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GLM 4.6 vs Kimi K2.6

Kimi K2.6 leads GLM 4.6 on coding, 52.1 to 8.8 on the Vibe Coding Index, and GLM 4.6 costs 1.8x less per output token ($2.20 vs $4.00 per million). Updated October 22, 2026; 57 benchmarks have a result for both.

GLM 4.6Z.ai
Coding
8.8
Overall
33.4
Output $/M
$2.20
2
Kimi K2.6Moonshot AI
Coding
52.1
Overall
60.5
Output $/M
$4.00
55
vs boards won of 57 shared

GLM 4.6 or Kimi K2.6: which to pick

Pick Kimi K2.6 for coding: it scores 52.1 on the Vibe Coding Index against 8.8 for GLM 4.6.

Pick GLM 4.6 for output tokens 1.8x cheaper ($2.20 vs $4.00 per million) and work measured by Design Arena: agents building Godot games or LMArena Text: Polish, where it leads.

Shared results
57
GLM 4.6 only
12
Kimi K2.6 only
52
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 4.6 8.8 from 2 benchmarks, Kimi K2.6 52.1 from 3 benchmarks.
Widest category
Coding, 17.2 points apart on average over 9 shared benchmarks.
Widest single gap
Vibe Code Bench (Vals AI): GLM 4.6 3.1% against Kimi K2.6 37.9%, 34.8 points apart on a 0 to 100 scale.
Output price
GLM 4.6 $2.20 against Kimi K2.6 $4.00 per million output tokens, 1.8x apart.
Context window
GLM 4.6 takes 205K tokens, Kimi K2.6 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 4.6BarsKimi K2.6Ties
Coding09
Intelligence01
Agentic work14
Reasoning06
Math02
Knowledge08
Web design07
General118
All shared boards255
CodingLike-for-likeSix of these boards feed the Vibe Coding Index09
Coding benchmarks for GLM 4.6 and Kimi K2.6
BenchmarkGLM 4.6Kimi K2.6Leader
SWE-bench Verified68.2%Not measuredOnly GLM 4.6 measured
LMArena WebDev13411509Kimi K2.6
AA Coding Index45.861.8Kimi K2.6
LiveCodeBench (Vals AI)81.0%86.8%Kimi K2.6
SWE-bench Verified (Vals AI)Not measured76.2%Only Kimi K2.6 measured
Vibe Code Bench (Vals AI)3.1%37.9%Kimi K2.6
Code migration (Vals AI)Not measured27.8%Only Kimi K2.6 measured
ProgramBench (Vals AI)Not measured0.0%Only Kimi K2.6 measured
Terminal-Bench 2.1 (Vals AI)Not measured53.6%Only Kimi K2.6 measured
LiveCodeBench (Artificial Analysis run)69.5%Not measuredOnly GLM 4.6 measured
SciCodeNot measured51.5%Only Kimi K2.6 measured
Terminal-Bench Hard (Artificial Analysis run)28.8%43.9%Kimi K2.6
Terminal-Bench24.5%Not measuredOnly GLM 4.6 measured
WeirdMLNot measured55.9%Only Kimi K2.6 measured
ALE-Bench340.81092.7Kimi K2.6
LMArena Text: coding14491489Kimi K2.6
LMArena Text: software and IT14551484Kimi K2.6
LMArena WebDev: ReactNot measured1505Only Kimi K2.6 measured
LMArena WebDev: HTML13501485Kimi K2.6
LMArena WebDev: from an imageNot measured1521Only Kimi K2.6 measured
Neither model has a result on the other 19 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GLM 4.6 and Kimi K2.6
BenchmarkGLM 4.6Kimi K2.6Leader
AA Intelligence Index18.527.0Kimi K2.6
Epoch Capabilities IndexNot measured150.9Only Kimi K2.6 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index14
Agentic work benchmarks for GLM 4.6 and Kimi K2.6
BenchmarkGLM 4.6Kimi K2.6Leader
AA Agentic IndexNot measured20.5Only Kimi K2.6 measured
Design Arena: agents building web appsNot measured1268Only Kimi K2.6 measured
Design Arena: agents building full-stack apps10291155Kimi K2.6
Design Arena: agents building mobile apps10961156Kimi K2.6
Design Arena: agents building Android apps10841161Kimi K2.6
Design Arena: agents building Godot games11801120GLM 4.6
Design Arena: agents building gamesNot measured1141Only Kimi K2.6 measured
Design Arena: HTML slidesNot measured1135Only Kimi K2.6 measured
Design Arena: PowerPoint slidesNot measured1103Only Kimi K2.6 measured
Design Arena: spreadsheetsNot measured1276Only Kimi K2.6 measured
Finance Agent (Vals AI)Not measured44.9%Only Kimi K2.6 measured
Tax Agent Bench (Vals AI)Not measured12.4%Only Kimi K2.6 measured
Legal Research Bench (Vals AI)Not measured15.9%Only Kimi K2.6 measured
Excel Modeling Benchmark (Vals AI)Not measured57.9%Only Kimi K2.6 measured
τ²-Bench Telecom76.9%95.9%Kimi K2.6
OSWorld 2.0Not measured4.6%Only Kimi K2.6 measured
Vending-Bench 2Not measured$6,205Only Kimi K2.6 measured
Berkeley Function Calling Leaderboard (BFCL)72.4%Not measuredOnly GLM 4.6 measured
BrowseComp-Plus48.2%Not measuredOnly GLM 4.6 measured
Neither model has a result on the other 22 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
Reasoning benchmarks for GLM 4.6 and Kimi K2.6
BenchmarkGLM 4.6Kimi K2.6Leader
GPQA Diamond74.5%89.1%Kimi K2.6
MMMU Pro (Vals AI)Not measured86.3%Only Kimi K2.6 measured
GPQA Diamond (Artificial Analysis run)78.0%91.1%Kimi K2.6
Humanity's Last Exam (Artificial Analysis run)14.5%37.5%Kimi K2.6
GPQA Diamond (Epoch AI run)Not measured90.8%Only Kimi K2.6 measured
Chess puzzlesNot measured20.0%Only Kimi K2.6 measured
Mystery game puzzlesNot measured18.0%Only Kimi K2.6 measured
EBR-benchNot measured2.4%Only Kimi K2.6 measured
LMArena Text: hard prompts14401470Kimi K2.6
LMArena Text: expert14321491Kimi K2.6
LMArena Text: hard prompts in English14451471Kimi K2.6
LMArena Vision: diagramsNot measured1298Only Kimi K2.6 measured
LMArena Vision: homeworkNot measured1310Only Kimi K2.6 measured
Kagi LLM Benchmark45.7%Not measuredOnly GLM 4.6 measured
Neither model has a result on the other 8 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for GLM 4.6 and Kimi K2.6
BenchmarkGLM 4.6Kimi K2.6Leader
AIME92.7%Not measuredOnly GLM 4.6 measured
MGSM (Vals AI)89.8%Not measuredOnly GLM 4.6 measured
AA Math Index86.0Not measuredOnly GLM 4.6 measured
AIME 2025 (Artificial Analysis run)86.0%Not measuredOnly GLM 4.6 measured
OTIS Mock AIMENot measured96.1%Only Kimi K2.6 measured
FrontierMath, tiers 1 to 3Not measured57.2%Only Kimi K2.6 measured
FrontierMath, tier 4Not measured25.6%Only Kimi K2.6 measured
ProofBenchNot measured16.0%Only Kimi K2.6 measured
LMArena Text: math14321474Kimi K2.6
LMArena Text: mathematical work14471480Kimi K2.6
Neither model has a result on the other 3 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index08
Knowledge benchmarks for GLM 4.6 and Kimi K2.6
BenchmarkGLM 4.6Kimi K2.6Leader
MMLU Pro82.2%87.6%Kimi K2.6
LegalBench (Vals AI)79.6%84.7%Kimi K2.6
CaseLaw (Vals AI)Not measured61.2%Only Kimi K2.6 measured
CorpFin (Vals AI)56.8%66.7%Kimi K2.6
TaxEval (Vals AI)66.2%74.7%Kimi K2.6
MortgageTax (Vals AI)Not measured65.8%Only Kimi K2.6 measured
MedQA (Vals AI)92.2%Not measuredOnly GLM 4.6 measured
MedCode (Vals AI)Not measured40.1%Only Kimi K2.6 measured
MMLU Pro (Artificial Analysis run)82.9%Not measuredOnly GLM 4.6 measured
SimpleQA VerifiedNot measured34.9%Only Kimi K2.6 measured
LMArena Text: science14561468Kimi K2.6
LMArena Text: medicine and healthcare14551457Kimi K2.6
LMArena Text: legal and government14481462Kimi K2.6
LMArena Text: business and finance14161445Kimi K2.6
Neither model has a result on the other 4 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index07
Web design benchmarks for GLM 4.6 and Kimi K2.6
BenchmarkGLM 4.6Kimi K2.6Leader
Design Arena: websites11741268Kimi K2.6
Design Arena: UI components11451263Kimi K2.6
Design Arena: game dev11501250Kimi K2.6
Design Arena: data viz11611249Kimi K2.6
Design Arena: 3D11281267Kimi K2.6
Design Arena: all code categories11651271Kimi K2.6
Design Arena: SVG11081181Kimi K2.6
Design Arena: ASCII artNot measured1165Only Kimi K2.6 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index118
General benchmarks for GLM 4.6 and Kimi K2.6
BenchmarkGLM 4.6Kimi K2.6Leader
Public Benefits Bench (Vals AI)Not measured56.6%Only Kimi K2.6 measured
SAGE (Vals AI)Not measured50.2%Only Kimi K2.6 measured
MedScribe (Vals AI)Not measured78.2%Only Kimi K2.6 measured
Vals Multimodal IndexNot measured56.4%Only Kimi K2.6 measured
IFBench43.4%76.0%Kimi K2.6
Long context reasoning (AA-LCR)54.0%81.0%Kimi K2.6
DTBenchNot measured90.9%Only Kimi K2.6 measured
LMCANot measured37.3%Only Kimi K2.6 measured
Furniture assemblyNot measured21.7%Only Kimi K2.6 measured
ExploitBenchNot measured18.4%Only Kimi K2.6 measured
LMArena Text14401455Kimi K2.6
LMArena Text: instruction following14101451Kimi K2.6
LMArena Text: creative writing14121434Kimi K2.6
LMArena VisionNot measured1282Only Kimi K2.6 measured
LMArena Text: English14491460Kimi K2.6
LMArena Text: non-English14261446Kimi K2.6
LMArena Text: Chinese14991520Kimi K2.6
LMArena Text: German14471451Kimi K2.6
LMArena Text: Japanese13931443Kimi K2.6
LMArena Text: Korean14001427Kimi K2.6
LMArena Text: French14591471Kimi K2.6
LMArena Text: Spanish14361464Kimi K2.6
LMArena Text: Russian14181446Kimi K2.6
LMArena Text: Polish14471438GLM 4.6
LMArena Text: multi-turn14271453Kimi K2.6
LMArena Text: longer queries14221468Kimi K2.6
LMArena Text: writing and language14151439Kimi K2.6
LMArena Text: entertainment and media14091428Kimi K2.6
LMArena Vision: EnglishNot measured1287Only Kimi K2.6 measured
LMArena Vision: ChineseNot measured1346Only Kimi K2.6 measured
LMArena Vision: reading textNot measured1291Only Kimi K2.6 measured
LMArena Vision: humorNot measured1280Only Kimi K2.6 measured
LMArena Vision: creative writingNot measured1273Only Kimi K2.6 measured
LMArena DocumentNot measured1451Only Kimi K2.6 measured
Neither model has a result on the other 12 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.6 and Kimi K2.6
BenchmarkGLM 4.6Kimi K2.6Leader
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 4.6$0.0017Fits in one request
Kimi K2.6$0.0029Fits in one request

GLM 4.6 costs less

Repository review

50K input + 3K output tokens

GLM 4.6$0.037Fits in one request
Kimi K2.6$0.059Fits in one request

GLM 4.6 costs less

Agent loop

200K input + 10K output tokens

GLM 4.6$0.142Exceeds the context window
Kimi K2.6$0.230Fits in one request

GLM 4.6 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 4.6 and Kimi K2.6
SpecificationZ.ai GLM 4.6MoonshotAI Kimi K2.6
MakerZ.aiMoonshot AI
WeightsOpen weightsOpen weights
ReleasedSeptember 30, 2025April 20, 2026
Context window205K262K
Input $/Mtok$0.60$0.95
Output $/Mtok$2.20$4.00
Coding index8.852.1
Independent measurements behind it23
Cost per solved taskNot computable$0.35
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.6 better than Kimi K2.6 for coding?

Pick Kimi K2.6 for coding: it scores 52.1 on the Vibe Coding Index against 8.8 for GLM 4.6. Pick GLM 4.6 for output tokens 1.8x cheaper ($2.20 vs $4.00 per million) and work measured by Design Arena: agents building Godot games or LMArena Text: Polish, where it leads.

Which is cheaper, GLM 4.6 or Kimi K2.6?

GLM 4.6 costs $0.60 per million input tokens and $2.20 per million output tokens; Kimi K2.6 costs $0.95 and $4.00. Cost per solved task is known for Kimi K2.6 only.

Which has the bigger context window, GLM 4.6 or Kimi K2.6?

Kimi K2.6, with 262K tokens against 205K.

How many benchmarks is the GLM 4.6 vs Kimi K2.6 comparison based on?

57 benchmarks have a result for both models as of October 22, 2026. GLM 4.6's coding index rests on 2 independent benchmarks and Kimi K2.6's on 3. Where only one model has a result, the page says so instead of naming a winner.

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<p style="font:13px/1.4 sans-serif;margin:6px 0 0">Source: <a href="https://llmbenchmark.ai/compare/glm-4-6-vs-kimi-k2-6">LLMBenchmark.ai, GLM 4.6 vs Kimi K2.6</a></p>