GLM 5.3 vs Qwen3.8 27B

Updated October 21, 2026. 70 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GLM 5.3 Replace Qwen3.8 27B

GLM 5.3Z.ai

Coding: 73.1
Overall: 44.8
Output $/M: $1.40

Qwen3.8 27BQwen

Coding: 73.6
Overall: 33.7
Output $/M: $1.35

vs boards won of 70 shared

Decision reading

GLM 5.3 and Qwen3.8 27B are level for coding, 73.1 against 73.6 on the Vibe Coding Index, so price and context decide.

Pick GLM 5.3 when a solved task costs $0.36 against $1.26, or when the context window is larger, 1.05M against 1M tokens. Pick Qwen3.8 27B when output tokens cost 1.0x less, or when what matters is LiveBench coding or LiveCodeBench (Vals AI), where it leads.

Model AGLM 5.3Z.ai73.1/1002 independent benchmarks$0.36 per solved task Model BQwen3.8 27BQwen73.6/1002 independent benchmarks$1.26 per solved task

703015

Shared results: 70
GLM 5.3 only: 30
Qwen3.8 27B only: 15
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
No call
Level: 73.1 against 73.6, inside 2 points.
2 benchmarks behind each score
Intelligence
AA Intelligence Index
GLM 5.3
GLM 5.3 leads, 44.8 against 33.7.
Same board, both measured
Agentic work
AA Agentic Index
GLM 5.3
GLM 5.3 leads, 53.1 against 45.8.
Same board, both measured
Reasoning
GPQA Diamond
Qwen3.8 27B
Qwen3.8 27B leads, 88.9% against 88.1%.
Same board, both measured
Knowledge
MMLU Pro
GLM 5.3
GLM 5.3 leads, 86.8% against 84.3%.
Same board, both measured
Web design
Design Arena: websites
No call
Only GLM 5.3 has a Design Arena: websites result, 1295.
Directional only
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
Qwen3.8 27B
GLM 5.3 $0.0008, Qwen3.8 27B $0.0007 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GLM 5.3
GLM 5.3 $0.36, Qwen3.8 27B $1.26.
Published attempt cost and pass rate

At a glance

Coding index: GLM 5.3 73.1 from 2 benchmarks, Qwen3.8 27B 73.6 from 2 benchmarks.
Widest category: Math, 15.4 points apart on average over 3 shared benchmarks.
Widest single gap: ProofBench: GLM 5.3 49.0% against Qwen3.8 27B 16.0%, 33.0 points apart on a 0 to 100 scale.
Output price: GLM 5.3 $1.40 against Qwen3.8 27B $1.35 per million output tokens, 1.0x apart.
Context window: GLM 5.3 takes 1.05M tokens, Qwen3.8 27B 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	Qwen3.8 27B
Coding	15	2
Intelligence	2	0
Agentic work	8	2
Reasoning	6	1
Math	3	0
Knowledge	8	0
Web design	1	0
General	19	3
All shared boards	62	8

CodingLike-for-likeFour of these boards feed the Vibe Coding Index152

Coding benchmarks for GLM 5.3 and Qwen3.8 27B
Benchmark	GLM 5.3	Qwen3.8 27B	Leader
LiveBench coding	82.8%	86.8%	Qwen3.8 27B
LMArena WebDev	1621	1593	GLM 5.3
AA Coding Index	74.8	68.1	GLM 5.3
LiveCodeBench (Vals AI)	80.5%	84.0%	Qwen3.8 27B
SWE-bench Verified (Vals AI)	95.4%	86.0%	GLM 5.3
Terminal-Bench 4 (Vals AI)	38.9%	Not measured	Only GLM 5.3 measured
Vibe Code Bench (Vals AI)	78.1%	64.8%	GLM 5.3
Code migration (Vals AI)	44.2%	14.2%	GLM 5.3
ProgramBench (Vals AI)	1.5%	0.0%	GLM 5.3
IOI (Vals AI)	68.4%	39.1%	GLM 5.3
Terminal-Bench 2.1 (Vals AI)	71.5%	58.4%	GLM 5.3
Vibe Code Bench 1-100 (Vals AI)	20.0%	Not measured	Only GLM 5.3 measured
SkillsBench (Vals AI)	47.5%	38.1%	GLM 5.3
CyberBench (Vals AI)	72.1%	67.8%	GLM 5.3
SciCode	59.0%	46.6%	GLM 5.3
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	Not measured	Only GLM 5.3 measured
LMArena Text: coding	1497	1483	GLM 5.3
LMArena Text: software and IT	1493	1478	GLM 5.3
LMArena WebDev: React	1641	1602	GLM 5.3
LMArena WebDev: HTML	1595	1570	GLM 5.3
LMArena WebDev: from an image	Not measured	1577	Only Qwen3.8 27B 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 Qwen3.8 27B
Benchmark	GLM 5.3	Qwen3.8 27B	Leader
AA Intelligence Index	44.8	33.7	GLM 5.3
Epoch Capabilities Index	156.0	151.7	GLM 5.3

Agentic workLike-for-likePublic boards, shown as published, kept out of the index82

Agentic work benchmarks for GLM 5.3 and Qwen3.8 27B
Benchmark	GLM 5.3	Qwen3.8 27B	Leader
AA Agentic Index	53.1	45.8	GLM 5.3
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	1255	Not measured	Only GLM 5.3 measured
LiveBench research	73.2%	59.4%	GLM 5.3
LiveBench browser	0.0%	32.0%	Qwen3.8 27B
LiveBench long agentic	35.9%	27.9%	GLM 5.3
SREBench (Vals AI)	9.2%	Not measured	Only GLM 5.3 measured
Finance Agent (Vals AI)	55.8%	48.5%	GLM 5.3
Tax Agent Bench (Vals AI)	39.7%	30.9%	GLM 5.3
Legal Research Bench (Vals AI)	49.0%	36.1%	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%	59.7%	Qwen3.8 27B
Terminal-Bench-Science (Vals AI)	5.7%	0.0%	GLM 5.3
APEX-Agents	56.6%	47.5%	GLM 5.3
Vending-Bench 2	$8,164	Not measured	Only GLM 5.3 measured
Neither model has a result on the other 23 boards here

ReasoningLike-for-likePublic boards, shown as published, kept out of the index61

Reasoning benchmarks for GLM 5.3 and Qwen3.8 27B
Benchmark	GLM 5.3	Qwen3.8 27B	Leader
GPQA Diamond	88.1%	88.9%	Qwen3.8 27B
LiveBench reasoning	85.8%	80.0%	GLM 5.3
MysteryMechanism (Vals AI)	23.0%	Not measured	Only GLM 5.3 measured
MMMU Pro (Vals AI)	Not measured	83.9%	Only Qwen3.8 27B measured
GPQA Diamond (Artificial Analysis run)	91.7%	90.5%	GLM 5.3
Humanity's Last Exam (Artificial Analysis run)	42.3%	33.9%	GLM 5.3
GPQA Diamond (Epoch AI run)	90.9%	Not measured	Only GLM 5.3 measured
ARC-AGI	Not measured	87.5%	Only Qwen3.8 27B measured
ARC-AGI-2	Not measured	42.4%	Only Qwen3.8 27B 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	1459	GLM 5.3
LMArena Text: expert	1514	1479	GLM 5.3
LMArena Text: hard prompts in English	1496	1461	GLM 5.3
LMArena Vision: diagrams	Not measured	1285	Only Qwen3.8 27B measured
LMArena Vision: homework	Not measured	1295	Only Qwen3.8 27B measured
Neither model has a result on the other 5 boards here

MathLike-for-likePublic boards, shown as published, kept out of the index30

Math benchmarks for GLM 5.3 and Qwen3.8 27B
Benchmark	GLM 5.3	Qwen3.8 27B	Leader
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%	16.0%	GLM 5.3
LMArena Text: math	1495	1455	GLM 5.3
LMArena Text: mathematical work	1497	1458	GLM 5.3
Neither model has a result on the other 7 boards here

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

Knowledge benchmarks for GLM 5.3 and Qwen3.8 27B
Benchmark	GLM 5.3	Qwen3.8 27B	Leader
MMLU Pro	86.8%	84.3%	GLM 5.3
LegalBench (Vals AI)	84.8%	82.4%	GLM 5.3
TaxEval (Vals AI)	72.4%	70.8%	GLM 5.3
MortgageTax (Vals AI)	Not measured	64.9%	Only Qwen3.8 27B measured
MedCode (Vals AI)	42.9%	28.7%	GLM 5.3
SimpleQA Verified	41.0%	Not measured	Only GLM 5.3 measured
LMArena Text: science	1498	1467	GLM 5.3
LMArena Text: medicine and healthcare	1481	1437	GLM 5.3
LMArena Text: legal and government	1473	1447	GLM 5.3
LMArena Text: business and finance	1459	1438	GLM 5.3
Neither model has a result on the other 7 boards here

Web designLike-for-likePublic boards, shown as published, kept out of the index10

Web design benchmarks for GLM 5.3 and Qwen3.8 27B
Benchmark	GLM 5.3	Qwen3.8 27B	Leader
Design Arena: websites	1295	Not measured	Only GLM 5.3 measured
Design Arena: UI components	1314	Not measured	Only GLM 5.3 measured
Design Arena: game dev	1311	Not measured	Only GLM 5.3 measured
Design Arena: data viz	1260	Not measured	Only GLM 5.3 measured
Design Arena: 3D	1332	Not measured	Only GLM 5.3 measured
Design Arena: all code categories	1304	Not measured	Only GLM 5.3 measured
Design Arena: SVG	1292	Not measured	Only GLM 5.3 measured
Design Arena: ASCII art	1213	Not measured	Only GLM 5.3 measured
LiveBench design	77.3%	75.9%	GLM 5.3

GeneralLike-for-likePublic boards, shown as published, kept out of the index193

General benchmarks for GLM 5.3 and Qwen3.8 27B
Benchmark	GLM 5.3	Qwen3.8 27B	Leader
Vals Index	59.7%	Not measured	Only GLM 5.3 measured
LiveBench data analysis	70.2%	76.6%	Qwen3.8 27B
Public Benefits Bench (Vals AI)	68.5%	Not measured	Only GLM 5.3 measured
SAGE (Vals AI)	Not measured	52.4%	Only Qwen3.8 27B measured
MedScribe (Vals AI)	88.8%	83.8%	GLM 5.3
Long context reasoning (AA-LCR)	79.7%	82.0%	Qwen3.8 27B
DTBench	87.7%	88.0%	Qwen3.8 27B
LMCA	55.5%	41.4%	GLM 5.3
Surface Evolver Bench	Not measured	45.0%	Only Qwen3.8 27B measured
LMArena Text	1474	1440	GLM 5.3
LMArena Text: instruction following	1479	1436	GLM 5.3
LMArena Text: creative writing	1458	1383	GLM 5.3
LMArena Vision	Not measured	1269	Only Qwen3.8 27B measured
LMArena Text: English	1482	1448	GLM 5.3
LMArena Text: non-English	1459	1428	GLM 5.3
LMArena Text: Chinese	1532	1501	GLM 5.3
LMArena Text: German	1488	1443	GLM 5.3
LMArena Text: Japanese	1460	1387	GLM 5.3
LMArena Text: Korean	1460	1384	GLM 5.3
LMArena Text: French	1503	1465	GLM 5.3
LMArena Text: Spanish	1462	1449	GLM 5.3
LMArena Text: Russian	1469	1415	GLM 5.3
LMArena Text: Polish	1466	1439	GLM 5.3
LMArena Text: multi-turn	1472	1440	GLM 5.3
LMArena Text: longer queries	1483	1448	GLM 5.3
LMArena Text: writing and language	1469	1405	GLM 5.3
LMArena Text: entertainment and media	1447	1393	GLM 5.3
LMArena Vision: English	Not measured	1267	Only Qwen3.8 27B measured
LMArena Vision: Chinese	Not measured	1326	Only Qwen3.8 27B measured
LMArena Vision: reading text	Not measured	1281	Only Qwen3.8 27B measured
LMArena Vision: humor	Not measured	1235	Only Qwen3.8 27B measured
LMArena Vision: creative writing	Not measured	1237	Only Qwen3.8 27B measured
Neither model has a result on the other 14 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 Qwen3.8 27B
Benchmark	GLM 5.3	Qwen3.8 27B	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$0.0008Fits in one request

Qwen3.8 27B$0.0007Fits in one request

Qwen3.8 27B costs less

Repository review

50K input + 3K output tokens

GLM 5.3$0.0081Fits in one request

Qwen3.8 27B$0.0060Fits in one request

Qwen3.8 27B costs less

Agent loop

200K input + 10K output tokens

GLM 5.3$0.030Fits in one request

Qwen3.8 27B$0.021Fits in one request

Qwen3.8 27B 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 Qwen3.8 27B
Specification	Z.ai GLM 5.3	Qwen3.8 27B
Maker	Z.ai	Qwen
Weights	Open weights	Open weights
Released	August 18, 2026	August 14, 2026
Context window	1.05M	1M
Input $/Mtok	$0.079	$0.04
Output $/Mtok	$1.40	$1.35
Coding index	73.1	73.6
Independent measurements behind it	2	2
Cost per solved task	$0.36	$1.26
Source	OpenRouter listing	OpenRouter listing

Questions

Is GLM 5.3 better than Qwen3.8 27B for coding?

GLM 5.3 and Qwen3.8 27B are level for coding, 73.1 against 73.6 on the Vibe Coding Index, so price and context decide. Pick GLM 5.3 when a solved task costs $0.36 against $1.26, or when the context window is larger, 1.05M against 1M tokens. Pick Qwen3.8 27B when output tokens cost 1.0x less, or when what matters is LiveBench coding or LiveCodeBench (Vals AI), where it leads.

Which is cheaper, GLM 5.3 or Qwen3.8 27B?

GLM 5.3 costs $0.079 per million input tokens and $1.40 per million output tokens; Qwen3.8 27B costs $0.04 and $1.35. Per solved SWE-bench Verified task, GLM 5.3 costs $0.36 and Qwen3.8 27B $1.26.

Which has the bigger context window, GLM 5.3 or Qwen3.8 27B?

GLM 5.3, with 1.05M tokens against 1M.

How many benchmarks is the GLM 5.3 vs Qwen3.8 27B comparison based on?

70 benchmarks have a result for both models as of October 21, 2026. GLM 5.3's coding index rests on 2 independent benchmarks and Qwen3.8 27B's on 2. Where only one model has a result, the page says so instead of naming a winner.

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Decision reading

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

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