GLM 5.3 Flash vs Qwen3.8 27B

Updated October 21, 2026. 80 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 Flash Replace Qwen3.8 27B

GLM 5.3 FlashZ.ai

Coding: 71.0
Overall: 41.8
Output $/M: $0.50

Qwen3.8 27BQwen

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

vs boards won of 80 shared, 1 tied

Decision reading

Pick Qwen3.8 27B for coding: it scores 73.6 on the Vibe Coding Index against 71.0 for GLM 5.3 Flash.

Pick GLM 5.3 Flash when output tokens cost 2.7x less, or when a solved task costs $0.02 against $1.26.

Model AGLM 5.3 FlashZ.ai71.0/1002 independent benchmarks$0.02 per solved task Model BQwen3.8 27BQwen73.6/1002 independent benchmarks$1.26 per solved task

80225

Shared results: 80
GLM 5.3 Flash only: 22
Qwen3.8 27B only: 5
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
Qwen3.8 27B
Qwen3.8 27B scores 73.6 against 71.0.
2 benchmarks behind each score
Intelligence
AA Intelligence Index
GLM 5.3 Flash
GLM 5.3 Flash leads, 41.8 against 33.7.
Same board, both measured
Agentic work
AA Agentic Index
GLM 5.3 Flash
GLM 5.3 Flash leads, 50.9 against 45.8.
Same board, both measured
Reasoning
GPQA Diamond
Qwen3.8 27B
Qwen3.8 27B leads, 88.9% against 86.4%.
Same board, both measured
Knowledge
MMLU Pro
GLM 5.3 Flash
GLM 5.3 Flash leads, 86.1% against 84.3%.
Same board, both measured
Web design
Design Arena: websites
No call
Only GLM 5.3 Flash has a Design Arena: websites result, 1272.
Directional only
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, Qwen3.8 27B $0.0007 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GLM 5.3 Flash
GLM 5.3 Flash $0.02, Qwen3.8 27B $1.26.
Published attempt cost and pass rate

At a glance

Coding index: GLM 5.3 Flash 71.0 from 2 benchmarks, Qwen3.8 27B 73.6 from 2 benchmarks.
Widest category: Agentic work, 9.9 points apart on average over 10 shared benchmarks.
Widest single gap: LiveBench browser: GLM 5.3 Flash 84.0% against Qwen3.8 27B 32.0%, 52.0 points apart on a 0 to 100 scale.
Output price: GLM 5.3 Flash $0.50 against Qwen3.8 27B $1.35 per million output tokens, 2.7x apart.
Context window: GLM 5.3 Flash 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 Flash	Qwen3.8 27B	Ties
Coding	14	3	1 tie
Intelligence	2	0
Agentic work	9	1
Reasoning	10	2
Math	3	0
Knowledge	7	0
Web design	1	0
General	25	2
All shared boards	71	8	1 tie

CodingLike-for-likeFour of these boards feed the Vibe Coding Index143

Coding benchmarks for GLM 5.3 Flash and Qwen3.8 27B
Benchmark	GLM 5.3 Flash	Qwen3.8 27B	Leader
LiveBench coding	80.6%	86.8%	Qwen3.8 27B
LMArena WebDev	1607	1593	GLM 5.3 Flash
AA Coding Index	71.5	68.1	GLM 5.3 Flash
LiveCodeBench (Vals AI)	80.5%	84.0%	Qwen3.8 27B
SWE-bench Verified (Vals AI)	92.0%	86.0%	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%	64.8%	Qwen3.8 27B
Code migration (Vals AI)	20.5%	14.2%	GLM 5.3 Flash
ProgramBench (Vals AI)	0.0%	0.0%	Tie
IOI (Vals AI)	52.5%	39.1%	GLM 5.3 Flash
Terminal-Bench 2.1 (Vals AI)	62.9%	58.4%	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%	38.1%	GLM 5.3 Flash
CyberBench (Vals AI)	67.9%	67.8%	GLM 5.3 Flash
SciCode	51.6%	46.6%	GLM 5.3 Flash
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	Not measured	Only GLM 5.3 Flash measured
LMArena Text: coding	1507	1483	GLM 5.3 Flash
LMArena Text: software and IT	1500	1478	GLM 5.3 Flash
LMArena WebDev: React	1624	1602	GLM 5.3 Flash
LMArena WebDev: HTML	1577	1570	GLM 5.3 Flash
LMArena WebDev: from an image	1578	1577	GLM 5.3 Flash
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 Flash and Qwen3.8 27B
Benchmark	GLM 5.3 Flash	Qwen3.8 27B	Leader
AA Intelligence Index	41.8	33.7	GLM 5.3 Flash
Epoch Capabilities Index	153.1	151.7	GLM 5.3 Flash

Agentic workLike-for-likePublic boards, shown as published, kept out of the index91

Agentic work benchmarks for GLM 5.3 Flash and Qwen3.8 27B
Benchmark	GLM 5.3 Flash	Qwen3.8 27B	Leader
AA Agentic Index	50.9	45.8	GLM 5.3 Flash
LiveBench research	70.1%	59.4%	GLM 5.3 Flash
LiveBench browser	84.0%	32.0%	GLM 5.3 Flash
LiveBench long agentic	28.4%	27.9%	GLM 5.3 Flash
SREBench (Vals AI)	0.8%	Not measured	Only GLM 5.3 Flash measured
Finance Agent (Vals AI)	57.9%	48.5%	GLM 5.3 Flash
Tax Agent Bench (Vals AI)	31.6%	30.9%	GLM 5.3 Flash
Legal Research Bench (Vals AI)	45.2%	36.1%	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%	59.7%	Qwen3.8 27B
Terminal-Bench-Science (Vals AI)	2.9%	0.0%	GLM 5.3 Flash
APEX-Agents	52.8%	47.5%	GLM 5.3 Flash
Neither model has a result on the other 27 boards here

ReasoningLike-for-likePublic boards, shown as published, kept out of the index102

Reasoning benchmarks for GLM 5.3 Flash and Qwen3.8 27B
Benchmark	GLM 5.3 Flash	Qwen3.8 27B	Leader
GPQA Diamond	86.4%	88.9%	Qwen3.8 27B
LiveBench reasoning	77.6%	80.0%	Qwen3.8 27B
MMMU Pro (Vals AI)	86.0%	83.9%	GLM 5.3 Flash
GPQA Diamond (Artificial Analysis run)	91.2%	90.5%	GLM 5.3 Flash
Humanity's Last Exam (Artificial Analysis run)	39.9%	33.9%	GLM 5.3 Flash
GPQA Diamond (Epoch AI run)	90.2%	Not measured	Only GLM 5.3 Flash measured
ARC-AGI	91.0%	87.5%	GLM 5.3 Flash
ARC-AGI-2	65.8%	42.4%	GLM 5.3 Flash
Chess puzzles	21.0%	Not measured	Only GLM 5.3 Flash measured
Mystery game puzzles	8.0%	Not measured	Only GLM 5.3 Flash measured
LMArena Text: hard prompts	1490	1459	GLM 5.3 Flash
LMArena Text: expert	1511	1479	GLM 5.3 Flash
LMArena Text: hard prompts in English	1488	1461	GLM 5.3 Flash
LMArena Vision: diagrams	1317	1285	GLM 5.3 Flash
LMArena Vision: homework	1318	1295	GLM 5.3 Flash
Neither model has a result on the other 6 boards here

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

Math benchmarks for GLM 5.3 Flash and Qwen3.8 27B
Benchmark	GLM 5.3 Flash	Qwen3.8 27B	Leader
OTIS Mock AIME	93.9%	Not measured	Only GLM 5.3 Flash measured
FrontierMath, tiers 1 to 3	55.8%	Not measured	Only GLM 5.3 Flash measured
FrontierMath, tier 4	17.1%	Not measured	Only GLM 5.3 Flash measured
ProofBench	21.0%	16.0%	GLM 5.3 Flash
LMArena Text: math	1503	1455	GLM 5.3 Flash
LMArena Text: mathematical work	1497	1458	GLM 5.3 Flash
Neither model has a result on the other 7 boards here

KnowledgeLike-for-likePublic boards, shown as published, kept out of the index70

Knowledge benchmarks for GLM 5.3 Flash and Qwen3.8 27B
Benchmark	GLM 5.3 Flash	Qwen3.8 27B	Leader
MMLU Pro	86.1%	84.3%	GLM 5.3 Flash
LegalBench (Vals AI)	83.9%	82.4%	GLM 5.3 Flash
TaxEval (Vals AI)	75.6%	70.8%	GLM 5.3 Flash
MortgageTax (Vals AI)	Not measured	64.9%	Only Qwen3.8 27B measured
MedCode (Vals AI)	Not measured	28.7%	Only Qwen3.8 27B measured
LMArena Text: science	1484	1467	GLM 5.3 Flash
LMArena Text: medicine and healthcare	1475	1437	GLM 5.3 Flash
LMArena Text: legal and government	1473	1447	GLM 5.3 Flash
LMArena Text: business and finance	1460	1438	GLM 5.3 Flash
Neither model has a result on the other 8 boards here

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

Web design benchmarks for GLM 5.3 Flash and Qwen3.8 27B
Benchmark	GLM 5.3 Flash	Qwen3.8 27B	Leader
Design Arena: websites	1272	Not measured	Only GLM 5.3 Flash measured
Design Arena: UI components	1316	Not measured	Only GLM 5.3 Flash measured
Design Arena: game dev	1279	Not measured	Only GLM 5.3 Flash measured
Design Arena: data viz	1262	Not measured	Only GLM 5.3 Flash measured
Design Arena: 3D	1316	Not measured	Only GLM 5.3 Flash measured
Design Arena: all code categories	1282	Not measured	Only GLM 5.3 Flash measured
Design Arena: SVG	1284	Not measured	Only GLM 5.3 Flash measured
Design Arena: ASCII art	1269	Not measured	Only GLM 5.3 Flash measured
LiveBench design	77.2%	75.9%	GLM 5.3 Flash

GeneralLike-for-likePublic boards, shown as published, kept out of the index252

General benchmarks for GLM 5.3 Flash and Qwen3.8 27B
Benchmark	GLM 5.3 Flash	Qwen3.8 27B	Leader
Vals Index	50.9%	Not measured	Only GLM 5.3 Flash measured
LiveBench data analysis	76.4%	76.6%	Qwen3.8 27B
SAGE (Vals AI)	Not measured	52.4%	Only Qwen3.8 27B measured
MedScribe (Vals AI)	88.9%	83.8%	GLM 5.3 Flash
Long context reasoning (AA-LCR)	80.0%	82.0%	Qwen3.8 27B
DTBench	Not measured	88.0%	Only Qwen3.8 27B measured
LMCA	Not measured	41.4%	Only Qwen3.8 27B measured
Surface Evolver Bench	52.5%	45.0%	GLM 5.3 Flash
LMArena Text	1471	1440	GLM 5.3 Flash
LMArena Text: instruction following	1478	1436	GLM 5.3 Flash
LMArena Text: creative writing	1441	1383	GLM 5.3 Flash
LMArena Vision	1294	1269	GLM 5.3 Flash
LMArena Text: English	1474	1448	GLM 5.3 Flash
LMArena Text: non-English	1462	1428	GLM 5.3 Flash
LMArena Text: Chinese	1531	1501	GLM 5.3 Flash
LMArena Text: German	1462	1443	GLM 5.3 Flash
LMArena Text: Japanese	1444	1387	GLM 5.3 Flash
LMArena Text: Korean	1436	1384	GLM 5.3 Flash
LMArena Text: French	1491	1465	GLM 5.3 Flash
LMArena Text: Spanish	1475	1449	GLM 5.3 Flash
LMArena Text: Russian	1465	1415	GLM 5.3 Flash
LMArena Text: Polish	1450	1439	GLM 5.3 Flash
LMArena Text: multi-turn	1469	1440	GLM 5.3 Flash
LMArena Text: longer queries	1481	1448	GLM 5.3 Flash
LMArena Text: writing and language	1455	1405	GLM 5.3 Flash
LMArena Text: entertainment and media	1440	1393	GLM 5.3 Flash
LMArena Vision: English	1295	1267	GLM 5.3 Flash
LMArena Vision: Chinese	1363	1326	GLM 5.3 Flash
LMArena Vision: reading text	1299	1281	GLM 5.3 Flash
LMArena Vision: humor	1279	1235	GLM 5.3 Flash
LMArena Vision: creative writing	1270	1237	GLM 5.3 Flash
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 Qwen3.8 27B
Benchmark	GLM 5.3 Flash	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 Flash$0.0004Fits in one request

Qwen3.8 27B$0.0007Fits 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

Qwen3.8 27B$0.0060Fits in one request

Qwen3.8 27B costs less

Agent loop

200K input + 10K output tokens

GLM 5.3 Flash$0.035Fits 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 Flash and Qwen3.8 27B
Specification	Z.ai GLM 5.3 Flash	Qwen3.8 27B
Maker	Z.ai	Qwen
Weights	Open weights	Open weights
Released	August 26, 2026	August 14, 2026
Context window	1.05M	1M
Input $/Mtok	$0.15	$0.04
Output $/Mtok	$0.50	$1.35
Coding index	71.0	73.6
Independent measurements behind it	2	2
Cost per solved task	$0.02	$1.26
Source	OpenRouter listing	OpenRouter listing

Questions

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

Pick Qwen3.8 27B for coding: it scores 73.6 on the Vibe Coding Index against 71.0 for GLM 5.3 Flash. Pick GLM 5.3 Flash when output tokens cost 2.7x less, or when a solved task costs $0.02 against $1.26.

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

GLM 5.3 Flash costs $0.15 per million input tokens and $0.50 per million output tokens; Qwen3.8 27B costs $0.04 and $1.35. Per solved SWE-bench Verified task, GLM 5.3 Flash costs $0.02 and Qwen3.8 27B $1.26.

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

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

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

80 benchmarks have a result for both models as of October 21, 2026. GLM 5.3 Flash'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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<iframe src="https://llmbenchmark.ai/embed/compare/glm-5-3-flash-vs-qwen3-8-27b" title="GLM 5.3 Flash vs Qwen3.8 27B, LLMBenchmark.ai" width="100%" height="360" style="border:0;max-width:100%" loading="lazy"></iframe>
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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