GLM 5.3 vs Kimi K2.5

GLM 5.3 leads Kimi K2.5 on coding, 70.6 to 38.6 on the Vibe Coding Index, and Kimi K2.5 costs 1.5x less per output token ($2.85 vs $4.40 per million). Updated October 21, 2026; 63 benchmarks have a result for both.

Replace GLM 5.3 Replace Kimi K2.5

GLM 5.3Z.ai

Coding: 70.6
Overall: 67.9
Output $/M: $4.40

Kimi K2.5Moonshot AI

Coding: 38.6
Overall: 44.8
Output $/M: $2.85

vs boards won of 63 shared

GLM 5.3 or Kimi K2.5: which to pick

Pick GLM 5.3 for coding: it scores 70.6 on the Vibe Coding Index against 38.6 for Kimi K2.5.

Pick Kimi K2.5 for output tokens 1.5x cheaper ($2.85 vs $4.40 per million) and a lower cost per solved task ($0.24 against $0.36).

Model AGLM 5.3Z.ai70.6/1006 independent benchmarks$0.36 per solved task Model BKimi K2.5Moonshot AI38.6/1003 independent benchmarks$0.24 per solved task

633736

Shared results: 63
GLM 5.3 only: 37
Kimi K2.5 only: 36
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.

Coding
Vibe Coding Index, six public coding benchmarks
GLM 5.3
GLM 5.3 scores 70.6 against 38.6.
6 and 3 benchmarks behind the scores
Intelligence
AA Intelligence Index
GLM 5.3
GLM 5.3 leads, 44.8 against 23.5.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GLM 5.3 has a AA Agentic Index result, 53.1.
Directional only
Reasoning
GPQA Diamond
GLM 5.3
GLM 5.3 leads, 88.1% against 84.1%.
Same board, both measured
Math
AIME
No call
Only Kimi K2.5 has a AIME result, 95.6%.
Directional only
Knowledge
MMLU Pro
GLM 5.3
GLM 5.3 leads, 86.8% against 85.9%.
Same board, both measured
Web design
Design Arena: websites
GLM 5.3
GLM 5.3 leads, 1295 against 1248.
Same board, both measured
General
Vals Index
No call
Only GLM 5.3 has a Vals Index result, 59.7%.
Directional only
Long documents
Listed context window
GLM 5.3
GLM 5.3 takes 1.05M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Kimi K2.5
GLM 5.3 $0.0036, Kimi K2.5 $0.0020 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
Kimi K2.5
GLM 5.3 $0.36, Kimi K2.5 $0.24.
Published attempt cost and pass rate

At a glance

Coding index: GLM 5.3 70.6 from 6 benchmarks, Kimi K2.5 38.6 from 3 benchmarks.
Widest category: Agentic work, 26.1 points apart on average over 5 shared benchmarks.
Widest single gap: Vibe Code Bench (Vals AI): GLM 5.3 78.1% against Kimi K2.5 17.5%, 60.6 points apart on a 0 to 100 scale.
Output price: GLM 5.3 $4.40 against Kimi K2.5 $2.85 per million output tokens, 1.5x apart.
Context window: GLM 5.3 takes 1.05M 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
Category	GLM 5.3	Kimi K2.5
Coding	12	1
Intelligence	2	0
Agentic work	5	0
Reasoning	6	0
Math	2	0
Knowledge	7	1
Web design	8	0
General	19	0
All shared boards	61	2

CodingLike-for-likeSix of these boards feed the Vibe Coding Index121

Coding benchmarks for GLM 5.3 and Kimi K2.5
Benchmark	GLM 5.3	Kimi K2.5	Leader
SWE-bench Verified	Not measured	70.8%	Only Kimi K2.5 measured
SWE-bench Multilingual	Not measured	67.3%	Only Kimi K2.5 measured
LiveBench coding	82.8%	Not measured	Only GLM 5.3 measured
LMArena WebDev	1621	1437	GLM 5.3
AA Coding Index	74.8	46.8	GLM 5.3
LiveCodeBench (Vals AI)	80.5%	83.9%	Kimi K2.5
SWE-bench Verified (Vals AI)	95.4%	70.0%	GLM 5.3
Terminal-Bench 4 (Vals AI)	38.9%	Not measured	Only GLM 5.3 measured
Vibe Code Bench (Vals AI)	78.1%	17.5%	GLM 5.3
Code migration (Vals AI)	44.2%	7.0%	GLM 5.3
ProgramBench (Vals AI)	1.5%	Not measured	Only GLM 5.3 measured
IOI (Vals AI)	68.4%	Not measured	Only GLM 5.3 measured
Terminal-Bench 2.1 (Vals AI)	71.5%	42.0%	GLM 5.3
Vibe Code Bench 1-100 (Vals AI)	20.0%	Not measured	Only GLM 5.3 measured
SkillsBench (Vals AI)	47.5%	Not measured	Only GLM 5.3 measured
CyberBench (Vals AI)	72.1%	Not measured	Only GLM 5.3 measured
SciCode	59.0%	Not measured	Only GLM 5.3 measured
Terminal-Bench Hard (Artificial Analysis run)	Not measured	34.8%	Only Kimi K2.5 measured
Terminal-Bench	Not measured	43.2%	Only Kimi K2.5 measured
FrontierCode	40.1%	Not measured	Only GLM 5.3 measured
FrontierSWE	30.2%	Not measured	Only GLM 5.3 measured
WeirdML	75.4%	45.6%	GLM 5.3
ALE-Bench	1317.4	821.6	GLM 5.3
LMArena Text: coding	1497	1483	GLM 5.3
LMArena Text: software and IT	1493	1471	GLM 5.3
LMArena WebDev: React	1641	1429	GLM 5.3
LMArena WebDev: HTML	1595	1426	GLM 5.3
LMArena WebDev: from an image	Not measured	1439	Only Kimi K2.5 measured
Neither model has a result on the other 11 boards here

IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20

Intelligence benchmarks for GLM 5.3 and Kimi K2.5
Benchmark	GLM 5.3	Kimi K2.5	Leader
AA Intelligence Index	44.8	23.5	GLM 5.3
Epoch Capabilities Index	156.0	148.1	GLM 5.3

Agentic workLike-for-likePublic boards, shown as published, kept out of the index50

Agentic work benchmarks for GLM 5.3 and Kimi K2.5
Benchmark	GLM 5.3	Kimi K2.5	Leader
AA Agentic Index	53.1	Not measured	Only GLM 5.3 measured
Design Arena: agents building web apps	Not measured	1136	Only Kimi K2.5 measured
Design Arena: agents building full-stack apps	Not measured	1114	Only Kimi K2.5 measured
Design Arena: agents building mobile apps	1194	1147	GLM 5.3
Design Arena: agents building Android apps	Not measured	1118	Only Kimi K2.5 measured
Design Arena: agents building Godot games	Not measured	1219	Only Kimi K2.5 measured
Design Arena: HTML slides	1189	Not measured	Only GLM 5.3 measured
Design Arena: PowerPoint slides	1254	Not measured	Only GLM 5.3 measured
LiveBench research	73.2%	Not measured	Only GLM 5.3 measured
LiveBench browser	0.0%	Not measured	Only GLM 5.3 measured
LiveBench long agentic	35.9%	Not measured	Only GLM 5.3 measured
SREBench (Vals AI)	9.2%	Not measured	Only GLM 5.3 measured
Finance Agent (Vals AI)	55.8%	35.8%	GLM 5.3
Tax Agent Bench (Vals AI)	39.7%	Not measured	Only GLM 5.3 measured
Legal Research Bench (Vals AI)	49.0%	15.9%	GLM 5.3
Harvey's Legal Agent Benchmark (Vals AI)	14.6%	Not measured	Only GLM 5.3 measured
Excel Modeling Benchmark (Vals AI)	56.3%	28.5%	GLM 5.3
Terminal-Bench-Science (Vals AI)	5.7%	Not measured	Only GLM 5.3 measured
τ²-Bench Telecom	Not measured	95.9%	Only Kimi K2.5 measured
APEX-Agents	56.6%	Not measured	Only GLM 5.3 measured
OSWorld	Not measured	63.3%	Only Kimi K2.5 measured
Vending-Bench 2	$8,164	$1,198	GLM 5.3
Neither model has a result on the other 19 boards here

ReasoningLike-for-likePublic boards, shown as published, kept out of the index60

Reasoning benchmarks for GLM 5.3 and Kimi K2.5
Benchmark	GLM 5.3	Kimi K2.5	Leader
GPQA Diamond	88.1%	84.1%	GLM 5.3
LiveBench reasoning	85.8%	Not measured	Only GLM 5.3 measured
MysteryMechanism (Vals AI)	23.0%	Not measured	Only GLM 5.3 measured
MMMU Pro (Vals AI)	Not measured	84.3%	Only Kimi K2.5 measured
GPQA Diamond (Artificial Analysis run)	91.7%	87.9%	GLM 5.3
Humanity's Last Exam (Artificial Analysis run)	42.3%	30.7%	GLM 5.3
GPQA Diamond (Epoch AI run)	90.9%	Not measured	Only GLM 5.3 measured
ARC-AGI	Not measured	65.3%	Only Kimi K2.5 measured
ARC-AGI-2	Not measured	11.8%	Only Kimi K2.5 measured
SimpleBench	Not measured	46.8%	Only Kimi K2.5 measured
Chess puzzles	37.0%	Not measured	Only GLM 5.3 measured
Mystery game puzzles	33.0%	Not measured	Only GLM 5.3 measured
LMArena Text: hard prompts	1490	1453	GLM 5.3
LMArena Text: expert	1514	1466	GLM 5.3
LMArena Text: hard prompts in English	1496	1460	GLM 5.3
LMArena Vision: diagrams	Not measured	1282	Only Kimi K2.5 measured
LMArena Vision: homework	Not measured	1297	Only Kimi K2.5 measured
Kagi LLM Benchmark	Not measured	78.5%	Only Kimi K2.5 measured
Neither model has a result on the other 4 boards here

MathLike-for-likePublic boards, shown as published, kept out of the index20

Math benchmarks for GLM 5.3 and Kimi K2.5
Benchmark	GLM 5.3	Kimi K2.5	Leader
AIME	Not measured	95.6%	Only Kimi K2.5 measured
OTIS Mock AIME	91.1%	Not measured	Only GLM 5.3 measured
FrontierMath, tiers 1 to 3	68.8%	Not measured	Only GLM 5.3 measured
FrontierMath, tier 4	29.3%	Not measured	Only GLM 5.3 measured
ProofBench	49.0%	Not measured	Only GLM 5.3 measured
LMArena Text: math	1495	1470	GLM 5.3
LMArena Text: mathematical work	1497	1473	GLM 5.3
Neither model has a result on the other 6 boards here

KnowledgeLike-for-likePublic boards, shown as published, kept out of the index71

Knowledge benchmarks for GLM 5.3 and Kimi K2.5
Benchmark	GLM 5.3	Kimi K2.5	Leader
MMLU Pro	86.8%	85.9%	GLM 5.3
LegalBench (Vals AI)	84.8%	Not measured	Only GLM 5.3 measured
CaseLaw (Vals AI)	Not measured	58.7%	Only Kimi K2.5 measured
CorpFin (Vals AI)	Not measured	68.3%	Only Kimi K2.5 measured
TaxEval (Vals AI)	72.4%	74.2%	Kimi K2.5
MortgageTax (Vals AI)	Not measured	66.5%	Only Kimi K2.5 measured
MedQA (Vals AI)	Not measured	94.4%	Only Kimi K2.5 measured
MedCode (Vals AI)	42.9%	39.3%	GLM 5.3
SimpleQA Verified	41.0%	34.3%	GLM 5.3
LMArena Text: science	1498	1459	GLM 5.3
LMArena Text: medicine and healthcare	1481	1448	GLM 5.3
LMArena Text: legal and government	1473	1441	GLM 5.3
LMArena Text: business and finance	1459	1427	GLM 5.3
Neither model has a result on the other 5 boards here

Web designLike-for-likePublic boards, shown as published, kept out of the index80

Web design benchmarks for GLM 5.3 and Kimi K2.5
Benchmark	GLM 5.3	Kimi K2.5	Leader
Design Arena: websites	1295	1248	GLM 5.3
Design Arena: UI components	1314	1236	GLM 5.3
Design Arena: game dev	1311	1207	GLM 5.3
Design Arena: data viz	1259	1219	GLM 5.3
Design Arena: 3D	1332	1205	GLM 5.3
Design Arena: all code categories	1304	1236	GLM 5.3
Design Arena: SVG	1293	1149	GLM 5.3
Design Arena: ASCII art	1213	1166	GLM 5.3
LiveBench design	77.3%	Not measured	Only GLM 5.3 measured

GeneralLike-for-likePublic boards, shown as published, kept out of the index190

General benchmarks for GLM 5.3 and Kimi K2.5
Benchmark	GLM 5.3	Kimi K2.5	Leader
Vals Index	59.7%	Not measured	Only GLM 5.3 measured
LiveBench data analysis	70.2%	Not measured	Only GLM 5.3 measured
Public Benefits Bench (Vals AI)	68.5%	Not measured	Only GLM 5.3 measured
SAGE (Vals AI)	Not measured	49.9%	Only Kimi K2.5 measured
MedScribe (Vals AI)	88.8%	76.4%	GLM 5.3
IFBench	Not measured	70.2%	Only Kimi K2.5 measured
Long context reasoning (AA-LCR)	79.7%	78.0%	GLM 5.3
DTBench	87.7%	Not measured	Only GLM 5.3 measured
LMCA	55.5%	Not measured	Only GLM 5.3 measured
Fiction.LiveBench	Not measured	86.1%	Only Kimi K2.5 measured
CL-bench	Not measured	19.3%	Only Kimi K2.5 measured
CL-bench Life	Not measured	13.2%	Only Kimi K2.5 measured
LMArena Text	1474	1445	GLM 5.3
LMArena Text: instruction following	1479	1431	GLM 5.3
LMArena Text: creative writing	1458	1422	GLM 5.3
LMArena Vision	Not measured	1269	Only Kimi K2.5 measured
LMArena Text: English	1482	1453	GLM 5.3
LMArena Text: non-English	1459	1433	GLM 5.3
LMArena Text: Chinese	1532	1494	GLM 5.3
LMArena Text: German	1488	1441	GLM 5.3
LMArena Text: Japanese	1460	1423	GLM 5.3
LMArena Text: Korean	1460	1412	GLM 5.3
LMArena Text: French	1503	1454	GLM 5.3
LMArena Text: Spanish	1462	1450	GLM 5.3
LMArena Text: Russian	1469	1435	GLM 5.3
LMArena Text: Polish	1466	1445	GLM 5.3
LMArena Text: multi-turn	1472	1444	GLM 5.3
LMArena Text: longer queries	1483	1445	GLM 5.3
LMArena Text: writing and language	1469	1425	GLM 5.3
LMArena Text: entertainment and media	1447	1417	GLM 5.3
LMArena Vision: English	Not measured	1274	Only Kimi K2.5 measured
LMArena Vision: Chinese	Not measured	1328	Only Kimi K2.5 measured
LMArena Vision: reading text	Not measured	1276	Only Kimi K2.5 measured
LMArena Vision: humor	Not measured	1284	Only Kimi K2.5 measured
LMArena Vision: captioning	Not measured	1257	Only Kimi K2.5 measured
LMArena Vision: recognizing things	Not measured	1285	Only Kimi K2.5 measured
LMArena Vision: creative writing	Not measured	1269	Only Kimi K2.5 measured
Neither model has a result on the other 9 boards here

Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared

Image generation benchmarks for GLM 5.3 and Kimi K2.5
Benchmark	GLM 5.3	Kimi K2.5	Leader
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 5.3$0.0036Fits in one request

Kimi K2.5$0.0020Fits in one request

Kimi K2.5 costs less

Repository review

50K input + 3K output tokens

GLM 5.3$0.083Fits in one request

Kimi K2.5$0.035Fits in one request

Kimi K2.5 costs less

Agent loop

200K input + 10K output tokens

GLM 5.3$0.324Fits in one request

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 5.3 and Kimi K2.5
Specification	Z.ai GLM 5.3	MoonshotAI Kimi K2.5
Maker	Z.ai	Moonshot AI
Weights	Open weights	Open weights
Released	August 18, 2026	January 27, 2026
Context window	1.05M	262K
Input $/Mtok	$1.40	$0.532
Output $/Mtok	$4.40	$2.85
Coding index	70.6	38.6
Independent measurements behind it	6	3
Cost per solved task	$0.36	$0.24
Source	OpenRouter listing	OpenRouter listing

Questions

Is GLM 5.3 better than Kimi K2.5 for coding?

Pick GLM 5.3 for coding: it scores 70.6 on the Vibe Coding Index against 38.6 for Kimi K2.5. Pick Kimi K2.5 for output tokens 1.5x cheaper ($2.85 vs $4.40 per million) and a lower cost per solved task ($0.24 against $0.36).

Which is cheaper, GLM 5.3 or Kimi K2.5?

GLM 5.3 costs $1.40 per million input tokens and $4.40 per million output tokens; Kimi K2.5 costs $0.532 and $2.85. Per solved SWE-bench Verified task, GLM 5.3 costs $0.36 and Kimi K2.5 $0.24.

Which has the bigger context window, GLM 5.3 or Kimi K2.5?

GLM 5.3, with 1.05M tokens against 262K.

How many benchmarks is the GLM 5.3 vs Kimi K2.5 comparison based on?

63 benchmarks have a result for both models as of October 21, 2026. GLM 5.3's coding index rests on 6 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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GLM 5.3 or Kimi K2.5: which to pick

Which one for your work

Coding

Intelligence

Agentic work

Reasoning

Math

Knowledge

Web design

General

Long documents

Everyday cost

Cost per solved task

At a glance

Benchmark by benchmark

What each workload costs

Chat turn

Repository review

Agent loop

Specification differences

Questions

Compare next