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Kimi K2.7 Code vs Qwen3 14B

Kimi K2.7 Code leads Qwen3 14B on the Artificial Analysis Intelligence Index, 25.8 to 6.7, and Qwen3 14B costs 17.4x less per output token ($0.23 vs $4.00 per million). Updated October 21, 2026; 11 benchmarks have a result for both.

Kimi K2.7 CodeMoonshot AI
Coding
53.4
Overall
25.8
Output $/M
$4.00
11
Qwen3 14BQwen
Coding
No data
Overall
6.7
Output $/M
$0.23
0
vs boards won of 11 shared

Kimi K2.7 Code or Qwen3 14B: which to pick

Only Kimi K2.7 Code has a coding score, 53.4 on the Vibe Coding Index; Qwen3 14B has none, so the two cannot be ranked on coding.

Pick Qwen3 14B for output tokens 17.4x cheaper ($0.23 vs $4.00 per million).

Shared results
11
Kimi K2.7 Code only
40
Qwen3 14B only
7
Like-for-like categories
6 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
Kimi K2.7 Code 53.4 from 4 benchmarks, Qwen3 14B not benchmarked.
Widest category
Agentic work, 55.6 points apart on average over 1 shared benchmark.
Widest single gap
Long context reasoning (AA-LCR): Kimi K2.7 Code 79.3% against Qwen3 14B 0.0%, 79.3 points apart on a 0 to 100 scale.
Output price
Kimi K2.7 Code $4.00 against Qwen3 14B $0.23 per million output tokens, 17.4x apart.
Context window
Kimi K2.7 Code takes 262K tokens, Qwen3 14B 41K.

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
CategoryKimi K2.7 CodeBarsQwen3 14BTies
Coding20
Intelligence20
Agentic work10
Reasoning30
Math10
General20
All shared boards110
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
Coding benchmarks for Kimi K2.7 Code and Qwen3 14B
BenchmarkKimi K2.7 CodeQwen3 14BLeader
LMArena WebDev1474Not measuredOnly Kimi K2.7 Code measured
AA Coding Index60.8Not measuredOnly Kimi K2.7 Code measured
LiveCodeBench (Vals AI)82.0%Not measuredOnly Kimi K2.7 Code measured
SWE-bench Verified (Vals AI)78.2%Not measuredOnly Kimi K2.7 Code measured
Vibe Code Bench (Vals AI)47.2%Not measuredOnly Kimi K2.7 Code measured
Code migration (Vals AI)25.4%Not measuredOnly Kimi K2.7 Code measured
ProgramBench (Vals AI)0.0%Not measuredOnly Kimi K2.7 Code measured
Terminal-Bench 2.1 (Vals AI)67.0%Not measuredOnly Kimi K2.7 Code measured
SkillsBench (Vals AI)50.0%Not measuredOnly Kimi K2.7 Code measured
LiveCodeBench (Artificial Analysis run)Not measured52.3%Only Qwen3 14B measured
SciCode47.8%30.7%Kimi K2.7 Code
Terminal-Bench Hard (Artificial Analysis run)44.7%5.3%Kimi K2.7 Code
FrontierCode30.1%Not measuredOnly Kimi K2.7 Code measured
WeirdML54.1%Not measuredOnly Kimi K2.7 Code measured
ALE-Bench886.2Not measuredOnly Kimi K2.7 Code measured
LMArena WebDev: React1475Not measuredOnly Kimi K2.7 Code measured
LMArena WebDev: HTML1451Not measuredOnly Kimi K2.7 Code measured
LMArena WebDev: from an image1513Not measuredOnly Kimi K2.7 Code measured
Neither model has a result on the other 20 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for Kimi K2.7 Code and Qwen3 14B
BenchmarkKimi K2.7 CodeQwen3 14BLeader
AA Intelligence Index25.86.7Kimi K2.7 Code
Epoch Capabilities Index150.1138.2Kimi K2.7 Code
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for Kimi K2.7 Code and Qwen3 14B
BenchmarkKimi K2.7 CodeQwen3 14BLeader
AA Agentic Index21.0Not measuredOnly Kimi K2.7 Code measured
Design Arena: agents building web apps1176Not measuredOnly Kimi K2.7 Code measured
Design Arena: agents building full-stack apps1175Not measuredOnly Kimi K2.7 Code measured
Design Arena: agents building mobile apps1143Not measuredOnly Kimi K2.7 Code measured
Design Arena: agents building Android apps1137Not measuredOnly Kimi K2.7 Code measured
Design Arena: agents building Godot games1147Not measuredOnly Kimi K2.7 Code measured
Design Arena: agents building games1131Not measuredOnly Kimi K2.7 Code measured
Design Arena: HTML slides1201Not measuredOnly Kimi K2.7 Code measured
Design Arena: PowerPoint slides1154Not measuredOnly Kimi K2.7 Code measured
τ²-Bench Telecom90.1%34.5%Kimi K2.7 Code
APEX-Agents37.6%Not measuredOnly Kimi K2.7 Code measured
Vending-Bench 2$5,083Not measuredOnly Kimi K2.7 Code measured
Neither model has a result on the other 27 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index30
Reasoning benchmarks for Kimi K2.7 Code and Qwen3 14B
BenchmarkKimi K2.7 CodeQwen3 14BLeader
GPQA Diamond (Artificial Analysis run)89.6%60.4%Kimi K2.7 Code
Humanity's Last Exam (Artificial Analysis run)35.0%4.5%Kimi K2.7 Code
GPQA Diamond (Epoch AI run)87.9%63.8%Kimi K2.7 Code
SimpleBench57.9%Not measuredOnly Kimi K2.7 Code measured
Chess puzzles14.0%Not measuredOnly Kimi K2.7 Code measured
Neither model has a result on the other 16 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index10
Math benchmarks for Kimi K2.7 Code and Qwen3 14B
BenchmarkKimi K2.7 CodeQwen3 14BLeader
AA Math IndexNot measured58.0Only Qwen3 14B measured
AIME 2025 (Artificial Analysis run)Not measured58.0%Only Qwen3 14B measured
OTIS Mock AIME95.6%66.4%Kimi K2.7 Code
FrontierMath, tiers 1 to 354.0%Not measuredOnly Kimi K2.7 Code measured
FrontierMath, tier 412.2%Not measuredOnly Kimi K2.7 Code measured
Neither model has a result on the other 8 boards here
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Knowledge benchmarks for Kimi K2.7 Code and Qwen3 14B
BenchmarkKimi K2.7 CodeQwen3 14BLeader
MMLU Pro (Artificial Analysis run)Not measured77.4%Only Qwen3 14B measured
SimpleQA Verified36.5%Not measuredOnly Kimi K2.7 Code measured
Neither model has a result on the other 15 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for Kimi K2.7 Code and Qwen3 14B
BenchmarkKimi K2.7 CodeQwen3 14BLeader
Design Arena: websites1264Not measuredOnly Kimi K2.7 Code measured
Design Arena: UI components1259Not measuredOnly Kimi K2.7 Code measured
Design Arena: game dev1220Not measuredOnly Kimi K2.7 Code measured
Design Arena: data viz1227Not measuredOnly Kimi K2.7 Code measured
Design Arena: 3D1241Not measuredOnly Kimi K2.7 Code measured
Design Arena: all code categories1253Not measuredOnly Kimi K2.7 Code measured
Design Arena: SVG1176Not measuredOnly Kimi K2.7 Code measured
Design Arena: ASCII art1196Not measuredOnly 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 index20
General benchmarks for Kimi K2.7 Code and Qwen3 14B
BenchmarkKimi K2.7 CodeQwen3 14BLeader
IFBench63.1%40.5%Kimi K2.7 Code
Long context reasoning (AA-LCR)79.3%0.0%Kimi K2.7 Code
DTBenchNot measured64.0%Only Qwen3 14B measured
LMCANot measured18.2%Only Qwen3 14B measured
Fiction.LiveBenchNot measured62.5%Only Qwen3 14B measured
Surface Evolver Bench48.8%Not measuredOnly Kimi K2.7 Code measured
Neither model has a result on the other 40 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Kimi K2.7 Code and Qwen3 14B
BenchmarkKimi K2.7 CodeQwen3 14BLeader
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

Kimi K2.7 Code$0.0029Fits in one request
Qwen3 14B$0.0002Fits in one request

Qwen3 14B costs less

Repository review

50K input + 3K output tokens

Kimi K2.7 Code$0.059Fits in one request
Qwen3 14B$0.0062Exceeds the context window

Qwen3 14B costs less

Agent loop

200K input + 10K output tokens

Kimi K2.7 Code$0.230Fits in one request
Qwen3 14B$0.024Exceeds the context window

Qwen3 14B 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 Kimi K2.7 Code and Qwen3 14B
SpecificationMoonshotAI Kimi K2.7 CodeQwen3 14B
MakerMoonshot AIQwen
WeightsOpen weightsOpen weights
ReleasedJune 12, 2026April 28, 2025
Context window262K41K
Input $/Mtok$0.95$0.11
Output $/Mtok$4.00$0.23
Coding index53.4Not benchmarked
Independent measurements behind it40
Cost per solved task$0.35Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Kimi K2.7 Code better than Qwen3 14B for coding?

Only Kimi K2.7 Code has a coding score, 53.4 on the Vibe Coding Index; Qwen3 14B has none, so the two cannot be ranked on coding. Pick Qwen3 14B for output tokens 17.4x cheaper ($0.23 vs $4.00 per million).

Which is cheaper, Kimi K2.7 Code or Qwen3 14B?

Kimi K2.7 Code costs $0.95 per million input tokens and $4.00 per million output tokens; Qwen3 14B costs $0.11 and $0.23. Cost per solved task is known for Kimi K2.7 Code only.

Which has the bigger context window, Kimi K2.7 Code or Qwen3 14B?

Kimi K2.7 Code, with 262K tokens against 41K.

How many benchmarks is the Kimi K2.7 Code vs Qwen3 14B comparison based on?

11 benchmarks have a result for both models as of October 21, 2026. Kimi K2.7 Code's coding index rests on 4 independent benchmarks and Qwen3 14B's on 0. Where only one model has a result, the page says so instead of naming a winner.

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