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Kimi K2.7 Code vs Qwen3.5-Flash

Updated October 21, 2026. 12 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Kimi K2.7 CodeMoonshot AI
Coding
39.1
Overall
25.8
Output $/M
$3.35
10
Qwen3.5-FlashQwen
Coding
6.2
Overall
No data
Output $/M
$0.26
1
vs boards won of 12 shared, 1 tied

Decision reading

Pick Kimi K2.7 Code for coding: it scores 39.1 on the Vibe Coding Index against 6.2 for Qwen3.5-Flash.

Pick Qwen3.5-Flash when output tokens cost 12.9x less, or when the context window is larger, 1M against 262K tokens.

Shared results
12
Kimi K2.7 Code only
39
Qwen3.5-Flash only
43
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
Kimi K2.7 Code 39.1 from 1 benchmark, Qwen3.5-Flash 6.2 from 1 benchmark.
Widest category
Math, 23.5 points apart on average over 2 shared benchmarks.
Widest single gap
LMArena WebDev: React: Kimi K2.7 Code 1475 against Qwen3.5-Flash 1229, 41.0 points apart on a 0 to 100 scale.
Output price
Kimi K2.7 Code $3.35 against Qwen3.5-Flash $0.26 per million output tokens, 12.9x apart.
Context window
Kimi K2.7 Code takes 262K tokens, Qwen3.5-Flash 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
CategoryKimi K2.7 CodeBarsQwen3.5-FlashTies
Coding51
Intelligence10
Reasoning101 tie
Math20
Knowledge10
All shared boards1011 tie
CodingLike-for-likeFour of these boards feed the Vibe Coding Index51
Coding benchmarks for Kimi K2.7 Code and Qwen3.5-Flash
BenchmarkKimi K2.7 CodeQwen3.5-FlashLeader
LMArena WebDev14741244Kimi K2.7 Code
AA Coding Index60.8Not measuredOnly Kimi K2.7 Code measured
LiveCodeBench (Vals AI)82.0%83.3%Qwen3.5-Flash
SWE-bench Verified (Vals AI)78.2%64.4%Kimi K2.7 Code
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
SciCode47.8%Not measuredOnly Kimi K2.7 Code measured
Terminal-Bench Hard (Artificial Analysis run)44.7%Not measuredOnly Kimi K2.7 Code measured
FrontierCode30.1%Not measuredOnly Kimi K2.7 Code measured
WeirdML54.1%Not measuredOnly Kimi K2.7 Code measured
ALE-Bench886.2265.9Kimi K2.7 Code
LMArena Text: codingNot measured1412Only Qwen3.5-Flash measured
LMArena Text: software and ITNot measured1416Only Qwen3.5-Flash measured
LMArena WebDev: React14751229Kimi K2.7 Code
LMArena WebDev: HTML14511291Kimi K2.7 Code
LMArena WebDev: from an image1513Not measuredOnly Kimi K2.7 Code measured
Neither model has a result on the other 19 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for Kimi K2.7 Code and Qwen3.5-Flash
BenchmarkKimi K2.7 CodeQwen3.5-FlashLeader
AA Intelligence Index25.8Not measuredOnly Kimi K2.7 Code measured
Epoch Capabilities Index150.1143.5Kimi K2.7 Code
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index12 boards, none shared
Agentic work benchmarks for Kimi K2.7 Code and Qwen3.5-Flash
BenchmarkKimi K2.7 CodeQwen3.5-FlashLeader
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 apps1143Not measuredOnly Kimi K2.7 Code measured
Design Arena: agents building Godot games1147Not measuredOnly Kimi K2.7 Code measured
Design Arena: agents building games1130Not 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%Not measuredOnly Kimi K2.7 Code measured
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 index10
Reasoning benchmarks for Kimi K2.7 Code and Qwen3.5-Flash
BenchmarkKimi K2.7 CodeQwen3.5-FlashLeader
GPQA DiamondNot measured82.8%Only Qwen3.5-Flash measured
MMMU Pro (Vals AI)Not measured81.9%Only Qwen3.5-Flash measured
GPQA Diamond (Artificial Analysis run)89.6%Not measuredOnly Kimi K2.7 Code measured
Humanity's Last Exam (Artificial Analysis run)35.0%Not measuredOnly Kimi K2.7 Code measured
GPQA Diamond (Epoch AI run)87.9%82.3%Kimi K2.7 Code
SimpleBench57.9%Not measuredOnly Kimi K2.7 Code measured
Chess puzzles14.0%14.0%Tie
Mystery game puzzlesNot measured20.0%Only Qwen3.5-Flash measured
LMArena Text: hard promptsNot measured1403Only Qwen3.5-Flash measured
LMArena Text: expertNot measured1407Only Qwen3.5-Flash measured
LMArena Text: hard prompts in EnglishNot measured1409Only Qwen3.5-Flash measured
Neither model has a result on the other 10 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index20
Math benchmarks for Kimi K2.7 Code and Qwen3.5-Flash
BenchmarkKimi K2.7 CodeQwen3.5-FlashLeader
AIMENot measured92.5%Only Qwen3.5-Flash measured
OTIS Mock AIME95.6%84.4%Kimi K2.7 Code
FrontierMath, tiers 1 to 354.0%18.2%Kimi K2.7 Code
FrontierMath, tier 412.2%Not measuredOnly Kimi K2.7 Code measured
LMArena Text: mathNot measured1407Only Qwen3.5-Flash measured
LMArena Text: mathematical workNot measured1413Only Qwen3.5-Flash measured
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
Knowledge benchmarks for Kimi K2.7 Code and Qwen3.5-Flash
BenchmarkKimi K2.7 CodeQwen3.5-FlashLeader
MMLU ProNot measured84.1%Only Qwen3.5-Flash measured
LegalBench (Vals AI)Not measured84.3%Only Qwen3.5-Flash measured
CaseLaw (Vals AI)Not measured56.0%Only Qwen3.5-Flash measured
CorpFin (Vals AI)Not measured63.6%Only Qwen3.5-Flash measured
TaxEval (Vals AI)Not measured72.2%Only Qwen3.5-Flash measured
MortgageTax (Vals AI)Not measured67.4%Only Qwen3.5-Flash measured
MedCode (Vals AI)Not measured33.0%Only Qwen3.5-Flash measured
SimpleQA Verified36.5%20.3%Kimi K2.7 Code
LMArena Text: scienceNot measured1415Only Qwen3.5-Flash measured
LMArena Text: medicine and healthcareNot measured1407Only Qwen3.5-Flash measured
LMArena Text: legal and governmentNot measured1398Only Qwen3.5-Flash measured
LMArena Text: business and financeNot measured1394Only Qwen3.5-Flash measured
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 Kimi K2.7 Code and Qwen3.5-Flash
BenchmarkKimi K2.7 CodeQwen3.5-FlashLeader
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 viz1226Not 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
GeneralDirectional onlyPublic boards, shown as published, kept out of the index24 boards, none shared
General benchmarks for Kimi K2.7 Code and Qwen3.5-Flash
BenchmarkKimi K2.7 CodeQwen3.5-FlashLeader
SAGE (Vals AI)Not measured42.5%Only Qwen3.5-Flash measured
MedScribe (Vals AI)Not measured70.6%Only Qwen3.5-Flash measured
IFBench63.1%Not measuredOnly Kimi K2.7 Code measured
Long context reasoning (AA-LCR)79.3%Not measuredOnly Kimi K2.7 Code measured
DTBenchNot measured82.9%Only Qwen3.5-Flash measured
LMCANot measured29.1%Only Qwen3.5-Flash measured
Surface Evolver Bench48.8%Not measuredOnly Kimi K2.7 Code measured
LMArena TextNot measured1397Only Qwen3.5-Flash measured
LMArena Text: instruction followingNot measured1374Only Qwen3.5-Flash measured
LMArena Text: creative writingNot measured1343Only Qwen3.5-Flash measured
LMArena Text: EnglishNot measured1404Only Qwen3.5-Flash measured
LMArena Text: non-EnglishNot measured1385Only Qwen3.5-Flash measured
LMArena Text: ChineseNot measured1445Only Qwen3.5-Flash measured
LMArena Text: GermanNot measured1390Only Qwen3.5-Flash measured
LMArena Text: JapaneseNot measured1369Only Qwen3.5-Flash measured
LMArena Text: KoreanNot measured1344Only Qwen3.5-Flash measured
LMArena Text: FrenchNot measured1413Only Qwen3.5-Flash measured
LMArena Text: SpanishNot measured1400Only Qwen3.5-Flash measured
LMArena Text: RussianNot measured1380Only Qwen3.5-Flash measured
LMArena Text: PolishNot measured1376Only Qwen3.5-Flash measured
LMArena Text: multi-turnNot measured1393Only Qwen3.5-Flash measured
LMArena Text: longer queriesNot measured1392Only Qwen3.5-Flash measured
LMArena Text: writing and languageNot measured1369Only Qwen3.5-Flash measured
LMArena Text: entertainment and mediaNot measured1347Only Qwen3.5-Flash measured
Neither model has a result on the other 22 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 Qwen3.5-Flash
BenchmarkKimi K2.7 CodeQwen3.5-FlashLeader
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$0.0023Fits in one request
Qwen3.5-Flash$0.0002Fits in one request

Qwen3.5-Flash costs less

Repository review

50K input + 3K output tokens

Kimi K2.7 Code$0.044Fits in one request
Qwen3.5-Flash$0.0040Fits in one request

Qwen3.5-Flash costs less

Agent loop

200K input + 10K output tokens

Kimi K2.7 Code$0.168Fits in one request
Qwen3.5-Flash$0.016Fits in one request

Qwen3.5-Flash 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 Qwen3.5-Flash
SpecificationMoonshotAI Kimi K2.7 CodeQwen3.5-Flash
MakerMoonshot AIQwen
WeightsOpen weightsNo public weights listed
ReleasedJune 12, 2026February 25, 2026
Context window262K1M
Input $/Mtok$0.6712$0.065
Output $/Mtok$3.35$0.26
Coding index39.16.2
Independent measurements behind it11
Cost per solved task$0.35$0.40
SourceOpenRouter listingOpenRouter listing

Questions

Is Kimi K2.7 Code better than Qwen3.5-Flash for coding?

Pick Kimi K2.7 Code for coding: it scores 39.1 on the Vibe Coding Index against 6.2 for Qwen3.5-Flash. Pick Qwen3.5-Flash when output tokens cost 12.9x less, or when the context window is larger, 1M against 262K tokens.

Which is cheaper, Kimi K2.7 Code or Qwen3.5-Flash?

Kimi K2.7 Code costs $0.6712 per million input tokens and $3.35 per million output tokens; Qwen3.5-Flash costs $0.065 and $0.26. Per solved SWE-bench Verified task, Kimi K2.7 Code costs $0.35 and Qwen3.5-Flash $0.40.

Which has the bigger context window, Kimi K2.7 Code or Qwen3.5-Flash?

Qwen3.5-Flash, with 1M tokens against 262K.

How many benchmarks is the Kimi K2.7 Code vs Qwen3.5-Flash comparison based on?

12 benchmarks have a result for both models as of October 21, 2026. Kimi K2.7 Code's coding index rests on 1 independent benchmark and Qwen3.5-Flash's on 1. Where only one model has a result, the page says so instead of naming a winner.

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