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Command A vs Kimi K2.7 Code

Updated October 21, 2026. 13 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Command ACohere
Coding
No data
Overall
13.1
Output $/M
$10.00
1
Kimi K2.7 CodeMoonshot AI
Coding
53.4
Overall
25.8
Output $/M
$4.00
12
vs boards won of 13 shared

Decision reading

Only Kimi K2.7 Code has a coding score, 53.4 on the Vibe Coding Index; Command A has none, so the two cannot be ranked on coding.

Command A has the edge where the benchmark that matters is IFBench, where it leads.

Shared results
13
Command A only
47
Kimi K2.7 Code only
38
Like-for-like categories
5 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, Kimi K2.7 Code 53.4 from 4 benchmarks.
Widest category
Coding, 38.1 points apart on average over 6 shared benchmarks.
Widest single gap
SWE-bench Verified (Vals AI): Command A 7.8% against Kimi K2.7 Code 78.2%, 70.4 points apart on a 0 to 100 scale.
Output price
Command A $10.00 against Kimi K2.7 Code $4.00 per million output tokens, 2.5x apart.
Context window
Command A takes 256K tokens, Kimi K2.7 Code 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 ABarsKimi K2.7 CodeTies
Coding06
Intelligence01
Agentic work02
Reasoning02
General11
All shared boards112
CodingLike-for-likeSix of these boards feed the Vibe Coding Index06
Coding benchmarks for Command A and Kimi K2.7 Code
BenchmarkCommand AKimi K2.7 CodeLeader
LMArena WebDevNot measured1474Only Kimi K2.7 Code measured
AA Coding Index27.860.8Kimi K2.7 Code
LiveCodeBench (Vals AI)35.1%82.0%Kimi K2.7 Code
SWE-bench Verified (Vals AI)7.8%78.2%Kimi K2.7 Code
Vibe Code Bench (Vals AI)Not measured47.2%Only Kimi K2.7 Code measured
Code migration (Vals AI)Not measured25.4%Only Kimi K2.7 Code measured
ProgramBench (Vals AI)Not measured0.0%Only Kimi K2.7 Code measured
Terminal-Bench 2.1 (Vals AI)17.6%67.0%Kimi K2.7 Code
SkillsBench (Vals AI)Not measured50.0%Only Kimi K2.7 Code measured
LiveCodeBench (Artificial Analysis run)28.7%Not measuredOnly Command A measured
SciCode38.5%47.8%Kimi K2.7 Code
Terminal-Bench Hard (Artificial Analysis run)25.0%44.7%Kimi K2.7 Code
FrontierCodeNot measured30.1%Only Kimi K2.7 Code measured
WeirdMLNot measured54.1%Only Kimi K2.7 Code measured
ALE-BenchNot measured886.2Only Kimi K2.7 Code measured
LMArena Text: coding1330Not measuredOnly Command A measured
LMArena Text: software and IT1336Not measuredOnly Command A measured
LMArena WebDev: ReactNot measured1475Only Kimi K2.7 Code measured
LMArena WebDev: HTMLNot measured1451Only Kimi K2.7 Code measured
LMArena WebDev: from an imageNot measured1513Only Kimi K2.7 Code measured
Neither model has a result on the other 18 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for Command A and Kimi K2.7 Code
BenchmarkCommand AKimi K2.7 CodeLeader
AA Intelligence Index13.125.8Kimi K2.7 Code
Epoch Capabilities IndexNot measured150.1Only Kimi K2.7 Code measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index02
Agentic work benchmarks for Command A and Kimi K2.7 Code
BenchmarkCommand AKimi K2.7 CodeLeader
AA Agentic Index1.021.0Kimi K2.7 Code
Design Arena: agents building web appsNot measured1176Only Kimi K2.7 Code measured
Design Arena: agents building full-stack appsNot measured1175Only Kimi K2.7 Code measured
Design Arena: agents building mobile appsNot measured1143Only Kimi K2.7 Code measured
Design Arena: agents building Android appsNot measured1143Only Kimi K2.7 Code measured
Design Arena: agents building Godot gamesNot measured1147Only Kimi K2.7 Code measured
Design Arena: agents building gamesNot measured1131Only Kimi K2.7 Code measured
Design Arena: HTML slidesNot measured1201Only Kimi K2.7 Code measured
Design Arena: PowerPoint slidesNot measured1154Only Kimi K2.7 Code measured
Finance Agent (Vals AI)9.0%Not measuredOnly Command A measured
τ²-Bench Telecom80.7%90.1%Kimi K2.7 Code
APEX-AgentsNot measured37.6%Only Kimi K2.7 Code measured
Vending-Bench 2Not measured$5,083Only Kimi K2.7 Code measured
Neither model has a result on the other 26 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
Reasoning benchmarks for Command A and Kimi K2.7 Code
BenchmarkCommand AKimi K2.7 CodeLeader
GPQA Diamond48.5%Not measuredOnly Command A measured
GPQA Diamond (Artificial Analysis run)76.1%89.6%Kimi K2.7 Code
Humanity's Last Exam (Artificial Analysis run)12.0%35.0%Kimi K2.7 Code
GPQA Diamond (Epoch AI run)Not measured87.9%Only Kimi K2.7 Code measured
SimpleBenchNot measured57.9%Only Kimi K2.7 Code measured
Chess puzzlesNot measured14.0%Only Kimi K2.7 Code 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 12 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
Math benchmarks for Command A and Kimi K2.7 Code
BenchmarkCommand AKimi K2.7 CodeLeader
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 measured95.6%Only Kimi K2.7 Code measured
FrontierMath, tiers 1 to 3Not measured54.0%Only Kimi K2.7 Code measured
FrontierMath, tier 4Not measured12.2%Only Kimi K2.7 Code measured
LMArena Text: math1300Not measuredOnly Command A measured
LMArena Text: mathematical work1312Not measuredOnly Command A measured
Neither model has a result on the other 3 boards here
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index13 boards, none shared
Knowledge benchmarks for Command A and Kimi K2.7 Code
BenchmarkCommand AKimi K2.7 CodeLeader
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
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
SimpleQA VerifiedNot measured36.5%Only Kimi K2.7 Code 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 designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for Command A and Kimi K2.7 Code
BenchmarkCommand AKimi K2.7 CodeLeader
Design Arena: websitesNot measured1264Only Kimi K2.7 Code measured
Design Arena: UI componentsNot measured1259Only Kimi K2.7 Code measured
Design Arena: game devNot measured1220Only Kimi K2.7 Code measured
Design Arena: data vizNot measured1227Only Kimi K2.7 Code measured
Design Arena: 3DNot measured1241Only Kimi K2.7 Code measured
Design Arena: all code categoriesNot measured1253Only Kimi K2.7 Code measured
Design Arena: SVGNot measured1176Only Kimi K2.7 Code measured
Design Arena: ASCII artNot measured1196Only Kimi K2.7 Code measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index11
General benchmarks for Command A and Kimi K2.7 Code
BenchmarkCommand AKimi K2.7 CodeLeader
MedScribe (Vals AI)55.7%Not measuredOnly Command A measured
IFBench73.9%63.1%Command A
Long context reasoning (AA-LCR)52.7%79.3%Kimi K2.7 Code
DTBench61.3%Not measuredOnly Command A measured
LMCA10.3%Not measuredOnly Command A measured
Surface Evolver BenchNot measured48.8%Only Kimi K2.7 Code measured
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 Kimi K2.7 Code
BenchmarkCommand AKimi K2.7 CodeLeader
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
Kimi K2.7 Code$0.0029Fits in one request

Kimi K2.7 Code costs less

Repository review

50K input + 3K output tokens

Command A$0.155Fits in one request
Kimi K2.7 Code$0.059Fits in one request

Kimi K2.7 Code costs less

Agent loop

200K input + 10K output tokens

Command A$0.600Fits in one request
Kimi K2.7 Code$0.230Fits in one request

Kimi K2.7 Code 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 Kimi K2.7 Code
SpecificationCohere Command AMoonshotAI Kimi K2.7 Code
MakerCohereMoonshot AI
WeightsOpen weightsOpen weights
ReleasedMarch 13, 2025June 12, 2026
Context window256K262K
Input $/Mtok$2.50$0.95
Output $/Mtok$10.00$4.00
Coding indexNot benchmarked53.4
Independent measurements behind it14
Cost per solved task$2.69$0.35
SourceOpenRouter listingOpenRouter listing

Questions

Is Command A better than Kimi K2.7 Code for coding?

Only Kimi K2.7 Code has a coding score, 53.4 on the Vibe Coding Index; Command A has none, so the two cannot be ranked on coding. Command A has the edge where the benchmark that matters is IFBench, where it leads.

Which is cheaper, Command A or Kimi K2.7 Code?

Command A costs $2.50 per million input tokens and $10.00 per million output tokens; Kimi K2.7 Code costs $0.95 and $4.00. Per solved SWE-bench Verified task, Command A costs $2.69 and Kimi K2.7 Code $0.35.

Which has the bigger context window, Command A or Kimi K2.7 Code?

Kimi K2.7 Code, with 262K tokens against 256K.

How many benchmarks is the Command A vs Kimi K2.7 Code comparison based on?

13 benchmarks have a result for both models as of October 21, 2026. Command A's coding index rests on 1 independent benchmark and Kimi K2.7 Code's on 4. Where only one model has a result, the page says so instead of naming a winner.

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