GLM 5.3 Flash vs Qwen3.7 Max

GLM 5.3 Flash leads Qwen3.7 Max on coding, 57.7 to 54.1 on the Vibe Coding Index, and GLM 5.3 Flash costs 8.8x less per output token ($0.50 vs $4.425 per million). Updated October 22, 2026; 41 benchmarks have a result for both.

Replace GLM 5.3 Flash Replace Qwen3.7 Max

GLM 5.3 FlashZ.ai

Coding: 57.7
Overall: 66.0
Output $/M: $0.50

Qwen3.7 MaxQwen

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

vs boards won of 41 shared

GLM 5.3 Flash or Qwen3.7 Max: which to pick

Pick GLM 5.3 Flash for coding: it scores 57.7 on the Vibe Coding Index against 54.1 for Qwen3.7 Max.

Pick Qwen3.7 Max for work measured by LiveCodeBench (Vals AI) or Vibe Code Bench (Vals AI), where it leads.

Model AGLM 5.3 FlashZ.ai57.7/1006 independent benchmarks$0.02 per solved task Model BQwen3.7 MaxQwen54.1/1003 independent benchmarks$4.69 per solved task

416118

Shared results: 41
GLM 5.3 Flash only: 61
Qwen3.7 Max only: 18
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 Flash
GLM 5.3 Flash scores 57.7 against 54.1.
6 and 3 benchmarks behind the scores
Intelligence
AA Intelligence Index
GLM 5.3 Flash
GLM 5.3 Flash leads, 41.8 against 29.5.
Same board, both measured
Agentic work
AA Agentic Index
GLM 5.3 Flash
GLM 5.3 Flash leads, 50.9 against 22.5.
Same board, both measured
Reasoning
GPQA Diamond
Qwen3.7 Max
Qwen3.7 Max leads, 90.2% against 86.4%.
Same board, both measured
Knowledge
MMLU Pro
Qwen3.7 Max
Qwen3.7 Max leads, 89.3% against 86.1%.
Same board, both measured
Web design
Design Arena: websites
Qwen3.7 Max
Qwen3.7 Max leads, 1274 against 1272.
Same board, both measured
General
Vals Index
No call
Only GLM 5.3 Flash has a Vals Index result, 50.9%.
Directional only
Long documents
Listed context window
GLM 5.3 Flash
GLM 5.3 Flash takes 1.05M tokens against 1M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 5.3 Flash
GLM 5.3 Flash $0.0004, Qwen3.7 Max $0.0037 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GLM 5.3 Flash
GLM 5.3 Flash $0.02, Qwen3.7 Max $4.69.
Published attempt cost and pass rate

At a glance

Coding index: GLM 5.3 Flash 57.7 from 6 benchmarks, Qwen3.7 Max 54.1 from 3 benchmarks.
Widest category: Agentic work, 15.0 points apart on average over 5 shared benchmarks.
Widest single gap: ALE-Bench: GLM 5.3 Flash 303.6 against Qwen3.7 Max 1189.4, 29.5 points apart on a 0 to 100 scale.
Output price: GLM 5.3 Flash $0.50 against Qwen3.7 Max $4.425 per million output tokens, 8.8x apart.
Context window: GLM 5.3 Flash 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 Flash	Qwen3.7 Max
Coding	8	3
Intelligence	1	1
Agentic work	4	1
Reasoning	0	6
Math	0	4
Knowledge	1	2
Web design	6	2
General	2	0
All shared boards	22	19

CodingLike-for-likeSix of these boards feed the Vibe Coding Index83

Coding benchmarks for GLM 5.3 Flash and Qwen3.7 Max
Benchmark	GLM 5.3 Flash	Qwen3.7 Max	Leader
LiveBench coding	80.6%	Not measured	Only GLM 5.3 Flash measured
LMArena WebDev	1607	1515	GLM 5.3 Flash
AA Coding Index	71.5	66.0	GLM 5.3 Flash
LiveCodeBench (Vals AI)	80.5%	87.1%	Qwen3.7 Max
SWE-bench Verified (Vals AI)	92.0%	68.8%	GLM 5.3 Flash
Terminal-Bench 4 (Vals AI)	37.4%	Not measured	Only GLM 5.3 Flash measured
Vibe Code Bench (Vals AI)	30.8%	47.7%	Qwen3.7 Max
Code migration (Vals AI)	20.5%	13.1%	GLM 5.3 Flash
ProgramBench (Vals AI)	0.0%	Not measured	Only GLM 5.3 Flash measured
IOI (Vals AI)	52.5%	Not measured	Only GLM 5.3 Flash measured
Terminal-Bench 2.1 (Vals AI)	62.9%	61.0%	GLM 5.3 Flash
Vibe Code Bench 1-100 (Vals AI)	16.0%	Not measured	Only GLM 5.3 Flash measured
SkillsBench (Vals AI)	40.2%	Not measured	Only GLM 5.3 Flash measured
CyberBench (Vals AI)	67.9%	Not measured	Only GLM 5.3 Flash measured
SciCode	51.6%	49.5%	GLM 5.3 Flash
Terminal-Bench Hard (Artificial Analysis run)	Not measured	50.8%	Only Qwen3.7 Max measured
FrontierCode	31.8%	Not measured	Only GLM 5.3 Flash measured
FrontierSWE	18.1%	Not measured	Only GLM 5.3 Flash measured
ALE-Bench	303.6	1189.4	Qwen3.7 Max
LMArena Text: coding	1507	Not measured	Only GLM 5.3 Flash measured
LMArena Text: software and IT	1500	Not measured	Only GLM 5.3 Flash measured
LMArena WebDev: React	1624	1514	GLM 5.3 Flash
LMArena WebDev: HTML	1577	1512	GLM 5.3 Flash
LMArena WebDev: from an image	1578	Not measured	Only GLM 5.3 Flash measured
Neither model has a result on the other 15 boards here

IntelligenceLike-for-likePublic boards, shown as published, kept out of the index11

Intelligence benchmarks for GLM 5.3 Flash and Qwen3.7 Max
Benchmark	GLM 5.3 Flash	Qwen3.7 Max	Leader
AA Intelligence Index	41.8	29.5	GLM 5.3 Flash
Epoch Capabilities Index	153.1	154.0	Qwen3.7 Max

Agentic workLike-for-likePublic boards, shown as published, kept out of the index41

Agentic work benchmarks for GLM 5.3 Flash and Qwen3.7 Max
Benchmark	GLM 5.3 Flash	Qwen3.7 Max	Leader
AA Agentic Index	50.9	22.5	GLM 5.3 Flash
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	Not measured	1155	Only Qwen3.7 Max measured
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	Not measured	1108	Only Qwen3.7 Max measured
Design Arena: PowerPoint slides	Not measured	1171	Only Qwen3.7 Max measured
LiveBench research	70.1%	Not measured	Only GLM 5.3 Flash measured
LiveBench browser	84.0%	Not measured	Only GLM 5.3 Flash measured
LiveBench long agentic	28.4%	Not measured	Only GLM 5.3 Flash measured
SREBench (Vals AI)	0.8%	Not measured	Only GLM 5.3 Flash measured
Finance Agent (Vals AI)	57.9%	47.8%	GLM 5.3 Flash
Tax Agent Bench (Vals AI)	31.6%	15.8%	GLM 5.3 Flash
Legal Research Bench (Vals AI)	45.2%	25.5%	GLM 5.3 Flash
Harvey's Legal Agent Benchmark (Vals AI)	10.4%	Not measured	Only GLM 5.3 Flash measured
Excel Modeling Benchmark (Vals AI)	55.9%	57.0%	Qwen3.7 Max
Terminal-Bench-Science (Vals AI)	2.9%	Not measured	Only GLM 5.3 Flash measured
τ²-Bench Telecom	Not measured	94.7%	Only Qwen3.7 Max measured
APEX-Agents	52.8%	Not measured	Only GLM 5.3 Flash measured
Neither model has a result on the other 20 boards here

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

Reasoning benchmarks for GLM 5.3 Flash and Qwen3.7 Max
Benchmark	GLM 5.3 Flash	Qwen3.7 Max	Leader
GPQA Diamond	86.4%	90.2%	Qwen3.7 Max
LiveBench reasoning	77.6%	Not measured	Only GLM 5.3 Flash measured
MMMU Pro (Vals AI)	86.0%	Not measured	Only GLM 5.3 Flash measured
GPQA Diamond (Artificial Analysis run)	91.2%	92.3%	Qwen3.7 Max
Humanity's Last Exam (Artificial Analysis run)	39.9%	40.5%	Qwen3.7 Max
GPQA Diamond (Epoch AI run)	90.2%	90.9%	Qwen3.7 Max
ARC-AGI	91.0%	Not measured	Only GLM 5.3 Flash measured
ARC-AGI-2	65.8%	Not measured	Only GLM 5.3 Flash measured
SimpleBench	Not measured	70.4%	Only Qwen3.7 Max measured
Chess puzzles	21.0%	26.0%	Qwen3.7 Max
Mystery game puzzles	8.0%	32.0%	Qwen3.7 Max
EBR-bench	Not measured	9.5%	Only Qwen3.7 Max measured
LMArena Text: hard prompts	1490	Not measured	Only GLM 5.3 Flash measured
LMArena Text: expert	1511	Not measured	Only GLM 5.3 Flash measured
LMArena Text: hard prompts in English	1488	Not measured	Only GLM 5.3 Flash measured
LMArena Vision: diagrams	1317	Not measured	Only GLM 5.3 Flash measured
LMArena Vision: homework	1318	Not measured	Only GLM 5.3 Flash measured
Neither model has a result on the other 5 boards here

MathLike-for-likePublic boards, shown as published, kept out of the index04

Math benchmarks for GLM 5.3 Flash and Qwen3.7 Max
Benchmark	GLM 5.3 Flash	Qwen3.7 Max	Leader
OTIS Mock AIME	93.9%	95.6%	Qwen3.7 Max
FrontierMath, tiers 1 to 3	55.8%	64.6%	Qwen3.7 Max
FrontierMath, tier 4	17.1%	34.1%	Qwen3.7 Max
ProofBench	21.0%	26.0%	Qwen3.7 Max
LMArena Text: math	1503	Not measured	Only GLM 5.3 Flash measured
LMArena Text: mathematical work	1497	Not measured	Only GLM 5.3 Flash measured
Neither model has a result on the other 7 boards here

KnowledgeLike-for-likePublic boards, shown as published, kept out of the index12

Knowledge benchmarks for GLM 5.3 Flash and Qwen3.7 Max
Benchmark	GLM 5.3 Flash	Qwen3.7 Max	Leader
MMLU Pro	86.1%	89.3%	Qwen3.7 Max
LegalBench (Vals AI)	83.9%	84.9%	Qwen3.7 Max
CorpFin (Vals AI)	Not measured	63.7%	Only Qwen3.7 Max measured
TaxEval (Vals AI)	75.6%	75.3%	GLM 5.3 Flash
MedCode (Vals AI)	Not measured	38.8%	Only Qwen3.7 Max measured
SimpleQA Verified	Not measured	55.8%	Only Qwen3.7 Max measured
LMArena Text: science	1484	Not measured	Only GLM 5.3 Flash measured
LMArena Text: medicine and healthcare	1475	Not measured	Only GLM 5.3 Flash measured
LMArena Text: legal and government	1473	Not measured	Only GLM 5.3 Flash measured
LMArena Text: business and finance	1460	Not measured	Only GLM 5.3 Flash measured
Neither model has a result on the other 8 boards here

Web designLike-for-likePublic boards, shown as published, kept out of the index62

Web design benchmarks for GLM 5.3 Flash and Qwen3.7 Max
Benchmark	GLM 5.3 Flash	Qwen3.7 Max	Leader
Design Arena: websites	1272	1274	Qwen3.7 Max
Design Arena: UI components	1316	1267	GLM 5.3 Flash
Design Arena: game dev	1278	1264	GLM 5.3 Flash
Design Arena: data viz	1262	1275	Qwen3.7 Max
Design Arena: 3D	1316	1265	GLM 5.3 Flash
Design Arena: all code categories	1282	1275	GLM 5.3 Flash
Design Arena: SVG	1285	1216	GLM 5.3 Flash
Design Arena: ASCII art	1267	1211	GLM 5.3 Flash
LiveBench design	77.2%	Not measured	Only GLM 5.3 Flash measured

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

General benchmarks for GLM 5.3 Flash and Qwen3.7 Max
Benchmark	GLM 5.3 Flash	Qwen3.7 Max	Leader
Vals Index	50.9%	Not measured	Only GLM 5.3 Flash measured
LiveBench data analysis	76.4%	Not measured	Only GLM 5.3 Flash measured
MedScribe (Vals AI)	88.9%	79.4%	GLM 5.3 Flash
IFBench	Not measured	80.5%	Only Qwen3.7 Max measured
Long context reasoning (AA-LCR)	80.0%	79.0%	GLM 5.3 Flash
DTBench	Not measured	92.3%	Only Qwen3.7 Max measured
LMCA	Not measured	44.0%	Only Qwen3.7 Max measured
Surface Evolver Bench	52.5%	Not measured	Only GLM 5.3 Flash measured
LMArena Text	1471	Not measured	Only GLM 5.3 Flash measured
LMArena Text: instruction following	1478	Not measured	Only GLM 5.3 Flash measured
LMArena Text: creative writing	1441	Not measured	Only GLM 5.3 Flash measured
LMArena Vision	1294	Not measured	Only GLM 5.3 Flash measured
LMArena Text: English	1474	Not measured	Only GLM 5.3 Flash measured
LMArena Text: non-English	1462	Not measured	Only GLM 5.3 Flash measured
LMArena Text: Chinese	1531	Not measured	Only GLM 5.3 Flash measured
LMArena Text: German	1462	Not measured	Only GLM 5.3 Flash measured
LMArena Text: Japanese	1444	Not measured	Only GLM 5.3 Flash measured
LMArena Text: Korean	1436	Not measured	Only GLM 5.3 Flash measured
LMArena Text: French	1491	Not measured	Only GLM 5.3 Flash measured
LMArena Text: Spanish	1475	Not measured	Only GLM 5.3 Flash measured
LMArena Text: Russian	1465	Not measured	Only GLM 5.3 Flash measured
LMArena Text: Polish	1450	Not measured	Only GLM 5.3 Flash measured
LMArena Text: multi-turn	1469	Not measured	Only GLM 5.3 Flash measured
LMArena Text: longer queries	1481	Not measured	Only GLM 5.3 Flash measured
LMArena Text: writing and language	1455	Not measured	Only GLM 5.3 Flash measured
LMArena Text: entertainment and media	1440	Not measured	Only GLM 5.3 Flash measured
LMArena Vision: English	1295	Not measured	Only GLM 5.3 Flash measured
LMArena Vision: Chinese	1363	Not measured	Only GLM 5.3 Flash measured
LMArena Vision: reading text	1299	Not measured	Only GLM 5.3 Flash measured
LMArena Vision: humor	1279	Not measured	Only GLM 5.3 Flash measured
LMArena Vision: creative writing	1270	Not measured	Only GLM 5.3 Flash measured
Neither model has a result on the other 15 boards here

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

Image generation benchmarks for GLM 5.3 Flash and Qwen3.7 Max
Benchmark	GLM 5.3 Flash	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 Flash$0.0004Fits in one request

Qwen3.7 Max$0.0037Fits in one request

GLM 5.3 Flash costs less

Repository review

50K input + 3K output tokens

GLM 5.3 Flash$0.0090Fits in one request

Qwen3.7 Max$0.087Fits in one request

GLM 5.3 Flash costs less

Agent loop

200K input + 10K output tokens

GLM 5.3 Flash$0.035Fits in one request

Qwen3.7 Max$0.339Fits in one request

GLM 5.3 Flash 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 Flash and Qwen3.7 Max
Specification	Z.ai GLM 5.3 Flash	Qwen3.7 Max
Maker	Z.ai	Qwen
Weights	Open weights	No public weights listed
Released	August 26, 2026	May 21, 2026
Context window	1.05M	1M
Input $/Mtok	$0.15	$1.475
Output $/Mtok	$0.50	$4.425
Coding index	57.7	54.1
Independent measurements behind it	6	3
Cost per solved task	$0.02	$4.69
Source	OpenRouter listing	OpenRouter listing

Questions

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

Pick GLM 5.3 Flash for coding: it scores 57.7 on the Vibe Coding Index against 54.1 for Qwen3.7 Max. Pick Qwen3.7 Max for work measured by LiveCodeBench (Vals AI) or Vibe Code Bench (Vals AI), where it leads.

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

GLM 5.3 Flash costs $0.15 per million input tokens and $0.50 per million output tokens; Qwen3.7 Max costs $1.475 and $4.425. Per solved SWE-bench Verified task, GLM 5.3 Flash costs $0.02 and Qwen3.7 Max $4.69.

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

GLM 5.3 Flash, with 1.05M tokens against 1M.

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

41 benchmarks have a result for both models as of October 22, 2026. GLM 5.3 Flash'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.

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<iframe src="https://llmbenchmark.ai/embed/compare/glm-5-3-flash-vs-qwen3-7-max" title="GLM 5.3 Flash 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-flash-vs-qwen3-7-max">LLMBenchmark.ai, GLM 5.3 Flash vs Qwen3.7 Max</a></p>

GLM 5.3 Flash 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