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GLM 4.7 vs Kimi K2.7 Code

GLM 4.7 leads Kimi K2.7 Code on coding, 55.1 to 53.4 on the Vibe Coding Index, and GLM 4.7 costs 1.8x less per output token ($2.20 vs $4.00 per million). Updated October 22, 2026; 33 benchmarks have a result for both.

Replace GLM 4.7 Replace Kimi K2.7 Code
GLM 4.7Z.ai
Coding
55.1
Overall
56.9
Output $/M
$2.20
4
Kimi K2.7 CodeMoonshot AI
Coding
53.4
Overall
56.7
Output $/M
$4.00
29
vs boards won of 33 shared

GLM 4.7 or Kimi K2.7 Code: which to pick

GLM 4.7 and Kimi K2.7 Code are level for coding, 55.1 against 53.4 on the Vibe Coding Index, so price and context decide.

Pick Kimi K2.7 Code for a larger context window (262K against 205K tokens) and work measured by LMArena WebDev or AA Coding Index, where it leads. Pick GLM 4.7 for output tokens 1.8x cheaper ($2.20 vs $4.00 per million) and a lower cost per solved task ($0.29 against $0.35).

Model AGLM 4.7Z.ai55.1/1002 independent benchmarks$0.29 per solved taskModel BKimi K2.7 CodeMoonshot AI53.4/1004 independent benchmarks$0.35 per solved task
Shared results
33
GLM 4.7 only
48
Kimi K2.7 Code only
18
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 4.7 55.1 from 2 benchmarks, Kimi K2.7 Code 53.4 from 4 benchmarks.
Widest category
Math, 12.2 points apart on average over 1 shared benchmark.
Widest single gap
Vending-Bench 2: GLM 4.7 $2,377 against Kimi K2.7 Code $5,083, 16.9 points apart on a 0 to 100 scale.
Output price
GLM 4.7 $2.20 against Kimi K2.7 Code $4.00 per million output tokens, 1.8x apart.
Context window
GLM 4.7 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 4.7BarsKimi K2.7 CodeTies
Coding17
Intelligence02
Agentic work15
Reasoning14
Math01
Knowledge01
Web design08
General11
All shared boards429
CodingLike-for-likeSix of these boards feed the Vibe Coding Index17
Coding benchmarks for GLM 4.7 and Kimi K2.7 Code
BenchmarkGLM 4.7Kimi K2.7 CodeLeader
LMArena WebDev14351474Kimi K2.7 Code
AA Coding Index45.360.8Kimi K2.7 Code
LiveCodeBench (Vals AI)82.2%82.0%GLM 4.7
SWE-bench Verified (Vals AI)69.4%78.2%Kimi K2.7 Code
Vibe Code Bench (Vals AI)Not measured47.2%Only Kimi K2.7 Code measured
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
LiveCodeBench (Artificial Analysis run)89.4%Not measuredOnly GLM 4.7 measured
SciCodeNot measured47.8%Only Kimi K2.7 Code measured
Terminal-Bench Hard (Artificial Analysis run)31.8%44.7%Kimi K2.7 Code
Terminal-Bench33.4%Not measuredOnly GLM 4.7 measured
FrontierCodeNot measured30.1%Only Kimi K2.7 Code measured
WeirdMLNot measured54.1%Only Kimi K2.7 Code measured
ALE-Bench399.5886.2Kimi K2.7 Code
LMArena Text: coding1454Not measuredOnly GLM 4.7 measured
LMArena Text: software and IT1453Not measuredOnly GLM 4.7 measured
LMArena WebDev: React14201475Kimi K2.7 Code
LMArena WebDev: HTML14471451Kimi K2.7 Code
LMArena WebDev: from an imageNot measured1513Only Kimi K2.7 Code measured
Neither model has a result on the other 18 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for GLM 4.7 and Kimi K2.7 Code
BenchmarkGLM 4.7Kimi K2.7 CodeLeader
AA Intelligence Index22.225.8Kimi K2.7 Code
Epoch Capabilities Index143.6150.1Kimi K2.7 Code
Agentic workLike-for-likePublic boards, shown as published, kept out of the index15
Agentic work benchmarks for GLM 4.7 and Kimi K2.7 Code
BenchmarkGLM 4.7Kimi 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 apps10501175Kimi K2.7 Code
Design Arena: agents building mobile apps11051143Kimi K2.7 Code
Design Arena: agents building Android apps11121138Kimi K2.7 Code
Design Arena: agents building Godot games10581147Kimi K2.7 Code
Design Arena: agents building gamesNot measured1130Only Kimi K2.7 Code measured
Design Arena: HTML slidesNot measured1151Only Kimi K2.7 Code measured
Design Arena: PowerPoint slidesNot measured1129Only Kimi K2.7 Code measured
τ²-Bench Telecom95.9%90.1%GLM 4.7
APEX-AgentsNot measured37.6%Only Kimi K2.7 Code measured
Vending-Bench 2$2,377$5,083Kimi K2.7 Code
BrowseComp-Plus64.7%Not measuredOnly GLM 4.7 measured
Neither model has a result on the other 28 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index14
Reasoning benchmarks for GLM 4.7 and Kimi K2.7 Code
BenchmarkGLM 4.7Kimi K2.7 CodeLeader
GPQA Diamond80.0%Not measuredOnly GLM 4.7 measured
GPQA Diamond (Artificial Analysis run)85.9%89.6%Kimi K2.7 Code
Humanity's Last Exam (Artificial Analysis run)27.4%35.0%Kimi K2.7 Code
GPQA Diamond (Epoch AI run)83.3%87.9%Kimi K2.7 Code
SimpleBench47.7%57.9%Kimi K2.7 Code
Chess puzzles21.0%14.0%GLM 4.7
LMArena Text: hard prompts1443Not measuredOnly GLM 4.7 measured
LMArena Text: expert1424Not measuredOnly GLM 4.7 measured
LMArena Text: hard prompts in English1452Not measuredOnly GLM 4.7 measured
Neither model has a result on the other 13 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index01
Math benchmarks for GLM 4.7 and Kimi K2.7 Code
BenchmarkGLM 4.7Kimi K2.7 CodeLeader
AIME93.3%Not measuredOnly GLM 4.7 measured
MGSM (Vals AI)88.2%Not measuredOnly GLM 4.7 measured
AA Math Index95.0Not measuredOnly GLM 4.7 measured
AIME 2025 (Artificial Analysis run)95.0%Not measuredOnly GLM 4.7 measured
OTIS Mock AIME83.3%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
ProofBench6.0%Not measuredOnly GLM 4.7 measured
LMArena Text: math1424Not measuredOnly GLM 4.7 measured
LMArena Text: mathematical work1411Not measuredOnly GLM 4.7 measured
Neither model has a result on the other 3 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index01
Knowledge benchmarks for GLM 4.7 and Kimi K2.7 Code
BenchmarkGLM 4.7Kimi K2.7 CodeLeader
MMLU Pro82.7%Not measuredOnly GLM 4.7 measured
LegalBench (Vals AI)83.4%Not measuredOnly GLM 4.7 measured
CaseLaw (Vals AI)54.9%Not measuredOnly GLM 4.7 measured
CorpFin (Vals AI)46.4%Not measuredOnly GLM 4.7 measured
TaxEval (Vals AI)68.8%Not measuredOnly GLM 4.7 measured
MedQA (Vals AI)93.7%Not measuredOnly GLM 4.7 measured
MedCode (Vals AI)32.8%Not measuredOnly GLM 4.7 measured
MMLU Pro (Artificial Analysis run)85.6%Not measuredOnly GLM 4.7 measured
SimpleQA Verified32.2%36.5%Kimi K2.7 Code
LMArena Text: science1457Not measuredOnly GLM 4.7 measured
LMArena Text: medicine and healthcare1469Not measuredOnly GLM 4.7 measured
LMArena Text: legal and government1444Not measuredOnly GLM 4.7 measured
LMArena Text: business and finance1429Not measuredOnly GLM 4.7 measured
Neither model has a result on the other 5 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index08
Web design benchmarks for GLM 4.7 and Kimi K2.7 Code
BenchmarkGLM 4.7Kimi K2.7 CodeLeader
Design Arena: websites12251263Kimi K2.7 Code
Design Arena: UI components11961255Kimi K2.7 Code
Design Arena: game dev11881220Kimi K2.7 Code
Design Arena: data viz11951227Kimi K2.7 Code
Design Arena: 3D11901240Kimi K2.7 Code
Design Arena: all code categories12161252Kimi K2.7 Code
Design Arena: SVG11421176Kimi K2.7 Code
Design Arena: ASCII art11651196Kimi 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 4.7 and Kimi K2.7 Code
BenchmarkGLM 4.7Kimi K2.7 CodeLeader
MedScribe (Vals AI)68.6%Not measuredOnly GLM 4.7 measured
IFBench67.9%63.1%GLM 4.7
Long context reasoning (AA-LCR)71.0%79.3%Kimi K2.7 Code
CL-bench15.9%Not measuredOnly GLM 4.7 measured
Surface Evolver BenchNot measured48.8%Only Kimi K2.7 Code measured
CL-bench Life10.9%Not measuredOnly GLM 4.7 measured
LMArena Text1435Not measuredOnly GLM 4.7 measured
LMArena Text: instruction following1412Not measuredOnly GLM 4.7 measured
LMArena Text: creative writing1401Not measuredOnly GLM 4.7 measured
LMArena Text: English1453Not measuredOnly GLM 4.7 measured
LMArena Text: non-English1417Not measuredOnly GLM 4.7 measured
LMArena Text: Chinese1495Not measuredOnly GLM 4.7 measured
LMArena Text: German1426Not measuredOnly GLM 4.7 measured
LMArena Text: Japanese1439Not measuredOnly GLM 4.7 measured
LMArena Text: Korean1399Not measuredOnly GLM 4.7 measured
LMArena Text: French1432Not measuredOnly GLM 4.7 measured
LMArena Text: Spanish1434Not measuredOnly GLM 4.7 measured
LMArena Text: Russian1423Not measuredOnly GLM 4.7 measured
LMArena Text: Polish1435Not measuredOnly GLM 4.7 measured
LMArena Text: multi-turn1446Not measuredOnly GLM 4.7 measured
LMArena Text: longer queries1432Not measuredOnly GLM 4.7 measured
LMArena Text: writing and language1411Not measuredOnly GLM 4.7 measured
LMArena Text: entertainment and media1400Not measuredOnly GLM 4.7 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 4.7 and Kimi K2.7 Code
BenchmarkGLM 4.7Kimi 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 4.7$0.0017Fits in one request
Kimi K2.7 Code$0.0029Fits in one request

GLM 4.7 costs less

Repository review

50K input + 3K output tokens

GLM 4.7$0.037Fits in one request
Kimi K2.7 Code$0.059Fits in one request

GLM 4.7 costs less

Agent loop

200K input + 10K output tokens

GLM 4.7$0.142Exceeds the context window
Kimi K2.7 Code$0.230Fits in one request

GLM 4.7 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 4.7 and Kimi K2.7 Code
SpecificationZ.ai GLM 4.7MoonshotAI Kimi K2.7 Code
MakerZ.aiMoonshot AI
WeightsOpen weightsOpen weights
ReleasedDecember 22, 2025June 12, 2026
Context window205K262K
Input $/Mtok$0.60$0.95
Output $/Mtok$2.20$4.00
Coding index55.153.4
Independent measurements behind it24
Cost per solved task$0.29$0.35
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.7 better than Kimi K2.7 Code for coding?

GLM 4.7 and Kimi K2.7 Code are level for coding, 55.1 against 53.4 on the Vibe Coding Index, so price and context decide. Pick Kimi K2.7 Code for a larger context window (262K against 205K tokens) and work measured by LMArena WebDev or AA Coding Index, where it leads. Pick GLM 4.7 for output tokens 1.8x cheaper ($2.20 vs $4.00 per million) and a lower cost per solved task ($0.29 against $0.35).

Which is cheaper, GLM 4.7 or Kimi K2.7 Code?

GLM 4.7 costs $0.60 per million input tokens and $2.20 per million output tokens; Kimi K2.7 Code costs $0.95 and $4.00. Per solved SWE-bench Verified task, GLM 4.7 costs $0.29 and Kimi K2.7 Code $0.35.

Which has the bigger context window, GLM 4.7 or Kimi K2.7 Code?

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

How many benchmarks is the GLM 4.7 vs Kimi K2.7 Code comparison based on?

33 benchmarks have a result for both models as of October 22, 2026. GLM 4.7's coding index rests on 2 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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