GLM 4.5 vs Qwen3.6 Plus

Updated October 21, 2026. 50 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GLM 4.5 Replace Qwen3.6 Plus

GLM 4.5Z.ai

Coding: 64.2
Overall: 12.8
Output $/M: $2.20

Qwen3.6 PlusQwen

Coding: 37.4
Overall: 27.0
Output $/M: $1.95

vs boards won of 50 shared

Decision reading

Pick GLM 4.5 for coding: it scores 64.2 on the Vibe Coding Index against 37.4 for Qwen3.6 Plus.

Pick Qwen3.6 Plus when output tokens cost 1.1x less, or when the context window is larger, 1M against 131K tokens.

Model AGLM 4.5Z.ai64.2/1001 independent benchmarkNo cost per solved task Model BQwen3.6 PlusQwen37.4/1001 independent benchmark$1.19 per solved task

501033

Shared results: 50
GLM 4.5 only: 10
Qwen3.6 Plus only: 33
Like-for-like categories: 8 of 9

Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.

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, four public coding benchmarks
GLM 4.5
GLM 4.5 scores 64.2 against 37.4.
One benchmark behind each score
Intelligence
AA Intelligence Index
Qwen3.6 Plus
Qwen3.6 Plus leads, 27.0 against 12.8.
Same board, both measured
Reasoning
GPQA Diamond
Qwen3.6 Plus
Qwen3.6 Plus leads, 87.4% against 72.2%.
Same board, both measured
Math
AIME
Qwen3.6 Plus
Qwen3.6 Plus leads, 94.6% against 86.7%.
Same board, both measured
Knowledge
MMLU Pro
Qwen3.6 Plus
Qwen3.6 Plus leads, 87.7% against 81.2%.
Same board, both measured
Web design
Design Arena: websites
Qwen3.6 Plus
Qwen3.6 Plus leads, 1243 against 1170.
Same board, both measured
Long documents
Listed context window
Qwen3.6 Plus
Qwen3.6 Plus takes 1M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.6 Plus
GLM 4.5 $0.0017, Qwen3.6 Plus $0.0013 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Qwen3.6 Plus has the number, $1.19.
Directional only

At a glance

Coding index: GLM 4.5 64.2 from 1 benchmark, Qwen3.6 Plus 37.4 from 1 benchmark.
Widest category: Agentic work, 54.7 points apart on average over 1 shared benchmark.
Widest single gap: τ²-Bench Telecom: GLM 4.5 43.0% against Qwen3.6 Plus 97.7%, 54.7 points apart on a 0 to 100 scale.
Output price: GLM 4.5 $2.20 against Qwen3.6 Plus $1.95 per million output tokens, 1.1x apart.
Context window: GLM 4.5 takes 131K 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 4.5	Qwen3.6 Plus
Coding	0	5
Intelligence	0	1
Agentic work	0	1
Reasoning	0	6
Math	0	3
Knowledge	3	5
Web design	0	7
General	4	15
All shared boards	7	43

CodingLike-for-likeFour of these boards feed the Vibe Coding Index05

Coding benchmarks for GLM 4.5 and Qwen3.6 Plus
Benchmark	GLM 4.5	Qwen3.6 Plus	Leader
SWE-bench Verified	64.2%	Not measured	Only GLM 4.5 measured
LMArena WebDev	Not measured	1461	Only Qwen3.6 Plus measured
AA Coding Index	Not measured	54.5	Only Qwen3.6 Plus measured
LiveCodeBench (Vals AI)	67.5%	86.0%	Qwen3.6 Plus
SWE-bench Verified (Vals AI)	Not measured	73.4%	Only Qwen3.6 Plus measured
Vibe Code Bench (Vals AI)	Not measured	25.6%	Only Qwen3.6 Plus measured
Code migration (Vals AI)	Not measured	11.1%	Only Qwen3.6 Plus measured
ProgramBench (Vals AI)	Not measured	0.0%	Only Qwen3.6 Plus measured
Terminal-Bench 2.1 (Vals AI)	Not measured	53.2%	Only Qwen3.6 Plus measured
LiveCodeBench (Artificial Analysis run)	73.8%	Not measured	Only GLM 4.5 measured
Terminal-Bench Hard (Artificial Analysis run)	22.0%	43.9%	Qwen3.6 Plus
WeirdML	40.6%	Not measured	Only GLM 4.5 measured
ALE-Bench	344.8	670.1	Qwen3.6 Plus
LMArena Text: coding	1434	1467	Qwen3.6 Plus
LMArena Text: software and IT	1440	1462	Qwen3.6 Plus
LMArena WebDev: React	Not measured	1454	Only Qwen3.6 Plus measured
LMArena WebDev: HTML	Not measured	1463	Only Qwen3.6 Plus measured
LMArena WebDev: from an image	Not measured	1472	Only Qwen3.6 Plus 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 4.5 and Qwen3.6 Plus
Benchmark	GLM 4.5	Qwen3.6 Plus	Leader
AA Intelligence Index	12.8	27.0	Qwen3.6 Plus
Epoch Capabilities Index	Not measured	147.3	Only Qwen3.6 Plus measured

Agentic workLike-for-likePublic boards, shown as published, kept out of the index01

Agentic work benchmarks for GLM 4.5 and Qwen3.6 Plus
Benchmark	GLM 4.5	Qwen3.6 Plus	Leader
Finance Agent (Vals AI)	Not measured	40.9%	Only Qwen3.6 Plus measured
Tax Agent Bench (Vals AI)	Not measured	7.7%	Only Qwen3.6 Plus measured
Legal Research Bench (Vals AI)	Not measured	14.9%	Only Qwen3.6 Plus measured
Excel Modeling Benchmark (Vals AI)	Not measured	32.9%	Only Qwen3.6 Plus measured
τ²-Bench Telecom	43.0%	97.7%	Qwen3.6 Plus
Vending-Bench 2	Not measured	$5,115	Only Qwen3.6 Plus measured
Neither model has a result on the other 33 boards here

ReasoningLike-for-likePublic boards, shown as published, kept out of the index06

Reasoning benchmarks for GLM 4.5 and Qwen3.6 Plus
Benchmark	GLM 4.5	Qwen3.6 Plus	Leader
GPQA Diamond	72.2%	87.4%	Qwen3.6 Plus
MMMU Pro (Vals AI)	Not measured	84.2%	Only Qwen3.6 Plus measured
GPQA Diamond (Artificial Analysis run)	78.2%	88.2%	Qwen3.6 Plus
Humanity's Last Exam (Artificial Analysis run)	13.0%	27.8%	Qwen3.6 Plus
GPQA Diamond (Epoch AI run)	Not measured	88.4%	Only Qwen3.6 Plus measured
Chess puzzles	Not measured	14.0%	Only Qwen3.6 Plus measured
Mystery game puzzles	Not measured	12.0%	Only Qwen3.6 Plus measured
LMArena Text: hard prompts	1429	1448	Qwen3.6 Plus
LMArena Text: expert	1433	1454	Qwen3.6 Plus
LMArena Text: hard prompts in English	1429	1455	Qwen3.6 Plus
Neither model has a result on the other 11 boards here

MathLike-for-likePublic boards, shown as published, kept out of the index03

Math benchmarks for GLM 4.5 and Qwen3.6 Plus
Benchmark	GLM 4.5	Qwen3.6 Plus	Leader
AIME	86.7%	94.6%	Qwen3.6 Plus
MATH 500 (Vals AI)	94.0%	Not measured	Only GLM 4.5 measured
MGSM (Vals AI)	90.8%	Not measured	Only GLM 4.5 measured
AA Math Index	73.7	Not measured	Only GLM 4.5 measured
AIME 2025 (Artificial Analysis run)	73.7%	Not measured	Only GLM 4.5 measured
OTIS Mock AIME	Not measured	93.3%	Only Qwen3.6 Plus measured
FrontierMath, tiers 1 to 3	Not measured	38.2%	Only Qwen3.6 Plus measured
LMArena Text: math	1427	1449	Qwen3.6 Plus
LMArena Text: mathematical work	1430	1448	Qwen3.6 Plus
Neither model has a result on the other 4 boards here

KnowledgeLike-for-likePublic boards, shown as published, kept out of the index35

Knowledge benchmarks for GLM 4.5 and Qwen3.6 Plus
Benchmark	GLM 4.5	Qwen3.6 Plus	Leader
MMLU Pro	81.2%	87.7%	Qwen3.6 Plus
LegalBench (Vals AI)	75.6%	84.2%	Qwen3.6 Plus
CaseLaw (Vals AI)	Not measured	51.5%	Only Qwen3.6 Plus measured
CorpFin (Vals AI)	61.0%	61.9%	Qwen3.6 Plus
TaxEval (Vals AI)	72.4%	74.7%	Qwen3.6 Plus
MortgageTax (Vals AI)	Not measured	68.0%	Only Qwen3.6 Plus measured
MedQA (Vals AI)	90.0%	Not measured	Only GLM 4.5 measured
MedCode (Vals AI)	Not measured	36.9%	Only Qwen3.6 Plus measured
MMLU Pro (Artificial Analysis run)	83.5%	Not measured	Only GLM 4.5 measured
SimpleQA Verified	Not measured	44.1%	Only Qwen3.6 Plus measured
LMArena Text: science	1444	1439	GLM 4.5
LMArena Text: medicine and healthcare	1455	1442	GLM 4.5
LMArena Text: legal and government	1438	1437	GLM 4.5
LMArena Text: business and finance	1420	1430	Qwen3.6 Plus
Neither model has a result on the other 3 boards here

Web designLike-for-likePublic boards, shown as published, kept out of the index07

Web design benchmarks for GLM 4.5 and Qwen3.6 Plus
Benchmark	GLM 4.5	Qwen3.6 Plus	Leader
Design Arena: websites	1170	1243	Qwen3.6 Plus
Design Arena: UI components	1144	1233	Qwen3.6 Plus
Design Arena: game dev	1145	1211	Qwen3.6 Plus
Design Arena: data viz	1159	1228	Qwen3.6 Plus
Design Arena: 3D	1170	1202	Qwen3.6 Plus
Design Arena: all code categories	1165	1236	Qwen3.6 Plus
Design Arena: SVG	1096	1160	Qwen3.6 Plus
Design Arena: ASCII art	Not measured	1115	Only Qwen3.6 Plus 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 4.5 and Qwen3.6 Plus
Benchmark	GLM 4.5	Qwen3.6 Plus	Leader
SAGE (Vals AI)	Not measured	44.9%	Only Qwen3.6 Plus measured
MedScribe (Vals AI)	Not measured	77.0%	Only Qwen3.6 Plus measured
Vals Multimodal Index	Not measured	51.5%	Only Qwen3.6 Plus measured
IFBench	44.1%	75.2%	Qwen3.6 Plus
Long context reasoning (AA-LCR)	52.7%	78.3%	Qwen3.6 Plus
DTBench	Not measured	81.9%	Only Qwen3.6 Plus measured
LMCA	Not measured	33.1%	Only Qwen3.6 Plus measured
Creative writing (Lech Mazur)	73.4%	Not measured	Only GLM 4.5 measured
CL-bench	Not measured	20.3%	Only Qwen3.6 Plus measured
LMArena Text	1430	1436	Qwen3.6 Plus
LMArena Text: instruction following	1404	1424	Qwen3.6 Plus
LMArena Text: creative writing	1395	1404	Qwen3.6 Plus
LMArena Text: English	1437	1445	Qwen3.6 Plus
LMArena Text: non-English	1417	1424	Qwen3.6 Plus
LMArena Text: Chinese	1465	1477	Qwen3.6 Plus
LMArena Text: German	1407	1451	Qwen3.6 Plus
LMArena Text: Japanese	1415	1390	GLM 4.5
LMArena Text: Korean	1380	1379	GLM 4.5
LMArena Text: French	1418	1455	Qwen3.6 Plus
LMArena Text: Spanish	1454	1432	GLM 4.5
LMArena Text: Russian	1413	1434	Qwen3.6 Plus
LMArena Text: Polish	1422	1433	Qwen3.6 Plus
LMArena Text: multi-turn	1415	1438	Qwen3.6 Plus
LMArena Text: longer queries	1412	1439	Qwen3.6 Plus
LMArena Text: writing and language	1403	1418	Qwen3.6 Plus
LMArena Text: entertainment and media	1404	1402	GLM 4.5
Neither model has a result on the other 20 boards here

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

Image generation benchmarks for GLM 4.5 and Qwen3.6 Plus
Benchmark	GLM 4.5	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 4.5$0.0017Fits in one request

Qwen3.6 Plus$0.0013Fits in one request

Qwen3.6 Plus costs less

Repository review

50K input + 3K output tokens

GLM 4.5$0.037Fits in one request

Qwen3.6 Plus$0.022Fits in one request

Qwen3.6 Plus costs less

Agent loop

200K input + 10K output tokens

GLM 4.5$0.142Exceeds the context window

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 4.5 and Qwen3.6 Plus
Specification	Z.ai GLM 4.5	Qwen3.6 Plus
Maker	Z.ai	Qwen
Weights	Open weights	No public weights listed
Released	July 25, 2025	April 2, 2026
Context window	131K	1M
Input $/Mtok	$0.60	$0.325
Output $/Mtok	$2.20	$1.95
Coding index	64.2	37.4
Independent measurements behind it	1	1
Cost per solved task	Not computable	$1.19
Source	OpenRouter listing	OpenRouter listing

Questions

Is GLM 4.5 better than Qwen3.6 Plus for coding?

Pick GLM 4.5 for coding: it scores 64.2 on the Vibe Coding Index against 37.4 for Qwen3.6 Plus. Pick Qwen3.6 Plus when output tokens cost 1.1x less, or when the context window is larger, 1M against 131K tokens.

Which is cheaper, GLM 4.5 or Qwen3.6 Plus?

GLM 4.5 costs $0.60 per million input tokens and $2.20 per million output tokens; Qwen3.6 Plus costs $0.325 and $1.95. Cost per solved task is known for Qwen3.6 Plus only.

Which has the bigger context window, GLM 4.5 or Qwen3.6 Plus?

Qwen3.6 Plus, with 1M tokens against 131K.

How many benchmarks is the GLM 4.5 vs Qwen3.6 Plus comparison based on?

50 benchmarks have a result for both models as of October 21, 2026. GLM 4.5's coding index rests on 1 independent benchmark and Qwen3.6 Plus's on 1. Where only one model has a result, the page says so instead of naming a winner.

Decision reading

Which one for your work

Coding

Intelligence

Reasoning

Math

Knowledge

Web design

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

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