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GLM 5.3 Flash vs Kimi K2.7 Code

Updated October 21, 2026. 36 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GLM 5.3 Flash Replace Kimi K2.7 Code
GLM 5.3 FlashZ.ai
Coding
71.0
Overall
41.8
Output $/M
$0.50
28
Kimi K2.7 CodeMoonshot AI
Coding
39.1
Overall
25.8
Output $/M
$3.35
7
vs boards won of 36 shared, 1 tied

Decision reading

Pick GLM 5.3 Flash for coding: it scores 71.0 on the Vibe Coding Index against 39.1 for Kimi K2.7 Code.

Pick Kimi K2.7 Code when what matters is LiveCodeBench (Vals AI) or Vibe Code Bench (Vals AI), where it leads.

Model AGLM 5.3 FlashZ.ai71.0/1002 independent benchmarks$0.02 per solved taskModel BKimi K2.7 CodeMoonshot AI39.1/1001 independent benchmark$0.35 per solved task
Shared results
36
GLM 5.3 Flash only
66
Kimi K2.7 Code only
15
Like-for-like categories
7 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
GLM 5.3 Flash 71.0 from 2 benchmarks, Kimi K2.7 Code 39.1 from 1 benchmark.
Widest category
Agentic work, 22.6 points apart on average over 2 shared benchmarks.
Widest single gap
AA Agentic Index: GLM 5.3 Flash 50.9 against Kimi K2.7 Code 21.0, 29.9 points apart on a 0 to 100 scale.
Output price
GLM 5.3 Flash $0.50 against Kimi K2.7 Code $3.35 per million output tokens, 6.7x apart.
Context window
GLM 5.3 Flash takes 1.05M 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
CategoryGLM 5.3 FlashBarsKimi K2.7 CodeTies
Coding861 tie
Intelligence20
Agentic work20
Reasoning40
Math21
Web design80
General20
All shared boards2871 tie
CodingLike-for-likeFour of these boards feed the Vibe Coding Index86
Coding benchmarks for GLM 5.3 Flash and Kimi K2.7 Code
BenchmarkGLM 5.3 FlashKimi K2.7 CodeLeader
LiveBench coding80.6%Not measuredOnly GLM 5.3 Flash measured
LMArena WebDev16071474GLM 5.3 Flash
AA Coding Index71.560.8GLM 5.3 Flash
LiveCodeBench (Vals AI)80.5%82.0%Kimi K2.7 Code
SWE-bench Verified (Vals AI)92.0%78.2%GLM 5.3 Flash
Terminal-Bench 4 (Vals AI)37.4%Not measuredOnly GLM 5.3 Flash measured
Vibe Code Bench (Vals AI)30.8%47.2%Kimi K2.7 Code
Code migration (Vals AI)20.5%25.4%Kimi K2.7 Code
ProgramBench (Vals AI)0.0%0.0%Tie
IOI (Vals AI)52.5%Not measuredOnly GLM 5.3 Flash measured
Terminal-Bench 2.1 (Vals AI)62.9%67.0%Kimi K2.7 Code
Vibe Code Bench 1-100 (Vals AI)16.0%Not measuredOnly GLM 5.3 Flash measured
SkillsBench (Vals AI)40.2%50.0%Kimi K2.7 Code
CyberBench (Vals AI)67.9%Not measuredOnly GLM 5.3 Flash measured
SciCode51.6%47.8%GLM 5.3 Flash
Terminal-Bench Hard (Artificial Analysis run)Not measured44.7%Only Kimi K2.7 Code measured
FrontierCode31.8%30.1%GLM 5.3 Flash
FrontierSWE18.1%Not measuredOnly GLM 5.3 Flash measured
WeirdMLNot measured54.1%Only Kimi K2.7 Code measured
ALE-Bench303.6886.2Kimi K2.7 Code
LMArena Text: coding1507Not measuredOnly GLM 5.3 Flash measured
LMArena Text: software and IT1500Not measuredOnly GLM 5.3 Flash measured
LMArena WebDev: React16241475GLM 5.3 Flash
LMArena WebDev: HTML15771451GLM 5.3 Flash
LMArena WebDev: from an image15781513GLM 5.3 Flash
Neither model has a result on the other 13 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for GLM 5.3 Flash and Kimi K2.7 Code
BenchmarkGLM 5.3 FlashKimi K2.7 CodeLeader
AA Intelligence Index41.825.8GLM 5.3 Flash
Epoch Capabilities Index153.1150.1GLM 5.3 Flash
Agentic workLike-for-likePublic boards, shown as published, kept out of the index20
Agentic work benchmarks for GLM 5.3 Flash and Kimi K2.7 Code
BenchmarkGLM 5.3 FlashKimi K2.7 CodeLeader
AA Agentic Index50.921.0GLM 5.3 Flash
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 measured1130Only 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
LiveBench research70.1%Not measuredOnly GLM 5.3 Flash measured
LiveBench browser84.0%Not measuredOnly GLM 5.3 Flash measured
LiveBench long agentic28.4%Not measuredOnly GLM 5.3 Flash measured
SREBench (Vals AI)0.8%Not measuredOnly GLM 5.3 Flash measured
Finance Agent (Vals AI)57.9%Not measuredOnly GLM 5.3 Flash measured
Tax Agent Bench (Vals AI)31.6%Not measuredOnly GLM 5.3 Flash measured
Legal Research Bench (Vals AI)45.2%Not measuredOnly GLM 5.3 Flash measured
Harvey's Legal Agent Benchmark (Vals AI)10.4%Not measuredOnly GLM 5.3 Flash measured
Excel Modeling Benchmark (Vals AI)55.9%Not measuredOnly GLM 5.3 Flash measured
Terminal-Bench-Science (Vals AI)2.9%Not measuredOnly GLM 5.3 Flash measured
τ²-Bench TelecomNot measured90.1%Only Kimi K2.7 Code measured
APEX-Agents52.8%37.6%GLM 5.3 Flash
Vending-Bench 2Not measured$5,083Only Kimi K2.7 Code measured
Neither model has a result on the other 17 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index40
Reasoning benchmarks for GLM 5.3 Flash and Kimi K2.7 Code
BenchmarkGLM 5.3 FlashKimi K2.7 CodeLeader
GPQA Diamond86.4%Not measuredOnly GLM 5.3 Flash measured
LiveBench reasoning77.6%Not measuredOnly GLM 5.3 Flash measured
MMMU Pro (Vals AI)86.0%Not measuredOnly GLM 5.3 Flash measured
GPQA Diamond (Artificial Analysis run)91.2%89.6%GLM 5.3 Flash
Humanity's Last Exam (Artificial Analysis run)39.9%35.0%GLM 5.3 Flash
GPQA Diamond (Epoch AI run)90.2%87.9%GLM 5.3 Flash
ARC-AGI91.0%Not measuredOnly GLM 5.3 Flash measured
ARC-AGI-265.8%Not measuredOnly GLM 5.3 Flash measured
SimpleBenchNot measured57.9%Only Kimi K2.7 Code measured
Chess puzzles21.0%14.0%GLM 5.3 Flash
Mystery game puzzles8.0%Not measuredOnly GLM 5.3 Flash measured
LMArena Text: hard prompts1490Not measuredOnly GLM 5.3 Flash measured
LMArena Text: expert1511Not measuredOnly GLM 5.3 Flash measured
LMArena Text: hard prompts in English1488Not measuredOnly GLM 5.3 Flash measured
LMArena Vision: diagrams1317Not measuredOnly GLM 5.3 Flash measured
LMArena Vision: homework1318Not measuredOnly GLM 5.3 Flash measured
Neither model has a result on the other 5 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index21
Math benchmarks for GLM 5.3 Flash and Kimi K2.7 Code
BenchmarkGLM 5.3 FlashKimi K2.7 CodeLeader
OTIS Mock AIME93.9%95.6%Kimi K2.7 Code
FrontierMath, tiers 1 to 355.8%54.0%GLM 5.3 Flash
FrontierMath, tier 417.1%12.2%GLM 5.3 Flash
ProofBench21.0%Not measuredOnly GLM 5.3 Flash measured
LMArena Text: math1503Not measuredOnly GLM 5.3 Flash measured
LMArena Text: mathematical work1497Not measuredOnly GLM 5.3 Flash measured
Neither model has a result on the other 7 boards here
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Knowledge benchmarks for GLM 5.3 Flash and Kimi K2.7 Code
BenchmarkGLM 5.3 FlashKimi K2.7 CodeLeader
MMLU Pro86.1%Not measuredOnly GLM 5.3 Flash measured
LegalBench (Vals AI)83.9%Not measuredOnly GLM 5.3 Flash measured
TaxEval (Vals AI)75.6%Not measuredOnly GLM 5.3 Flash measured
SimpleQA VerifiedNot measured36.5%Only Kimi K2.7 Code measured
LMArena Text: science1484Not measuredOnly GLM 5.3 Flash measured
LMArena Text: medicine and healthcare1475Not measuredOnly GLM 5.3 Flash measured
LMArena Text: legal and government1473Not measuredOnly GLM 5.3 Flash measured
LMArena Text: business and finance1460Not measuredOnly GLM 5.3 Flash measured
Neither model has a result on the other 9 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index80
Web design benchmarks for GLM 5.3 Flash and Kimi K2.7 Code
BenchmarkGLM 5.3 FlashKimi K2.7 CodeLeader
Design Arena: websites12721264GLM 5.3 Flash
Design Arena: UI components13161259GLM 5.3 Flash
Design Arena: game dev12791220GLM 5.3 Flash
Design Arena: data viz12621226GLM 5.3 Flash
Design Arena: 3D13161241GLM 5.3 Flash
Design Arena: all code categories12821253GLM 5.3 Flash
Design Arena: SVG12841176GLM 5.3 Flash
Design Arena: ASCII art12691196GLM 5.3 Flash
LiveBench design77.2%Not measuredOnly GLM 5.3 Flash measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
General benchmarks for GLM 5.3 Flash and Kimi K2.7 Code
BenchmarkGLM 5.3 FlashKimi K2.7 CodeLeader
Vals Index50.9%Not measuredOnly GLM 5.3 Flash measured
LiveBench data analysis76.4%Not measuredOnly GLM 5.3 Flash measured
MedScribe (Vals AI)88.9%Not measuredOnly GLM 5.3 Flash measured
IFBenchNot measured63.1%Only Kimi K2.7 Code measured
Long context reasoning (AA-LCR)80.0%79.3%GLM 5.3 Flash
Surface Evolver Bench52.5%48.8%GLM 5.3 Flash
LMArena Text1471Not measuredOnly GLM 5.3 Flash measured
LMArena Text: instruction following1478Not measuredOnly GLM 5.3 Flash measured
LMArena Text: creative writing1441Not measuredOnly GLM 5.3 Flash measured
LMArena Vision1294Not measuredOnly GLM 5.3 Flash measured
LMArena Text: English1474Not measuredOnly GLM 5.3 Flash measured
LMArena Text: non-English1462Not measuredOnly GLM 5.3 Flash measured
LMArena Text: Chinese1531Not measuredOnly GLM 5.3 Flash measured
LMArena Text: German1462Not measuredOnly GLM 5.3 Flash measured
LMArena Text: Japanese1444Not measuredOnly GLM 5.3 Flash measured
LMArena Text: Korean1436Not measuredOnly GLM 5.3 Flash measured
LMArena Text: French1491Not measuredOnly GLM 5.3 Flash measured
LMArena Text: Spanish1475Not measuredOnly GLM 5.3 Flash measured
LMArena Text: Russian1465Not measuredOnly GLM 5.3 Flash measured
LMArena Text: Polish1450Not measuredOnly GLM 5.3 Flash measured
LMArena Text: multi-turn1469Not measuredOnly GLM 5.3 Flash measured
LMArena Text: longer queries1481Not measuredOnly GLM 5.3 Flash measured
LMArena Text: writing and language1455Not measuredOnly GLM 5.3 Flash measured
LMArena Text: entertainment and media1440Not measuredOnly GLM 5.3 Flash measured
LMArena Vision: English1295Not measuredOnly GLM 5.3 Flash measured
LMArena Vision: Chinese1363Not measuredOnly GLM 5.3 Flash measured
LMArena Vision: reading text1299Not measuredOnly GLM 5.3 Flash measured
LMArena Vision: humor1279Not measuredOnly GLM 5.3 Flash measured
LMArena Vision: creative writing1270Not measuredOnly GLM 5.3 Flash measured
Neither model has a result on the other 17 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 5.3 Flash and Kimi K2.7 Code
BenchmarkGLM 5.3 FlashKimi 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

GLM 5.3 Flash$0.0004Fits in one request
Kimi K2.7 Code$0.0023Fits in one request

GLM 5.3 Flash costs less

Repository review

50K input + 3K output tokens

GLM 5.3 Flash$0.0090Fits in one request
Kimi K2.7 Code$0.044Fits in one request

GLM 5.3 Flash costs less

Agent loop

200K input + 10K output tokens

GLM 5.3 Flash$0.035Fits in one request
Kimi K2.7 Code$0.168Fits in one request

GLM 5.3 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 GLM 5.3 Flash and Kimi K2.7 Code
SpecificationZ.ai GLM 5.3 FlashMoonshotAI Kimi K2.7 Code
MakerZ.aiMoonshot AI
WeightsOpen weightsOpen weights
ReleasedAugust 26, 2026June 12, 2026
Context window1.05M262K
Input $/Mtok$0.15$0.6712
Output $/Mtok$0.50$3.35
Coding index71.039.1
Independent measurements behind it21
Cost per solved task$0.02$0.35
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 5.3 Flash better than Kimi K2.7 Code for coding?

Pick GLM 5.3 Flash for coding: it scores 71.0 on the Vibe Coding Index against 39.1 for Kimi K2.7 Code. Pick Kimi K2.7 Code when what matters is LiveCodeBench (Vals AI) or Vibe Code Bench (Vals AI), where it leads.

Which is cheaper, GLM 5.3 Flash or Kimi K2.7 Code?

GLM 5.3 Flash costs $0.15 per million input tokens and $0.50 per million output tokens; Kimi K2.7 Code costs $0.6712 and $3.35. Per solved SWE-bench Verified task, GLM 5.3 Flash costs $0.02 and Kimi K2.7 Code $0.35.

Which has the bigger context window, GLM 5.3 Flash or Kimi K2.7 Code?

GLM 5.3 Flash, with 1.05M tokens against 262K.

How many benchmarks is the GLM 5.3 Flash vs Kimi K2.7 Code comparison based on?

36 benchmarks have a result for both models as of October 21, 2026. GLM 5.3 Flash's coding index rests on 2 independent benchmarks and Kimi K2.7 Code's on 1. Where only one model has a result, the page says so instead of naming a winner.

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