmarked, Qwen3.7 Plus 33.2 from 2 benchmarks.
Widest category
Math, 27.1 points apart on average over 2 shared benchmarks.
Widest single gap
Terminal-Bench 2.1 (Vals AI): Command A 17.6% against Qwen3.7 Plus 52.8%, 35.2 points apart on a 0 to 100 scale.
Output price
Command A $10.00 against Qwen3.7 Plus $1.28 per million output tokens, 7.8x apart.
Context window
Command A takes 256K tokens, Qwen3.7 Plus 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.

Boards won, where both have a result
CategoryCommand ABarsQwen3.7 PlusTies
Coding06
Intelligence01
Agentic work03
Reasoning05
Math02
Knowledge04
General021
All shared boards042
CodingLike-for-likeSix of these boards feed the Vibe Coding Index06
Coding benchmarks for Command A and Qwen3.7 Plus
BenchmarkCommand AQwen3.7 PlusLeader
AA Coding Index27.855.9Qwen3.7 Plus
LiveCodeBench (Vals AI)35.1%Not measuredOnly Command A measured
SWE-bench Verified (Vals AI)7.8%Not measuredOnly Command A measured
Vibe Code Bench (Vals AI)Not measured46.4%Only Qwen3.7 Plus measured
Code migration (Vals AI)Not measured12.9%Only Qwen3.7 Plus measured
Terminal-Bench 2.1 (Vals AI)17.6%52.8%Qwen3.7 Plus
SkillsBench (Vals AI)Not measured54.3%Only Qwen3.7 Plus measured
LiveCodeBench (Artificial Analysis run)28.7%Not measuredOnly Command A measured
SciCode38.5%46.1%Qwen3.7 Plus
Terminal-Bench Hard (Artificial Analysis run)25.0%47.0%Qwen3.7 Plus
FrontierCodeNot measured10.2%Only Qwen3.7 Plus measured
LMArena Text: coding13301473Qwen3.7 Plus
LMArena Text: software and IT13361473Qwen3.7 Plus
Neither model has a result on the other 26 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for Command A and Qwen3.7 Plus
BenchmarkCommand AQwen3.7 PlusLeader
AA Intelligence Index13.125.2Qwen3.7 Plus
Epoch Capabilities IndexNot measured147.0Only Qwen3.7 Plus measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index03
Agentic work benchmarks for Command A and Qwen3.7 Plus
BenchmarkCommand AQwen3.7 PlusLeader
AA Agentic Index1.017.5Qwen3.7 Plus
Finance Agent (Vals AI)9.0%38.2%Qwen3.7 Plus
Tax Agent Bench (Vals AI)Not measured10.5%Only Qwen3.7 Plus measured
Legal Research Bench (Vals AI)Not measured16.4%Only Qwen3.7 Plus measured
Excel Modeling Benchmark (Vals AI)Not measured49.3%Only Qwen3.7 Plus measured
τ²-Bench Telecom80.7%93.0%Qwen3.7 Plus
OSWorld 2.0Not measured2.8%Only Qwen3.7 Plus measured
Berkeley Function Calling Leaderboard (BFCL)46.5%Not measuredOnly Command A measured
Neither model has a result on the other 33 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
Reasoning benchmarks for Command A and Qwen3.7 Plus
BenchmarkCommand AQwen3.7 PlusLeader
GPQA Diamond48.5%Not measuredOnly Command A measured
GPQA Diamond (Artificial Analysis run)76.1%90.0%Qwen3.7 Plus
Humanity's Last Exam (Artificial Analysis run)12.0%35.6%Qwen3.7 Plus
GPQA Diamond (Epoch AI run)Not measured87.9%Only Qwen3.7 Plus measured
Mystery game puzzlesNot measured17.0%Only Qwen3.7 Plus measured
LMArena Text: hard prompts13261460Qwen3.7 Plus
LMArena Text: expert12961466Qwen3.7 Plus
LMArena Text: hard prompts in English13351460Qwen3.7 Plus
LMArena Vision: diagramsNot measured1292Only Qwen3.7 Plus measured
LMArena Vision: homeworkNot measured1295Only Qwen3.7 Plus measured
Kagi LLM Benchmark28.8%Not measuredOnly Command A measured
Neither model has a result on the other 11 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for Command A and Qwen3.7 Plus
BenchmarkCommand AQwen3.7 PlusLeader
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 measured93.3%Only Qwen3.7 Plus measured
FrontierMath, tiers 1 to 3Not measured34.4%Only Qwen3.7 Plus measured
LMArena Text: math13001467Qwen3.7 Plus
LMArena Text: mathematical work13121470Qwen3.7 Plus
Neither model has a result on the other 4 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for Command A and Qwen3.7 Plus
BenchmarkCommand AQwen3.7 PlusLeader
MMLU Pro69.2%Not measuredOnly Command A measured
LegalBench (Vals AI)79.7%Not measuredOnly Command A measured
CaseLaw (Vals AI)64.5%Not measuredOnly Command A measured
CorpFin (Vals AI)46.0%Not measuredOnly Command A measured
TaxEval (Vals AI)61.4%Not measuredOnly Command A measured
MortgageTax (Vals AI)Not measured66.2%Only Qwen3.7 Plus 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: science13391464Qwen3.7 Plus
LMArena Text: medicine and healthcare13341464Qwen3.7 Plus
LMArena Text: legal and government13481451Qwen3.7 Plus
LMArena Text: business and finance13151435Qwen3.7 Plus
Neither model has a result on the other 5 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for Command A and Qwen3.7 Plus
BenchmarkCommand AQwen3.7 PlusLeader
Design Arena: websitesNot measured1269Only Qwen3.7 Plus measured
Design Arena: UI componentsNot measured1261Only Qwen3.7 Plus measured
Design Arena: game devNot measured1262Only Qwen3.7 Plus measured
Design Arena: data vizNot measured1276Only Qwen3.7 Plus measured
Design Arena: 3DNot measured1239Only Qwen3.7 Plus measured
Design Arena: all code categoriesNot measured1269Only Qwen3.7 Plus measured
Design Arena: SVGNot measured1208Only Qwen3.7 Plus measured
Design Arena: ASCII artNot measured1143Only Qwen3.7 Plus measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index021
General benchmarks for Command A and Qwen3.7 Plus
BenchmarkCommand AQwen3.7 PlusLeader
SAGE (Vals AI)Not measured39.3%Only Qwen3.7 Plus measured
MedScribe (Vals AI)55.7%Not measuredOnly Command A measured
Vals Multimodal IndexNot measured53.9%Only Qwen3.7 Plus measured
IFBench73.9%78.0%Qwen3.7 Plus
Long context reasoning (AA-LCR)52.7%73.0%Qwen3.7 Plus
DTBench61.3%84.0%Qwen3.7 Plus
LMCA10.3%37.6%Qwen3.7 Plus
LMArena Text13311454Qwen3.7 Plus
LMArena Text: instruction following13091439Qwen3.7 Plus
LMArena Text: creative writing13191438Qwen3.7 Plus
LMArena VisionNot measured1278Only Qwen3.7 Plus measured
LMArena Text: English13441458Qwen3.7 Plus
LMArena Text: non-English13131445Qwen3.7 Plus
LMArena Text: Chinese13271509Qwen3.7 Plus
LMArena Text: German13421471Qwen3.7 Plus
LMArena Text: Japanese12851412Qwen3.7 Plus
LMArena Text: Korean12851413Qwen3.7 Plus
LMArena Text: French13511472Qwen3.7 Plus
LMArena Text: Spanish13471457Qwen3.7 Plus
LMArena Text: Russian13141457Qwen3.7 Plus
LMArena Text: Polish13241449Qwen3.7 Plus
LMArena Text: multi-turn13381459Qwen3.7 Plus
LMArena Text: longer queries13341454Qwen3.7 Plus
LMArena Text: writing and language13281437Qwen3.7 Plus
LMArena Text: entertainment and media13061422Qwen3.7 Plus
LMArena Vision: EnglishNot measured1280Only Qwen3.7 Plus measured
LMArena Vision: ChineseNot measured1332Only Qwen3.7 Plus measured
LMArena Vision: reading textNot measured1288Only Qwen3.7 Plus measured
LMArena Vision: humorNot measured1261Only Qwen3.7 Plus measured
LMArena Vision: creative writingNot measured1258Only Qwen3.7 Plus measured
LMArena DocumentNot measured1444Only Qwen3.7 Plus 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
Image generation benchmarks for Command A and Qwen3.7 Plus
BenchmarkCommand AQwen3.7 PlusLeader
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
Qwen3.7 Plus$0.0010Fits in one request

Qwen3.7 Plus costs less

Repository review

50K input + 3K output tokens

Command A$0.155Fits in one request
Qwen3.7 Plus$0.020Fits in one request

Qwen3.7 Plus costs less

Agent loop

200K input + 10K output tokens

Command A$0.600Fits in one request
Qwen3.7 Plus$0.077Fits in one request

Qwen3.7 Plus 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 Qwen3.7 Plus
SpecificationCohere Command AQwen3.7 Plus
MakerCohereQwen
WeightsOpen weightsNo public weights listed
ReleasedMarch 13, 2025June 3, 2026
Context window256K1M
Input $/Mtok$2.50$0.32
Output $/Mtok$10.00$1.28
Coding indexNot benchmarked33.2
Independent measurements behind it12
Cost per solved task$2.69Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Command A better than Qwen3.7 Plus for coding?

Only Qwen3.7 Plus has a coding score, 33.2 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 Qwen3.7 Plus?

Command A costs $2.50 per million input tokens and $10.00 per million output tokens; Qwen3.7 Plus costs $0.32 and $1.28. Cost per solved task is known for Command A only.

Which has the bigger context window, Command A or Qwen3.7 Plus?

Qwen3.7 Plus, with 1M tokens against 256K.

How many benchmarks is the Command A vs Qwen3.7 Plus comparison based on?

42 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 Plus's on 2. Where only one model has a result, the page says so instead of naming a winner.

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