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GLM 4.6 vs Kimi K2.5

Kimi K2.5 leads GLM 4.6 on coding, 38.6 to 8.8 on the Vibe Coding Index, and GLM 4.6 costs 1.3x less per output token ($2.20 vs $2.85 per million). Updated October 22, 2026; 61 benchmarks have a result for both.

Replace GLM 4.6 Replace Kimi K2.5
GLM 4.6Z.ai
Coding
8.8
Overall
33.4
Output $/M
$2.20
6
Kimi K2.5Moonshot AI
Coding
38.6
Overall
44.8
Output $/M
$2.85
55
vs boards won of 61 shared

GLM 4.6 or Kimi K2.5: which to pick

Pick Kimi K2.5 for coding: it scores 38.6 on the Vibe Coding Index against 8.8 for GLM 4.6.

Pick GLM 4.6 for output tokens 1.3x cheaper ($2.20 vs $2.85 per million) and work measured by LMArena Text: medicine and healthcare or LMArena Text: legal and government, where it leads.

Model AGLM 4.6Z.ai8.8/1002 independent benchmarksNo cost per solved taskModel BKimi K2.5Moonshot AI38.6/1003 independent benchmarks$0.24 per solved task
Shared results
61
GLM 4.6 only
8
Kimi K2.5 only
38
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 K2.5 38.6 from 3 benchmarks.
Widest category
Reasoning, 11.3 points apart on average over 7 shared benchmarks.
Widest single gap
Kagi LLM Benchmark: GLM 4.6 45.7% against Kimi K2.5 78.5%, 32.8 points apart on a 0 to 100 scale.
Output price
GLM 4.6 $2.20 against Kimi K2.5 $2.85 per million output tokens, 1.3x apart.
Context window
GLM 4.6 takes 205K tokens, Kimi K2.5 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 K2.5Ties
Coding011
Intelligence01
Agentic work05
Reasoning07
Math03
Knowledge26
Web design07
General415
All shared boards655
CodingLike-for-likeSix of these boards feed the Vibe Coding Index011
Coding benchmarks for GLM 4.6 and Kimi K2.5
BenchmarkGLM 4.6Kimi K2.5Leader
SWE-bench Verified68.2%70.8%Kimi K2.5
SWE-bench MultilingualNot measured67.3%Only Kimi K2.5 measured
LMArena WebDev13411437Kimi K2.5
AA Coding Index45.846.8Kimi K2.5
LiveCodeBench (Vals AI)81.0%83.9%Kimi K2.5
SWE-bench Verified (Vals AI)Not measured70.0%Only Kimi K2.5 measured
Vibe Code Bench (Vals AI)3.1%17.5%Kimi K2.5
Code migration (Vals AI)Not measured7.0%Only Kimi K2.5 measured
Terminal-Bench 2.1 (Vals AI)Not measured42.0%Only Kimi K2.5 measured
LiveCodeBench (Artificial Analysis run)69.5%Not measuredOnly GLM 4.6 measured
Terminal-Bench Hard (Artificial Analysis run)28.8%34.8%Kimi K2.5
Terminal-Bench24.5%43.2%Kimi K2.5
WeirdMLNot measured45.6%Only Kimi K2.5 measured
ALE-Bench340.8821.6Kimi K2.5
LMArena Text: coding14491483Kimi K2.5
LMArena Text: software and IT14551471Kimi K2.5
LMArena WebDev: ReactNot measured1429Only Kimi K2.5 measured
LMArena WebDev: HTML13501426Kimi K2.5
LMArena WebDev: from an imageNot measured1439Only Kimi K2.5 measured
Neither model has a result on the other 20 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GLM 4.6 and Kimi K2.5
BenchmarkGLM 4.6Kimi K2.5Leader
AA Intelligence Index18.523.5Kimi K2.5
Epoch Capabilities IndexNot measured148.1Only Kimi K2.5 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index05
Agentic work benchmarks for GLM 4.6 and Kimi K2.5
BenchmarkGLM 4.6Kimi K2.5Leader
Design Arena: agents building web appsNot measured1136Only Kimi K2.5 measured
Design Arena: agents building full-stack apps10291114Kimi K2.5
Design Arena: agents building mobile apps10961147Kimi K2.5
Design Arena: agents building Android apps10841118Kimi K2.5
Design Arena: agents building Godot games11801219Kimi K2.5
Finance Agent (Vals AI)Not measured35.8%Only Kimi K2.5 measured
Legal Research Bench (Vals AI)Not measured15.9%Only Kimi K2.5 measured
Excel Modeling Benchmark (Vals AI)Not measured28.5%Only Kimi K2.5 measured
τ²-Bench Telecom76.9%95.9%Kimi K2.5
OSWorldNot measured63.3%Only Kimi K2.5 measured
Vending-Bench 2Not measured$1,198Only Kimi K2.5 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 28 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index07
Reasoning benchmarks for GLM 4.6 and Kimi K2.5
BenchmarkGLM 4.6Kimi K2.5Leader
GPQA Diamond74.5%84.1%Kimi K2.5
MMMU Pro (Vals AI)Not measured84.3%Only Kimi K2.5 measured
GPQA Diamond (Artificial Analysis run)78.0%87.9%Kimi K2.5
Humanity's Last Exam (Artificial Analysis run)14.5%30.7%Kimi K2.5
ARC-AGINot measured65.3%Only Kimi K2.5 measured
ARC-AGI-2Not measured11.8%Only Kimi K2.5 measured
SimpleBenchNot measured46.8%Only Kimi K2.5 measured
LMArena Text: hard prompts14401453Kimi K2.5
LMArena Text: expert14321466Kimi K2.5
LMArena Text: hard prompts in English14451460Kimi K2.5
LMArena Vision: diagramsNot measured1282Only Kimi K2.5 measured
LMArena Vision: homeworkNot measured1297Only Kimi K2.5 measured
Kagi LLM Benchmark45.7%78.5%Kimi K2.5
Neither model has a result on the other 9 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index03
Math benchmarks for GLM 4.6 and Kimi K2.5
BenchmarkGLM 4.6Kimi K2.5Leader
AIME92.7%95.6%Kimi K2.5
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
LMArena Text: math14321470Kimi K2.5
LMArena Text: mathematical work14471473Kimi K2.5
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index26
Knowledge benchmarks for GLM 4.6 and Kimi K2.5
BenchmarkGLM 4.6Kimi K2.5Leader
MMLU Pro82.2%85.9%Kimi K2.5
LegalBench (Vals AI)79.6%Not measuredOnly GLM 4.6 measured
CaseLaw (Vals AI)Not measured58.7%Only Kimi K2.5 measured
CorpFin (Vals AI)56.8%68.3%Kimi K2.5
TaxEval (Vals AI)66.2%74.2%Kimi K2.5
MortgageTax (Vals AI)Not measured66.5%Only Kimi K2.5 measured
MedQA (Vals AI)92.2%94.4%Kimi K2.5
MedCode (Vals AI)Not measured39.3%Only Kimi K2.5 measured
MMLU Pro (Artificial Analysis run)82.9%Not measuredOnly GLM 4.6 measured
SimpleQA VerifiedNot measured34.3%Only Kimi K2.5 measured
LMArena Text: science14561459Kimi K2.5
LMArena Text: medicine and healthcare14551448GLM 4.6
LMArena Text: legal and government14481441GLM 4.6
LMArena Text: business and finance14161427Kimi K2.5
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 K2.5
BenchmarkGLM 4.6Kimi K2.5Leader
Design Arena: websites11741248Kimi K2.5
Design Arena: UI components11451234Kimi K2.5
Design Arena: game dev11501206Kimi K2.5
Design Arena: data viz11611219Kimi K2.5
Design Arena: 3D11281204Kimi K2.5
Design Arena: all code categories11651236Kimi K2.5
Design Arena: SVG11081149Kimi K2.5
Design Arena: ASCII artNot measured1166Only Kimi K2.5 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index415
General benchmarks for GLM 4.6 and Kimi K2.5
BenchmarkGLM 4.6Kimi K2.5Leader
SAGE (Vals AI)Not measured49.9%Only Kimi K2.5 measured
MedScribe (Vals AI)Not measured76.4%Only Kimi K2.5 measured
IFBench43.4%70.2%Kimi K2.5
Long context reasoning (AA-LCR)54.0%78.0%Kimi K2.5
Fiction.LiveBenchNot measured86.1%Only Kimi K2.5 measured
CL-benchNot measured19.3%Only Kimi K2.5 measured
CL-bench LifeNot measured13.2%Only Kimi K2.5 measured
LMArena Text14401445Kimi K2.5
LMArena Text: instruction following14101431Kimi K2.5
LMArena Text: creative writing14121422Kimi K2.5
LMArena VisionNot measured1269Only Kimi K2.5 measured
LMArena Text: English14491453Kimi K2.5
LMArena Text: non-English14261433Kimi K2.5
LMArena Text: Chinese14991494GLM 4.6
LMArena Text: German14471441GLM 4.6
LMArena Text: Japanese13931423Kimi K2.5
LMArena Text: Korean14001412Kimi K2.5
LMArena Text: French14591454GLM 4.6
LMArena Text: Spanish14361450Kimi K2.5
LMArena Text: Russian14181435Kimi K2.5
LMArena Text: Polish14471445GLM 4.6
LMArena Text: multi-turn14271444Kimi K2.5
LMArena Text: longer queries14221445Kimi K2.5
LMArena Text: writing and language14151425Kimi K2.5
LMArena Text: entertainment and media14091417Kimi K2.5
LMArena Vision: EnglishNot measured1274Only Kimi K2.5 measured
LMArena Vision: ChineseNot measured1328Only Kimi K2.5 measured
LMArena Vision: reading textNot measured1276Only Kimi K2.5 measured
LMArena Vision: humorNot measured1284Only Kimi K2.5 measured
LMArena Vision: captioningNot measured1257Only Kimi K2.5 measured
LMArena Vision: recognizing thingsNot measured1285Only Kimi K2.5 measured
LMArena Vision: creative writingNot measured1269Only Kimi K2.5 measured
Neither model has a result on the other 14 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 K2.5
BenchmarkGLM 4.6Kimi K2.5Leader
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 K2.5$0.0020Fits in one request

GLM 4.6 costs less

Repository review

50K input + 3K output tokens

GLM 4.6$0.037Fits in one request
Kimi K2.5$0.035Fits in one request

Kimi K2.5 costs less

Agent loop

200K input + 10K output tokens

GLM 4.6$0.142Exceeds the context window
Kimi K2.5$0.135Fits in one request

Kimi K2.5 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 K2.5
SpecificationZ.ai GLM 4.6MoonshotAI Kimi K2.5
MakerZ.aiMoonshot AI
WeightsOpen weightsOpen weights
ReleasedSeptember 30, 2025January 27, 2026
Context window205K262K
Input $/Mtok$0.60$0.532
Output $/Mtok$2.20$2.85
Coding index8.838.6
Independent measurements behind it23
Cost per solved taskNot computable$0.24
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.6 better than Kimi K2.5 for coding?

Pick Kimi K2.5 for coding: it scores 38.6 on the Vibe Coding Index against 8.8 for GLM 4.6. Pick GLM 4.6 for output tokens 1.3x cheaper ($2.20 vs $2.85 per million) and work measured by LMArena Text: medicine and healthcare or LMArena Text: legal and government, where it leads.

Which is cheaper, GLM 4.6 or Kimi K2.5?

GLM 4.6 costs $0.60 per million input tokens and $2.20 per million output tokens; Kimi K2.5 costs $0.532 and $2.85. Cost per solved task is known for Kimi K2.5 only.

Which has the bigger context window, GLM 4.6 or Kimi K2.5?

Kimi K2.5, with 262K tokens against 205K.

How many benchmarks is the GLM 4.6 vs Kimi K2.5 comparison based on?

61 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 K2.5'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-k2-5">LLMBenchmark.ai, GLM 4.6 vs Kimi K2.5</a></p>