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GLM 5.2 vs Kimi K2.7 Code

GLM 5.2 leads Kimi K2.7 Code on coding, 61.1 to 53.4 on the Vibe Coding Index, at about the same output price ($4.40 vs $4.00 per million). Updated October 21, 2026; 50 benchmarks have a result for both.

Replace GLM 5.2 Replace Kimi K2.7 Code
GLM 5.2Z.ai
Coding
61.1
Overall
62.5
Output $/M
$4.40
40
Kimi K2.7 CodeMoonshot AI
Coding
53.4
Overall
56.7
Output $/M
$4.00
10
vs boards won of 50 shared

GLM 5.2 or Kimi K2.7 Code: which to pick

Pick GLM 5.2 for coding: it scores 61.1 on the Vibe Coding Index against 53.4 for Kimi K2.7 Code.

Pick Kimi K2.7 Code for output tokens 1.1x cheaper ($4.00 vs $4.40 per million) and a lower cost per solved task ($0.35 against $0.86).

Model AGLM 5.2Z.ai61.1/1004 independent benchmarks$0.86 per solved taskModel BKimi K2.7 CodeMoonshot AI53.4/1004 independent benchmarks$0.35 per solved task
Shared results
50
GLM 5.2 only
48
Kimi K2.7 Code only
1
Like-for-like categories
8 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
GLM 5.2 61.1 from 4 benchmarks, Kimi K2.7 Code 53.4 from 4 benchmarks.
Widest category
Math, 10.5 points apart on average over 3 shared benchmarks.
Widest single gap
LMArena WebDev: React: GLM 5.2 1617 against Kimi K2.7 Code 1475, 20.8 points apart on a 0 to 100 scale.
Output price
GLM 5.2 $4.40 against Kimi K2.7 Code $4.00 per million output tokens, 1.1x apart.
Context window
GLM 5.2 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 5.2BarsKimi K2.7 CodeTies
Coding133
Intelligence20
Agentic work102
Reasoning41
Math21
Knowledge01
Web design71
General21
All shared boards4010
CodingLike-for-likeSix of these boards feed the Vibe Coding Index133
Coding benchmarks for GLM 5.2 and Kimi K2.7 Code
BenchmarkGLM 5.2Kimi K2.7 CodeLeader
LMArena WebDev16041474GLM 5.2
AA Coding Index68.860.8GLM 5.2
LiveCodeBench (Vals AI)69.5%82.0%Kimi K2.7 Code
SWE-bench Verified (Vals AI)82.8%78.2%GLM 5.2
Vibe Code Bench (Vals AI)64.0%47.2%GLM 5.2
Code migration (Vals AI)37.9%25.4%GLM 5.2
ProgramBench (Vals AI)0.5%0.0%GLM 5.2
Terminal-Bench 2.1 (Vals AI)67.8%67.0%GLM 5.2
SkillsBench (Vals AI)45.1%50.0%Kimi K2.7 Code
SciCode51.2%47.8%GLM 5.2
Terminal-Bench Hard (Artificial Analysis run)50.8%44.7%GLM 5.2
FrontierCode24.5%30.1%Kimi K2.7 Code
WeirdML70.1%54.1%GLM 5.2
ALE-Bench1047.0886.2GLM 5.2
LMArena Text: coding1487Not measuredOnly GLM 5.2 measured
LMArena Text: software and IT1486Not measuredOnly GLM 5.2 measured
LMArena WebDev: React16171475GLM 5.2
LMArena WebDev: HTML15651451GLM 5.2
LMArena WebDev: from an imageNot measured1513Only 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 GLM 5.2 and Kimi K2.7 Code
BenchmarkGLM 5.2Kimi K2.7 CodeLeader
AA Intelligence Index33.725.8GLM 5.2
Epoch Capabilities Index151.9150.1GLM 5.2
Agentic workLike-for-likePublic boards, shown as published, kept out of the index102
Agentic work benchmarks for GLM 5.2 and Kimi K2.7 Code
BenchmarkGLM 5.2Kimi K2.7 CodeLeader
AA Agentic Index38.421.0GLM 5.2
Design Arena: agents building web apps12221176GLM 5.2
Design Arena: agents building full-stack apps12411175GLM 5.2
Design Arena: agents building mobile apps11601143GLM 5.2
Design Arena: agents building Android apps11781137GLM 5.2
Design Arena: agents building Godot games11421147Kimi K2.7 Code
Design Arena: agents building games12061131GLM 5.2
Design Arena: HTML slides11751201Kimi K2.7 Code
Design Arena: PowerPoint slides11951154GLM 5.2
SREBench (Vals AI)0.0%Not measuredOnly GLM 5.2 measured
Finance Agent (Vals AI)49.7%Not measuredOnly GLM 5.2 measured
Legal Research Bench (Vals AI)31.3%Not measuredOnly GLM 5.2 measured
Excel Modeling Benchmark (Vals AI)61.5%Not measuredOnly GLM 5.2 measured
τ²-Bench Telecom99.1%90.1%GLM 5.2
APEX-Agents45.2%37.6%GLM 5.2
PostTrainBench31.7%Not measuredOnly GLM 5.2 measured
Vending-Bench 2$8,314$5,083GLM 5.2
Neither model has a result on the other 24 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index41
Reasoning benchmarks for GLM 5.2 and Kimi K2.7 Code
BenchmarkGLM 5.2Kimi K2.7 CodeLeader
GPQA Diamond85.6%Not measuredOnly GLM 5.2 measured
GPQA Diamond (Artificial Analysis run)89.5%89.6%Kimi K2.7 Code
Humanity's Last Exam (Artificial Analysis run)41.1%35.0%GLM 5.2
GPQA Diamond (Epoch AI run)91.9%87.9%GLM 5.2
ARC-AGI77.0%Not measuredOnly GLM 5.2 measured
ARC-AGI-222.8%Not measuredOnly GLM 5.2 measured
SimpleBench58.8%57.9%GLM 5.2
Chess puzzles22.0%14.0%GLM 5.2
Mystery game puzzles19.0%Not measuredOnly GLM 5.2 measured
EBR-bench9.5%Not measuredOnly GLM 5.2 measured
LMArena Text: hard prompts1481Not measuredOnly GLM 5.2 measured
LMArena Text: expert1487Not measuredOnly GLM 5.2 measured
LMArena Text: hard prompts in English1481Not measuredOnly GLM 5.2 measured
Kagi LLM Benchmark62.6%Not measuredOnly GLM 5.2 measured
Neither model has a result on the other 8 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index21
Math benchmarks for GLM 5.2 and Kimi K2.7 Code
BenchmarkGLM 5.2Kimi K2.7 CodeLeader
OTIS Mock AIME86.4%95.6%Kimi K2.7 Code
FrontierMath, tiers 1 to 359.2%54.0%GLM 5.2
FrontierMath, tier 429.3%12.2%GLM 5.2
ProofBench35.0%Not measuredOnly GLM 5.2 measured
LMArena Text: math1483Not measuredOnly GLM 5.2 measured
LMArena Text: mathematical work1489Not measuredOnly GLM 5.2 measured
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index01
Knowledge benchmarks for GLM 5.2 and Kimi K2.7 Code
BenchmarkGLM 5.2Kimi K2.7 CodeLeader
MMLU Pro86.7%Not measuredOnly GLM 5.2 measured
LegalBench (Vals AI)84.1%Not measuredOnly GLM 5.2 measured
CorpFin (Vals AI)66.1%Not measuredOnly GLM 5.2 measured
TaxEval (Vals AI)73.3%Not measuredOnly GLM 5.2 measured
MedCode (Vals AI)40.8%Not measuredOnly GLM 5.2 measured
SimpleQA Verified34.2%36.5%Kimi K2.7 Code
LMArena Text: science1489Not measuredOnly GLM 5.2 measured
LMArena Text: medicine and healthcare1475Not measuredOnly GLM 5.2 measured
LMArena Text: legal and government1486Not measuredOnly GLM 5.2 measured
LMArena Text: business and finance1451Not measuredOnly GLM 5.2 measured
Neither model has a result on the other 8 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index71
Web design benchmarks for GLM 5.2 and Kimi K2.7 Code
BenchmarkGLM 5.2Kimi K2.7 CodeLeader
Design Arena: websites12851264GLM 5.2
Design Arena: UI components12961259GLM 5.2
Design Arena: game dev12681220GLM 5.2
Design Arena: data viz12851227GLM 5.2
Design Arena: 3D12881241GLM 5.2
Design Arena: all code categories12881253GLM 5.2
Design Arena: SVG12141176GLM 5.2
Design Arena: ASCII art11901196Kimi K2.7 Code
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index21
General benchmarks for GLM 5.2 and Kimi K2.7 Code
BenchmarkGLM 5.2Kimi K2.7 CodeLeader
MedScribe (Vals AI)83.5%Not measuredOnly GLM 5.2 measured
IFBench73.3%63.1%GLM 5.2
Long context reasoning (AA-LCR)78.3%79.3%Kimi K2.7 Code
DTBench93.6%Not measuredOnly GLM 5.2 measured
LMCA45.8%Not measuredOnly GLM 5.2 measured
Surface Evolver Bench55.6%48.8%GLM 5.2
LMArena Text1470Not measuredOnly GLM 5.2 measured
LMArena Text: instruction following1466Not measuredOnly GLM 5.2 measured
LMArena Text: creative writing1463Not measuredOnly GLM 5.2 measured
LMArena Text: English1475Not measuredOnly GLM 5.2 measured
LMArena Text: non-English1459Not measuredOnly GLM 5.2 measured
LMArena Text: Chinese1522Not measuredOnly GLM 5.2 measured
LMArena Text: German1470Not measuredOnly GLM 5.2 measured
LMArena Text: Japanese1450Not measuredOnly GLM 5.2 measured
LMArena Text: Korean1440Not measuredOnly GLM 5.2 measured
LMArena Text: French1479Not measuredOnly GLM 5.2 measured
LMArena Text: Spanish1478Not measuredOnly GLM 5.2 measured
LMArena Text: Russian1468Not measuredOnly GLM 5.2 measured
LMArena Text: Polish1456Not measuredOnly GLM 5.2 measured
LMArena Text: multi-turn1469Not measuredOnly GLM 5.2 measured
LMArena Text: longer queries1480Not measuredOnly GLM 5.2 measured
LMArena Text: writing and language1463Not measuredOnly GLM 5.2 measured
LMArena Text: entertainment and media1450Not measuredOnly GLM 5.2 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 GLM 5.2 and Kimi K2.7 Code
BenchmarkGLM 5.2Kimi 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 5.2$0.0036Fits 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

GLM 5.2$0.083Fits 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

GLM 5.2$0.324Fits 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 GLM 5.2 and Kimi K2.7 Code
SpecificationZ.ai GLM 5.2MoonshotAI Kimi K2.7 Code
MakerZ.aiMoonshot AI
WeightsOpen weightsOpen weights
ReleasedJune 16, 2026June 12, 2026
Context window1.05M262K
Input $/Mtok$1.40$0.95
Output $/Mtok$4.40$4.00
Coding index61.153.4
Independent measurements behind it44
Cost per solved task$0.86$0.35
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 5.2 better than Kimi K2.7 Code for coding?

Pick GLM 5.2 for coding: it scores 61.1 on the Vibe Coding Index against 53.4 for Kimi K2.7 Code. Pick Kimi K2.7 Code for output tokens 1.1x cheaper ($4.00 vs $4.40 per million) and a lower cost per solved task ($0.35 against $0.86).

Which is cheaper, GLM 5.2 or Kimi K2.7 Code?

GLM 5.2 costs $1.40 per million input tokens and $4.40 per million output tokens; Kimi K2.7 Code costs $0.95 and $4.00. Per solved SWE-bench Verified task, GLM 5.2 costs $0.86 and Kimi K2.7 Code $0.35.

Which has the bigger context window, GLM 5.2 or Kimi K2.7 Code?

GLM 5.2, with 1.05M tokens against 262K.

How many benchmarks is the GLM 5.2 vs Kimi K2.7 Code comparison based on?

50 benchmarks have a result for both models as of October 21, 2026. GLM 5.2's coding index rests on 4 independent benchmarks 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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