GLM 5.3 vs Qwen3.7 Max

GLM 5.3 leads Qwen3.7 Max on coding, 70.6 to 54.1 on the Vibe Coding Index, at about the same output price ($4.40 vs $4.425 per million). Updated October 21, 2026; 48 benchmarks have a result for both.

Replace GLM 5.3 Replace Qwen3.7 Max

GLM 5.3Z.ai

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

Qwen3.7 MaxQwen

Coding: 54.1
Overall: 61.7
Output $/M: $4.425

vs boards won of 48 shared, 1 tied

GLM 5.3 or Qwen3.7 Max: which to pick

Pick GLM 5.3 for coding: it scores 70.6 on the Vibe Coding Index against 54.1 for Qwen3.7 Max.

Pick Qwen3.7 Max for work measured by LiveCodeBench (Vals AI) or Excel Modeling Benchmark (Vals AI), where it leads.

Model AGLM 5.3Z.ai70.6/1006 independent benchmarks$0.36 per solved task Model BQwen3.7 MaxQwen54.1/1003 independent benchmarks$4.69 per solved task

485211

Shared results: 48
GLM 5.3 only: 52
Qwen3.7 Max only: 11
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 54.1.
6 and 3 benchmarks behind the scores
Intelligence
AA Intelligence Index
GLM 5.3
GLM 5.3 leads, 44.8 against 29.5.
Same board, both measured
Agentic work
AA Agentic Index
GLM 5.3
GLM 5.3 leads, 53.1 against 22.5.
Same board, both measured
Reasoning
GPQA Diamond
Qwen3.7 Max
Qwen3.7 Max leads, 90.2% against 88.1%.
Same board, both measured
Knowledge
MMLU Pro
Qwen3.7 Max
Qwen3.7 Max leads, 89.3% against 86.8%.
Same board, both measured
Web design
Design Arena: websites
GLM 5.3
GLM 5.3 leads, 1295 against 1274.
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
GLM 5.3
GLM 5.3 $0.0036, Qwen3.7 Max $0.0037 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GLM 5.3
GLM 5.3 $0.36, Qwen3.7 Max $4.69.
Published attempt cost and pass rate

At a glance

Coding index: GLM 5.3 70.6 from 6 benchmarks, Qwen3.7 Max 54.1 from 3 benchmarks.
Widest category: Coding, 15.6 points apart on average over 11 shared benchmarks.
Widest single gap: Code migration (Vals AI): GLM 5.3 44.2% against Qwen3.7 Max 13.1%, 31.1 points apart on a 0 to 100 scale.
Output price: GLM 5.3 $4.40 against Qwen3.7 Max $4.425 per million output tokens, 1.0x apart.
Context window: GLM 5.3 takes 1.05M tokens, Qwen3.7 Max 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	Qwen3.7 Max	Ties
Coding	10	1
Intelligence	2	0
Agentic work	7	1
Reasoning	3	2	1 tie
Math	2	2
Knowledge	1	4
Web design	7	1
General	3	1
All shared boards	35	12	1 tie

CodingLike-for-likeSix of these boards feed the Vibe Coding Index101

Coding benchmarks for GLM 5.3 and Qwen3.7 Max
Benchmark	GLM 5.3	Qwen3.7 Max	Leader
LiveBench coding	82.8%	Not measured	Only GLM 5.3 measured
LMArena WebDev	1621	1515	GLM 5.3
AA Coding Index	74.8	66.0	GLM 5.3
LiveCodeBench (Vals AI)	80.5%	87.1%	Qwen3.7 Max
SWE-bench Verified (Vals AI)	95.4%	68.8%	GLM 5.3
Terminal-Bench 4 (Vals AI)	38.9%	Not measured	Only GLM 5.3 measured
Vibe Code Bench (Vals AI)	78.1%	47.7%	GLM 5.3
Code migration (Vals AI)	44.2%	13.1%	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%	61.0%	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%	49.5%	GLM 5.3
Terminal-Bench Hard (Artificial Analysis run)	Not measured	50.8%	Only Qwen3.7 Max 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	1189.4	GLM 5.3
LMArena Text: coding	1497	Not measured	Only GLM 5.3 measured
LMArena Text: software and IT	1493	Not measured	Only GLM 5.3 measured
LMArena WebDev: React	1641	1514	GLM 5.3
LMArena WebDev: HTML	1595	1512	GLM 5.3
Neither model has a result on the other 15 boards here

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

Intelligence benchmarks for GLM 5.3 and Qwen3.7 Max
Benchmark	GLM 5.3	Qwen3.7 Max	Leader
AA Intelligence Index	44.8	29.5	GLM 5.3
Epoch Capabilities Index	156.0	154.0	GLM 5.3

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

Agentic work benchmarks for GLM 5.3 and Qwen3.7 Max
Benchmark	GLM 5.3	Qwen3.7 Max	Leader
AA Agentic Index	53.1	22.5	GLM 5.3
Design Arena: agents building web apps	Not measured	1208	Only Qwen3.7 Max measured
Design Arena: agents building full-stack apps	Not measured	1191	Only Qwen3.7 Max measured
Design Arena: agents building mobile apps	1194	1155	GLM 5.3
Design Arena: agents building Android apps	Not measured	1159	Only Qwen3.7 Max measured
Design Arena: agents building Godot games	Not measured	1187	Only Qwen3.7 Max measured
Design Arena: agents building games	Not measured	1161	Only Qwen3.7 Max measured
Design Arena: HTML slides	1189	1158	GLM 5.3
Design Arena: PowerPoint slides	1254	1197	GLM 5.3
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%	47.8%	GLM 5.3
Tax Agent Bench (Vals AI)	39.7%	15.8%	GLM 5.3
Legal Research Bench (Vals AI)	49.0%	25.5%	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%	57.0%	Qwen3.7 Max
Terminal-Bench-Science (Vals AI)	5.7%	Not measured	Only GLM 5.3 measured
τ²-Bench Telecom	Not measured	94.7%	Only Qwen3.7 Max measured
APEX-Agents	56.6%	Not measured	Only GLM 5.3 measured
Vending-Bench 2	$8,164	Not measured	Only GLM 5.3 measured
Neither model has a result on the other 19 boards here

ReasoningLike-for-likePublic boards, shown as published, kept out of the index32

Reasoning benchmarks for GLM 5.3 and Qwen3.7 Max
Benchmark	GLM 5.3	Qwen3.7 Max	Leader
GPQA Diamond	88.1%	90.2%	Qwen3.7 Max
LiveBench reasoning	85.8%	Not measured	Only GLM 5.3 measured
MysteryMechanism (Vals AI)	23.0%	Not measured	Only GLM 5.3 measured
GPQA Diamond (Artificial Analysis run)	91.7%	92.3%	Qwen3.7 Max
Humanity's Last Exam (Artificial Analysis run)	42.3%	40.5%	GLM 5.3
GPQA Diamond (Epoch AI run)	90.9%	90.9%	Tie
SimpleBench	Not measured	70.4%	Only Qwen3.7 Max measured
Chess puzzles	37.0%	26.0%	GLM 5.3
Mystery game puzzles	33.0%	32.0%	GLM 5.3
EBR-bench	Not measured	9.5%	Only Qwen3.7 Max measured
LMArena Text: hard prompts	1490	Not measured	Only GLM 5.3 measured
LMArena Text: expert	1514	Not measured	Only GLM 5.3 measured
LMArena Text: hard prompts in English	1496	Not measured	Only GLM 5.3 measured
Neither model has a result on the other 9 boards here

MathLike-for-likePublic boards, shown as published, kept out of the index22

Math benchmarks for GLM 5.3 and Qwen3.7 Max
Benchmark	GLM 5.3	Qwen3.7 Max	Leader
OTIS Mock AIME	91.1%	95.6%	Qwen3.7 Max
FrontierMath, tiers 1 to 3	68.8%	64.6%	GLM 5.3
FrontierMath, tier 4	29.3%	34.1%	Qwen3.7 Max
ProofBench	49.0%	26.0%	GLM 5.3
LMArena Text: math	1495	Not measured	Only GLM 5.3 measured
LMArena Text: mathematical work	1497	Not measured	Only GLM 5.3 measured
Neither model has a result on the other 7 boards here

KnowledgeLike-for-likePublic boards, shown as published, kept out of the index14

Knowledge benchmarks for GLM 5.3 and Qwen3.7 Max
Benchmark	GLM 5.3	Qwen3.7 Max	Leader
MMLU Pro	86.8%	89.3%	Qwen3.7 Max
LegalBench (Vals AI)	84.8%	84.9%	Qwen3.7 Max
CorpFin (Vals AI)	Not measured	63.7%	Only Qwen3.7 Max measured
TaxEval (Vals AI)	72.4%	75.3%	Qwen3.7 Max
MedCode (Vals AI)	42.9%	38.8%	GLM 5.3
SimpleQA Verified	41.0%	55.8%	Qwen3.7 Max
LMArena Text: science	1498	Not measured	Only GLM 5.3 measured
LMArena Text: medicine and healthcare	1481	Not measured	Only GLM 5.3 measured
LMArena Text: legal and government	1473	Not measured	Only GLM 5.3 measured
LMArena Text: business and finance	1459	Not measured	Only GLM 5.3 measured
Neither model has a result on the other 8 boards here

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

Web design benchmarks for GLM 5.3 and Qwen3.7 Max
Benchmark	GLM 5.3	Qwen3.7 Max	Leader
Design Arena: websites	1295	1274	GLM 5.3
Design Arena: UI components	1314	1270	GLM 5.3
Design Arena: game dev	1311	1265	GLM 5.3
Design Arena: data viz	1259	1275	Qwen3.7 Max
Design Arena: 3D	1332	1266	GLM 5.3
Design Arena: all code categories	1304	1275	GLM 5.3
Design Arena: SVG	1293	1217	GLM 5.3
Design Arena: ASCII art	1213	1211	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 index31

General benchmarks for GLM 5.3 and Qwen3.7 Max
Benchmark	GLM 5.3	Qwen3.7 Max	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
MedScribe (Vals AI)	88.8%	79.4%	GLM 5.3
IFBench	Not measured	80.5%	Only Qwen3.7 Max measured
Long context reasoning (AA-LCR)	79.7%	79.0%	GLM 5.3
DTBench	87.7%	92.3%	Qwen3.7 Max
LMCA	55.5%	44.0%	GLM 5.3
LMArena Text	1474	Not measured	Only GLM 5.3 measured
LMArena Text: instruction following	1479	Not measured	Only GLM 5.3 measured
LMArena Text: creative writing	1458	Not measured	Only GLM 5.3 measured
LMArena Text: English	1482	Not measured	Only GLM 5.3 measured
LMArena Text: non-English	1459	Not measured	Only GLM 5.3 measured
LMArena Text: Chinese	1532	Not measured	Only GLM 5.3 measured
LMArena Text: German	1488	Not measured	Only GLM 5.3 measured
LMArena Text: Japanese	1460	Not measured	Only GLM 5.3 measured
LMArena Text: Korean	1460	Not measured	Only GLM 5.3 measured
LMArena Text: French	1503	Not measured	Only GLM 5.3 measured
LMArena Text: Spanish	1462	Not measured	Only GLM 5.3 measured
LMArena Text: Russian	1469	Not measured	Only GLM 5.3 measured
LMArena Text: Polish	1466	Not measured	Only GLM 5.3 measured
LMArena Text: multi-turn	1472	Not measured	Only GLM 5.3 measured
LMArena Text: longer queries	1483	Not measured	Only GLM 5.3 measured
LMArena Text: writing and language	1469	Not measured	Only GLM 5.3 measured
LMArena Text: entertainment and media	1447	Not measured	Only GLM 5.3 measured
Neither model has a result on the other 21 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 Qwen3.7 Max
Benchmark	GLM 5.3	Qwen3.7 Max	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

Qwen3.7 Max$0.0037Fits in one request

GLM 5.3 costs less

Repository review

50K input + 3K output tokens

GLM 5.3$0.083Fits in one request

Qwen3.7 Max$0.087Fits in one request

GLM 5.3 costs less

Agent loop

200K input + 10K output tokens

GLM 5.3$0.324Fits in one request

Qwen3.7 Max$0.339Fits in one request

GLM 5.3 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 Qwen3.7 Max
Specification	Z.ai GLM 5.3	Qwen3.7 Max
Maker	Z.ai	Qwen
Weights	Open weights	No public weights listed
Released	August 18, 2026	May 21, 2026
Context window	1.05M	1M
Input $/Mtok	$1.40	$1.475
Output $/Mtok	$4.40	$4.425
Coding index	70.6	54.1
Independent measurements behind it	6	3
Cost per solved task	$0.36	$4.69
Source	OpenRouter listing	OpenRouter listing

Questions

Is GLM 5.3 better than Qwen3.7 Max for coding?

Pick GLM 5.3 for coding: it scores 70.6 on the Vibe Coding Index against 54.1 for Qwen3.7 Max. Pick Qwen3.7 Max for work measured by LiveCodeBench (Vals AI) or Excel Modeling Benchmark (Vals AI), where it leads.

Which is cheaper, GLM 5.3 or Qwen3.7 Max?

GLM 5.3 costs $1.40 per million input tokens and $4.40 per million output tokens; Qwen3.7 Max costs $1.475 and $4.425. Per solved SWE-bench Verified task, GLM 5.3 costs $0.36 and Qwen3.7 Max $4.69.

Which has the bigger context window, GLM 5.3 or Qwen3.7 Max?

GLM 5.3, with 1.05M tokens against 1M.

How many benchmarks is the GLM 5.3 vs Qwen3.7 Max comparison based on?

48 benchmarks have a result for both models as of October 21, 2026. GLM 5.3's coding index rests on 6 independent benchmarks and Qwen3.7 Max'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-qwen3-7-max" title="GLM 5.3 vs Qwen3.7 Max, 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-qwen3-7-max">LLMBenchmark.ai, GLM 5.3 vs Qwen3.7 Max</a></p>

GLM 5.3 or Qwen3.7 Max: which to pick

Which one for your work

Coding

Intelligence

Agentic work

Reasoning

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