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GLM 5 vs Kimi K2.7 Code

Kimi K2.7 Code leads GLM 5 on coding, 53.4 to 41.7 on the Vibe Coding Index, and GLM 5 costs 1.3x less per output token ($3.20 vs $4.00 per million). Updated October 21, 2026; 33 benchmarks have a result for both.

Replace GLM 5 Replace Kimi K2.7 Code
GLM 5Z.ai
Coding
41.7
Overall
54.3
Output $/M
$3.20
6
Kimi K2.7 CodeMoonshot AI
Coding
53.4
Overall
56.7
Output $/M
$4.00
27
vs boards won of 33 shared

GLM 5 or Kimi K2.7 Code: which to pick

Pick Kimi K2.7 Code for coding: it scores 53.4 on the Vibe Coding Index against 41.7 for GLM 5.

Pick GLM 5 for output tokens 1.3x cheaper ($3.20 vs $4.00 per million) and work measured by AA Intelligence Index or Design Arena: agents building Android apps, where it leads.

Model AGLM 5Z.ai41.7/1003 independent benchmarks$0.63 per solved taskModel BKimi K2.7 CodeMoonshot AI53.4/1004 independent benchmarks$0.35 per solved task
Shared results
33
GLM 5 only
43
Kimi K2.7 Code only
18
Like-for-like categories
7 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 41.7 from 3 benchmarks, Kimi K2.7 Code 53.4 from 4 benchmarks.
Widest category
Math, 15.6 points apart on average over 1 shared benchmark.
Widest single gap
Vibe Code Bench (Vals AI): GLM 5 23.4% against Kimi K2.7 Code 47.2%, 23.9 points apart on a 0 to 100 scale.
Output price
GLM 5 $3.20 against Kimi K2.7 Code $4.00 per million output tokens, 1.3x apart.
Context window
GLM 5 takes 205K 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 5BarsKimi K2.7 CodeTies
Coding09
Intelligence11
Agentic work25
Reasoning04
Math01
Web design26
General11
All shared boards627
CodingLike-for-likeSix of these boards feed the Vibe Coding Index09
Coding benchmarks for GLM 5 and Kimi K2.7 Code
BenchmarkGLM 5Kimi K2.7 CodeLeader
SWE-bench Verified72.8%Not measuredOnly GLM 5 measured
SWE-bench Multilingual69.7%Not measuredOnly GLM 5 measured
LMArena WebDev14351474Kimi K2.7 Code
AA Coding IndexNot measured60.8Only Kimi K2.7 Code measured
LiveCodeBench (Vals AI)81.9%82.0%Kimi K2.7 Code
SWE-bench Verified (Vals AI)71.4%78.2%Kimi K2.7 Code
Vibe Code Bench (Vals AI)23.4%47.2%Kimi K2.7 Code
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)Not measured67.0%Only Kimi K2.7 Code measured
SkillsBench (Vals AI)Not measured50.0%Only Kimi K2.7 Code measured
SciCodeNot measured47.8%Only Kimi K2.7 Code measured
Terminal-Bench Hard (Artificial Analysis run)43.2%44.7%Kimi K2.7 Code
Terminal-Bench52.4%Not measuredOnly GLM 5 measured
FrontierCodeNot measured30.1%Only Kimi K2.7 Code measured
WeirdML48.2%54.1%Kimi K2.7 Code
ALE-Bench765.6886.2Kimi K2.7 Code
LMArena Text: coding1461Not measuredOnly GLM 5 measured
LMArena Text: software and IT1460Not measuredOnly GLM 5 measured
LMArena WebDev: React14241475Kimi K2.7 Code
LMArena WebDev: HTML14501451Kimi K2.7 Code
LMArena WebDev: from an imageNot measured1513Only Kimi K2.7 Code measured
Neither model has a result on the other 17 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index11
Intelligence benchmarks for GLM 5 and Kimi K2.7 Code
BenchmarkGLM 5Kimi K2.7 CodeLeader
AA Intelligence Index27.925.8GLM 5
Epoch Capabilities Index145.8150.1Kimi K2.7 Code
Agentic workLike-for-likePublic boards, shown as published, kept out of the index25
Agentic work benchmarks for GLM 5 and Kimi K2.7 Code
BenchmarkGLM 5Kimi K2.7 CodeLeader
AA Agentic IndexNot measured21.0Only Kimi K2.7 Code measured
Design Arena: agents building web appsNot measured1176Only Kimi K2.7 Code measured
Design Arena: agents building full-stack apps11231175Kimi K2.7 Code
Design Arena: agents building mobile apps11361143Kimi K2.7 Code
Design Arena: agents building Android apps11461137GLM 5
Design Arena: agents building Godot games11441147Kimi K2.7 Code
Design Arena: agents building gamesNot measured1131Only Kimi K2.7 Code measured
Design Arena: HTML slides11481201Kimi K2.7 Code
Design Arena: PowerPoint slidesNot measured1154Only Kimi K2.7 Code measured
τ²-Bench Telecom98.2%90.1%GLM 5
APEX-AgentsNot measured37.6%Only Kimi K2.7 Code measured
Vending-Bench 2$4,432$5,083Kimi K2.7 Code
Neither model has a result on the other 29 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index04
Reasoning benchmarks for GLM 5 and Kimi K2.7 Code
BenchmarkGLM 5Kimi K2.7 CodeLeader
GPQA Diamond83.3%Not measuredOnly GLM 5 measured
GPQA Diamond (Artificial Analysis run)82.0%89.6%Kimi K2.7 Code
Humanity's Last Exam (Artificial Analysis run)29.3%35.0%Kimi K2.7 Code
GPQA Diamond (Epoch AI run)87.8%87.9%Kimi K2.7 Code
ARC-AGI44.7%Not measuredOnly GLM 5 measured
ARC-AGI-24.9%Not measuredOnly GLM 5 measured
SimpleBench53.2%57.9%Kimi K2.7 Code
Chess puzzlesNot measured14.0%Only Kimi K2.7 Code measured
LMArena Text: hard prompts1452Not measuredOnly GLM 5 measured
LMArena Text: expert1454Not measuredOnly GLM 5 measured
LMArena Text: hard prompts in English1460Not measuredOnly GLM 5 measured
Kagi LLM Benchmark75.0%Not measuredOnly GLM 5 measured
Neither model has a result on the other 10 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index01
Math benchmarks for GLM 5 and Kimi K2.7 Code
BenchmarkGLM 5Kimi K2.7 CodeLeader
AIME91.7%Not measuredOnly GLM 5 measured
OTIS Mock AIME80.0%95.6%Kimi K2.7 Code
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: math1440Not measuredOnly GLM 5 measured
LMArena Text: mathematical work1451Not measuredOnly GLM 5 measured
Neither model has a result on the other 7 boards here
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
Knowledge benchmarks for GLM 5 and Kimi K2.7 Code
BenchmarkGLM 5Kimi K2.7 CodeLeader
MMLU Pro86.0%Not measuredOnly GLM 5 measured
LegalBench (Vals AI)84.1%Not measuredOnly GLM 5 measured
CaseLaw (Vals AI)52.5%Not measuredOnly GLM 5 measured
CorpFin (Vals AI)62.9%Not measuredOnly GLM 5 measured
TaxEval (Vals AI)70.0%Not measuredOnly GLM 5 measured
MedQA (Vals AI)94.3%Not measuredOnly GLM 5 measured
SimpleQA VerifiedNot measured36.5%Only Kimi K2.7 Code measured
LMArena Text: science1460Not measuredOnly GLM 5 measured
LMArena Text: medicine and healthcare1455Not measuredOnly GLM 5 measured
LMArena Text: legal and government1453Not measuredOnly GLM 5 measured
LMArena Text: business and finance1430Not measuredOnly GLM 5 measured
Neither model has a result on the other 7 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index26
Web design benchmarks for GLM 5 and Kimi K2.7 Code
BenchmarkGLM 5Kimi K2.7 CodeLeader
Design Arena: websites12471264Kimi K2.7 Code
Design Arena: UI components12301259Kimi K2.7 Code
Design Arena: game dev12341220GLM 5
Design Arena: data viz12281227GLM 5
Design Arena: 3D12291241Kimi K2.7 Code
Design Arena: all code categories12461253Kimi K2.7 Code
Design Arena: SVG11681176Kimi K2.7 Code
Design Arena: ASCII art11471196Kimi 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 index11
General benchmarks for GLM 5 and Kimi K2.7 Code
BenchmarkGLM 5Kimi K2.7 CodeLeader
IFBench72.3%63.1%GLM 5
Long context reasoning (AA-LCR)75.7%79.3%Kimi K2.7 Code
CL-bench18.7%Not measuredOnly GLM 5 measured
Surface Evolver BenchNot measured48.8%Only Kimi K2.7 Code measured
LMArena Text1446Not measuredOnly GLM 5 measured
LMArena Text: instruction following1428Not measuredOnly GLM 5 measured
LMArena Text: creative writing1438Not measuredOnly GLM 5 measured
LMArena Text: English1458Not measuredOnly GLM 5 measured
LMArena Text: non-English1430Not measuredOnly GLM 5 measured
LMArena Text: Chinese1511Not measuredOnly GLM 5 measured
LMArena Text: German1447Not measuredOnly GLM 5 measured
LMArena Text: Japanese1416Not measuredOnly GLM 5 measured
LMArena Text: Korean1423Not measuredOnly GLM 5 measured
LMArena Text: French1456Not measuredOnly GLM 5 measured
LMArena Text: Spanish1454Not measuredOnly GLM 5 measured
LMArena Text: Russian1436Not measuredOnly GLM 5 measured
LMArena Text: Polish1434Not measuredOnly GLM 5 measured
LMArena Text: multi-turn1456Not measuredOnly GLM 5 measured
LMArena Text: longer queries1446Not measuredOnly GLM 5 measured
LMArena Text: writing and language1437Not measuredOnly GLM 5 measured
LMArena Text: entertainment and media1426Not measuredOnly GLM 5 measured
Neither model has a result on the other 25 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 5 and Kimi K2.7 Code
BenchmarkGLM 5Kimi 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$0.0026Fits in one request
Kimi K2.7 Code$0.0029Fits in one request

GLM 5 costs less

Repository review

50K input + 3K output tokens

GLM 5$0.060Fits 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$0.232Exceeds the context window
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 and Kimi K2.7 Code
SpecificationZ.ai GLM 5MoonshotAI Kimi K2.7 Code
MakerZ.aiMoonshot AI
WeightsOpen weightsOpen weights
ReleasedFebruary 11, 2026June 12, 2026
Context window205K262K
Input $/Mtok$1.00$0.95
Output $/Mtok$3.20$4.00
Coding index41.753.4
Independent measurements behind it34
Cost per solved task$0.63$0.35
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 5 better than Kimi K2.7 Code for coding?

Pick Kimi K2.7 Code for coding: it scores 53.4 on the Vibe Coding Index against 41.7 for GLM 5. Pick GLM 5 for output tokens 1.3x cheaper ($3.20 vs $4.00 per million) and work measured by AA Intelligence Index or Design Arena: agents building Android apps, where it leads.

Which is cheaper, GLM 5 or Kimi K2.7 Code?

GLM 5 costs $1.00 per million input tokens and $3.20 per million output tokens; Kimi K2.7 Code costs $0.95 and $4.00. Per solved SWE-bench Verified task, GLM 5 costs $0.63 and Kimi K2.7 Code $0.35.

Which has the bigger context window, GLM 5 or Kimi K2.7 Code?

Kimi K2.7 Code, with 262K tokens against 205K.

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

33 benchmarks have a result for both models as of October 21, 2026. GLM 5's coding index rests on 3 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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