GLM 5.3 vs Qwen3.6 Plus

GLM 5.3 leads Qwen3.6 Plus on coding, 70.6 to 44.1 on the Vibe Coding Index, and Qwen3.6 Plus costs 2.3x less per output token ($1.95 vs $4.40 per million). Updated October 22, 2026; 71 benchmarks have a result for both.

Replace GLM 5.3 Replace Qwen3.6 Plus

GLM 5.3Z.ai

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

Qwen3.6 PlusQwen

Coding: 44.1
Overall: 56.2
Output $/M: $1.95

vs boards won of 71 shared

GLM 5.3 or Qwen3.6 Plus: which to pick

Pick GLM 5.3 for coding: it scores 70.6 on the Vibe Coding Index against 44.1 for Qwen3.6 Plus.

Pick Qwen3.6 Plus for output tokens 2.3x cheaper ($1.95 vs $4.40 per million) and work measured by LiveCodeBench (Vals AI) or OTIS Mock AIME, where it leads.

Model AGLM 5.3Z.ai70.6/1006 independent benchmarks$0.36 per solved task Model BQwen3.6 PlusQwen44.1/1003 independent benchmarks$1.19 per solved task

712912

Shared results: 71
GLM 5.3 only: 29
Qwen3.6 Plus only: 12
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 44.1.
6 and 3 benchmarks behind the scores
Intelligence
AA Intelligence Index
GLM 5.3
GLM 5.3 leads, 44.8 against 27.0.
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 87.4%.
Same board, both measured
Math
AIME
No call
Only Qwen3.6 Plus has a AIME result, 94.6%.
Directional only
Knowledge
MMLU Pro
Qwen3.6 Plus
Qwen3.6 Plus leads, 87.7% against 86.8%.
Same board, both measured
Web design
Design Arena: websites
GLM 5.3
GLM 5.3 leads, 1295 against 1243.
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 1M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.6 Plus
GLM 5.3 $0.0036, Qwen3.6 Plus $0.0013 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GLM 5.3
GLM 5.3 $0.36, Qwen3.6 Plus $1.19.
Published attempt cost and pass rate

At a glance

Coding index: GLM 5.3 70.6 from 6 benchmarks, Qwen3.6 Plus 44.1 from 3 benchmarks.
Widest category: Agentic work, 24.7 points apart on average over 5 shared benchmarks.
Widest single gap: Vibe Code Bench (Vals AI): GLM 5.3 78.1% against Qwen3.6 Plus 25.6%, 52.6 points apart on a 0 to 100 scale.
Output price: GLM 5.3 $4.40 against Qwen3.6 Plus $1.95 per million output tokens, 2.3x apart.
Context window: GLM 5.3 takes 1.05M tokens, Qwen3.6 Plus 1M.

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	Qwen3.6 Plus
Coding	12	1
Intelligence	2	0
Agentic work	5	0
Reasoning	9	0
Math	3	1
Knowledge	6	3
Web design	8	0
General	21	0
All shared boards	66	5

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

Coding benchmarks for GLM 5.3 and Qwen3.6 Plus
Benchmark	GLM 5.3	Qwen3.6 Plus	Leader
LiveBench coding	82.8%	Not measured	Only GLM 5.3 measured
LMArena WebDev	1621	1461	GLM 5.3
AA Coding Index	74.8	54.5	GLM 5.3
LiveCodeBench (Vals AI)	80.5%	86.0%	Qwen3.6 Plus
SWE-bench Verified (Vals AI)	95.4%	73.4%	GLM 5.3
Terminal-Bench 4 (Vals AI)	38.9%	Not measured	Only GLM 5.3 measured
Vibe Code Bench (Vals AI)	78.1%	25.6%	GLM 5.3
Code migration (Vals AI)	44.2%	11.1%	GLM 5.3
ProgramBench (Vals AI)	1.5%	0.0%	GLM 5.3
IOI (Vals AI)	68.4%	Not measured	Only GLM 5.3 measured
Terminal-Bench 2.1 (Vals AI)	71.5%	53.2%	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	43.9%	Only Qwen3.6 Plus measured
FrontierCode	40.1%	Not measured	Only GLM 5.3 measured
FrontierSWE	30.2%	Not measured	Only GLM 5.3 measured
WeirdML	75.4%	Not measured	Only GLM 5.3 measured
ALE-Bench	1317.4	670.1	GLM 5.3
LMArena Text: coding	1497	1467	GLM 5.3
LMArena Text: software and IT	1493	1462	GLM 5.3
LMArena WebDev: React	1641	1454	GLM 5.3
LMArena WebDev: HTML	1595	1463	GLM 5.3
LMArena WebDev: from an image	Not measured	1472	Only Qwen3.6 Plus measured
Neither model has a result on the other 14 boards here

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

Intelligence benchmarks for GLM 5.3 and Qwen3.6 Plus
Benchmark	GLM 5.3	Qwen3.6 Plus	Leader
AA Intelligence Index	44.8	27.0	GLM 5.3
Epoch Capabilities Index	156.0	147.3	GLM 5.3

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

Agentic work benchmarks for GLM 5.3 and Qwen3.6 Plus
Benchmark	GLM 5.3	Qwen3.6 Plus	Leader
AA Agentic Index	53.1	Not measured	Only GLM 5.3 measured
Design Arena: agents building mobile apps	1194	Not measured	Only GLM 5.3 measured
Design Arena: HTML slides	1189	Not measured	Only GLM 5.3 measured
Design Arena: PowerPoint slides	1230	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%	40.9%	GLM 5.3
Tax Agent Bench (Vals AI)	39.7%	7.7%	GLM 5.3
Legal Research Bench (Vals AI)	49.0%	14.9%	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%	32.9%	GLM 5.3
Terminal-Bench-Science (Vals AI)	5.7%	Not measured	Only GLM 5.3 measured
τ²-Bench Telecom	Not measured	97.7%	Only Qwen3.6 Plus measured
APEX-Agents	56.6%	Not measured	Only GLM 5.3 measured
Vending-Bench 2	$8,164	$5,115	GLM 5.3
Neither model has a result on the other 24 boards here

ReasoningLike-for-likePublic boards, shown as published, kept out of the index90

Reasoning benchmarks for GLM 5.3 and Qwen3.6 Plus
Benchmark	GLM 5.3	Qwen3.6 Plus	Leader
GPQA Diamond	88.1%	87.4%	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	84.2%	Only Qwen3.6 Plus measured
GPQA Diamond (Artificial Analysis run)	91.7%	88.2%	GLM 5.3
Humanity's Last Exam (Artificial Analysis run)	42.3%	27.8%	GLM 5.3
GPQA Diamond (Epoch AI run)	90.9%	88.4%	GLM 5.3
Chess puzzles	37.0%	14.0%	GLM 5.3
Mystery game puzzles	33.0%	12.0%	GLM 5.3
LMArena Text: hard prompts	1490	1448	GLM 5.3
LMArena Text: expert	1514	1454	GLM 5.3
LMArena Text: hard prompts in English	1496	1455	GLM 5.3
Neither model has a result on the other 10 boards here

MathLike-for-likePublic boards, shown as published, kept out of the index31

Math benchmarks for GLM 5.3 and Qwen3.6 Plus
Benchmark	GLM 5.3	Qwen3.6 Plus	Leader
AIME	Not measured	94.6%	Only Qwen3.6 Plus measured
OTIS Mock AIME	91.1%	93.3%	Qwen3.6 Plus
FrontierMath, tiers 1 to 3	68.8%	38.2%	GLM 5.3
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	1449	GLM 5.3
LMArena Text: mathematical work	1497	1448	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 index63

Knowledge benchmarks for GLM 5.3 and Qwen3.6 Plus
Benchmark	GLM 5.3	Qwen3.6 Plus	Leader
MMLU Pro	86.8%	87.7%	Qwen3.6 Plus
LegalBench (Vals AI)	84.8%	84.2%	GLM 5.3
CaseLaw (Vals AI)	Not measured	51.5%	Only Qwen3.6 Plus measured
CorpFin (Vals AI)	Not measured	61.9%	Only Qwen3.6 Plus measured
TaxEval (Vals AI)	72.4%	74.7%	Qwen3.6 Plus
MortgageTax (Vals AI)	Not measured	68.0%	Only Qwen3.6 Plus measured
MedCode (Vals AI)	42.9%	36.9%	GLM 5.3
SimpleQA Verified	41.0%	44.1%	Qwen3.6 Plus
LMArena Text: science	1498	1439	GLM 5.3
LMArena Text: medicine and healthcare	1481	1442	GLM 5.3
LMArena Text: legal and government	1473	1437	GLM 5.3
LMArena Text: business and finance	1459	1430	GLM 5.3
Neither model has a result on the other 6 boards here

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

Web design benchmarks for GLM 5.3 and Qwen3.6 Plus
Benchmark	GLM 5.3	Qwen3.6 Plus	Leader
Design Arena: websites	1295	1243	GLM 5.3
Design Arena: UI components	1314	1230	GLM 5.3
Design Arena: game dev	1311	1211	GLM 5.3
Design Arena: data viz	1260	1228	GLM 5.3
Design Arena: 3D	1332	1202	GLM 5.3
Design Arena: all code categories	1304	1236	GLM 5.3
Design Arena: SVG	1294	1161	GLM 5.3
Design Arena: ASCII art	1208	1113	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 index210

General benchmarks for GLM 5.3 and Qwen3.6 Plus
Benchmark	GLM 5.3	Qwen3.6 Plus	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	44.9%	Only Qwen3.6 Plus measured
MedScribe (Vals AI)	88.8%	77.0%	GLM 5.3
Vals Multimodal Index	Not measured	51.5%	Only Qwen3.6 Plus measured
IFBench	Not measured	75.2%	Only Qwen3.6 Plus measured
Long context reasoning (AA-LCR)	79.7%	78.3%	GLM 5.3
DTBench	87.7%	81.9%	GLM 5.3
LMCA	55.5%	33.1%	GLM 5.3
CL-bench	Not measured	20.3%	Only Qwen3.6 Plus measured
LMArena Text	1474	1436	GLM 5.3
LMArena Text: instruction following	1479	1424	GLM 5.3
LMArena Text: creative writing	1458	1404	GLM 5.3
LMArena Text: English	1482	1445	GLM 5.3
LMArena Text: non-English	1459	1424	GLM 5.3
LMArena Text: Chinese	1532	1477	GLM 5.3
LMArena Text: German	1488	1451	GLM 5.3
LMArena Text: Japanese	1460	1390	GLM 5.3
LMArena Text: Korean	1460	1379	GLM 5.3
LMArena Text: French	1503	1455	GLM 5.3
LMArena Text: Spanish	1462	1432	GLM 5.3
LMArena Text: Russian	1469	1434	GLM 5.3
LMArena Text: Polish	1466	1433	GLM 5.3
LMArena Text: multi-turn	1472	1438	GLM 5.3
LMArena Text: longer queries	1483	1439	GLM 5.3
LMArena Text: writing and language	1469	1418	GLM 5.3
LMArena Text: entertainment and media	1447	1402	GLM 5.3
Neither model has a result on the other 18 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 Qwen3.6 Plus
Benchmark	GLM 5.3	Qwen3.6 Plus	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

Qwen3.6 Plus$0.0013Fits in one request

Qwen3.6 Plus costs less

Repository review

50K input + 3K output tokens

GLM 5.3$0.083Fits in one request

Qwen3.6 Plus$0.022Fits in one request

Qwen3.6 Plus costs less

Agent loop

200K input + 10K output tokens

GLM 5.3$0.324Fits in one request

Qwen3.6 Plus$0.085Fits in one request

Qwen3.6 Plus 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 Qwen3.6 Plus
Specification	Z.ai GLM 5.3	Qwen3.6 Plus
Maker	Z.ai	Qwen
Weights	Open weights	No public weights listed
Released	August 18, 2026	April 2, 2026
Context window	1.05M	1M
Input $/Mtok	$1.40	$0.325
Output $/Mtok	$4.40	$1.95
Coding index	70.6	44.1
Independent measurements behind it	6	3
Cost per solved task	$0.36	$1.19
Source	OpenRouter listing	OpenRouter listing

Questions

Is GLM 5.3 better than Qwen3.6 Plus for coding?

Pick GLM 5.3 for coding: it scores 70.6 on the Vibe Coding Index against 44.1 for Qwen3.6 Plus. Pick Qwen3.6 Plus for output tokens 2.3x cheaper ($1.95 vs $4.40 per million) and work measured by LiveCodeBench (Vals AI) or OTIS Mock AIME, where it leads.

Which is cheaper, GLM 5.3 or Qwen3.6 Plus?

GLM 5.3 costs $1.40 per million input tokens and $4.40 per million output tokens; Qwen3.6 Plus costs $0.325 and $1.95. Per solved SWE-bench Verified task, GLM 5.3 costs $0.36 and Qwen3.6 Plus $1.19.

Which has the bigger context window, GLM 5.3 or Qwen3.6 Plus?

GLM 5.3, with 1.05M tokens against 1M.

How many benchmarks is the GLM 5.3 vs Qwen3.6 Plus comparison based on?

71 benchmarks have a result for both models as of October 22, 2026. GLM 5.3's coding index rests on 6 independent benchmarks and Qwen3.6 Plus's on 3. Where only one model has a result, the page says so instead of naming a winner.

Compare next

Embed this comparison on your site

Free under CC BY 4.0, while the source link stays visible. Preview the frame · Terms and other embeds

<iframe src="https://llmbenchmark.ai/embed/compare/glm-5-3-vs-qwen3-6-plus" title="GLM 5.3 vs Qwen3.6 Plus, LLMBenchmark.ai" width="100%" height="360" style="border:0;max-width:100%" loading="lazy"></iframe>
<p style="font:13px/1.4 sans-serif;margin:6px 0 0">Source: <a href="https://llmbenchmark.ai/compare/glm-5-3-vs-qwen3-6-plus">LLMBenchmark.ai, GLM 5.3 vs Qwen3.6 Plus</a></p>

GLM 5.3 or Qwen3.6 Plus: 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