GLM-5.3 vs Qwen3.8 Max

Updated September 28, 2026. Rank says Qwen3.8 Max is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

Qwen3.8 Max has the higher public score estimate, 71.69 versus 65.44, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 17 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

GLM-5.3

Z.AI

65.44/100

Estimated · Public rank #27

90% interval 59.7–71.2

Model B

Qwen3.8 Max

Alibaba

71.69/100

Supported · Public rank #13

90% interval 68.5–74.8

Shared results: 17
GLM-5.3 only: 7
Qwen3.8 Max only: 43
Like-for-like categories: 3 / 8

Estimated: GLM-5.3 · Supported: Qwen3.8 MaxHow the comparison works

Which one for your work

Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.

Coding work
Code generation, repair, and software-engineering tasks
GLM-5.3
GLM-5.3 leads on the public coding lane, 56.7 to 55.6, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
GLM-5.3
GLM-5.3 leads on the public agentic lane, 67.3 to 64.8, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited

Show secondary and unsupported calls

Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Repository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

Pick the job you would use them for

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

56.7GLM-5.355.6Qwen3.8 Max

Like-for-like · BenchAlign v5.7

GLM-5.3 leads the like-for-like coding row, although the 90% intervals overlap.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboard Shortlist models for this job

Same basis rules as the category table below

Shape of the matched evidence

Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.

Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.

AutomationBenchAgentic
Normalized gap 20.9
GLM-5.3:48.2%
Qwen3.8 Max:27.3%
GLM-5.3 source Qwen3.8 Max source
LiveCodeBench (Vals)Coding
Normalized gap 7.4
GLM-5.3:80.5%
Qwen3.8 Max:87.9%
GLM-5.3 source Qwen3.8 Max source
MMLU-Pro (Vals)Knowledge
Normalized gap 1.8
GLM-5.3:86.8%
Qwen3.8 Max:88.6%
GLM-5.3 source Qwen3.8 Max source

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.7 lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.

Agentic

Like-for-like

GLM-5.3: 67.3; Supported · #9/111
Qwen3.8 Max: 64.8; Supported · #11/111
Basis: BenchAlign v5.7 lane · 9 vs 15 public rows
Reading: GLM-5.3 leads · intervals overlap

Coding

Like-for-like

GLM-5.3: 56.7; Supported · #22/136
Qwen3.8 Max: 55.6; Supported · #24/136
Basis: BenchAlign v5.7 lane · 13 vs 12 public rows
Reading: GLM-5.3 leads · intervals overlap

Knowledge

Like-for-like

GLM-5.3: 61.9; Supported · #33/160
Qwen3.8 Max: 66.6; Supported · #19/160
Basis: BenchAlign v5.7 lane · 2 vs 6 public rows
Reading: Qwen3.8 Max leads · intervals overlap

Reasoning

Not comparable

GLM-5.3: 77.1; #11/27
Qwen3.8 Max: 87.7; Unranked · 2 rankable rows
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Multimodal

Not comparable

GLM-5.3: Not ranked
Qwen3.8 Max: 88.4; #5/50
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GLM-5.3: Not ranked
Qwen3.8 Max: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

GLM-5.3: Not ranked
Qwen3.8 Max: 90.5; #16/124
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Math

Not comparable

GLM-5.3: Not ranked
Qwen3.8 Max: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GLM-5.3 and Qwen3.8 Max
Category	GLM-5.3	Qwen3.8 Max	Basis	Reading
Agentic	67.3Supported · #9/111	64.8Supported · #11/111	Like-for-likeBenchAlign v5.7 lane · 9 vs 15 public rows	GLM-5.3 leads · intervals overlap
Coding	56.7Supported · #22/136	55.6Supported · #24/136	Like-for-likeBenchAlign v5.7 lane · 13 vs 12 public rows	GLM-5.3 leads · intervals overlap
Knowledge	61.9Supported · #33/160	66.6Supported · #19/160	Like-for-likeBenchAlign v5.7 lane · 2 vs 6 public rows	Qwen3.8 Max leads · intervals overlap
Reasoning	77.1#11/27	87.7Unranked · 2 rankable rows	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable
Multimodal	Not ranked	88.4#5/50	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	Not ranked	90.5#16/124	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Math	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

What each workload costs

Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.

Chat turn

1K fresh input + 500 output tokens

GLM-5.3: Self-hosted; infrastructure cost varies; Fits in one request
Qwen3.8 Max: API rate not published; Fits in one request

GLM-5.3 has no comparable published API token rate. Qwen3.8 Max has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GLM-5.3: Self-hosted; infrastructure cost varies; Fits in one request
Qwen3.8 Max: API rate not published; Fits in one request

GLM-5.3 has no comparable published API token rate. Qwen3.8 Max has no comparable published API token rate.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

GLM-5.3: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable
Qwen3.8 Max: API rate not published; Fits in one request; Cached-input rate unavailable

GLM-5.3 has no comparable published API token rate. Qwen3.8 Max has no comparable published API token rate.

Cached input falls back to the list input rate only where a cached rate is unpublished

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

SpecificationGLM-5.3Qwen3.8 Max

Context window

Maximum documented context; output-token limits may be lower.

GLM-5.3

Z.AI GLM-5.3 model card

Qwen3.8 Max

Alibaba Cloud Model Studio pricing

API model ID

GLM-5.3

zai-org/GLM-5.3

Z.AI GLM-5.3 model card

Qwen3.8 Max

qwen3.8-max

Alibaba Cloud Model Studio pricing

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

GLM-5.3

No comparable hosted API rate

Z.AI GLM-5.3 model card

Qwen3.8 Max

No comparable hosted API rate

Alibaba Cloud Model Studio pricing

Documented inputs

GLM-5.3

Not sourced

Qwen3.8 Max

Not sourced

Documented outputs

GLM-5.3

Not sourced

Qwen3.8 Max

Not sourced

Provider availability

GLM-5.3

Not sourced

Qwen3.8 Max

Not sourced

Reasoning profile

GLM-5.3

Reasoning

Qwen3.8 Max

Reasoning

Weight access

GLM-5.3

Open Weight

Qwen3.8 Max

Open Weight

License

GLM-5.3

Open Weight

Qwen3.8 Max

Open Weight

Release date

GLM-5.3

2026-08-14

Qwen3.8 Max

2026-08-03

If you already use one of these models

Deployment change: The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
Quality signal: Qwen3.8 Max has the higher public score estimate, 71.69 versus 65.44, but the 90% score intervals overlap.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: Both models list 1M.

Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, GLM-5.3 or Qwen3.8 Max?

Qwen3.8 Max has the higher public score estimate, 71.69 versus 65.44, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, GLM-5.3 or Qwen3.8 Max?

GLM-5.3 leads the public coding lane, 56.7 to 55.6, with Supported evidence for both models, although the 90% intervals overlap.

Which is better for agentic tasks, GLM-5.3 or Qwen3.8 Max?

GLM-5.3 leads the public agentic tasks lane, 67.3 to 64.8, with Supported evidence for both models, although the 90% intervals overlap.

Which costs less, GLM-5.3 or Qwen3.8 Max?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, GLM-5.3 or Qwen3.8 Max?

Both models list the same context window, 1M.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence67 rows

Agentic

Terminal-Bench 2.1
GLM-5.388.2%
Source
Qwen3.8 Max86.6%
Source
GLM-5.3 leads this result
terminalBench3
GLM-5.328.3%
Source
Qwen3.8 Max—
Not directly comparable
CyberGym
GLM-5.384.5%
Source
Qwen3.8 Max—
Not directly comparable
ExploitGym
GLM-5.315.0%
Source
Qwen3.8 Max—
Not directly comparable
Toolathlon-Verified
GLM-5.373.0%
Source
Qwen3.8 Max72.5%
Source
GLM-5.3 leads this result
AutomationBench
GLM-5.348.2%
Source
Qwen3.8 Max27.3%
Source
GLM-5.3 leads this result
Agents' Last Exam
GLM-5.328.5%
Source
Qwen3.8 Max52.4%
Source
Qwen3.8 Max leads this result
HLE w/ tools
GLM-5.362.5%
Source
Qwen3.8 Max56.2%
Source
GLM-5.3 leads this result
Terminal-Bench 2.1 (Vals)
GLM-5.371.5%
Source
Qwen3.8 Max67.4%
Source
GLM-5.3 leads this result
CoWorkBench
GLM-5.3—
Qwen3.8 Max74.8%
Source
Not directly comparable
JobBench
GLM-5.3—
Qwen3.8 Max53.4%
Source
Not directly comparable
skillsBench
GLM-5.3—
Qwen3.8 Max70.2%
Source
Not directly comparable
WideResearch
GLM-5.3—
Qwen3.8 Max81.9%
Source
Not directly comparable
OSWorld-Verified
GLM-5.3—
Qwen3.8 Max86.1%
Source
Not directly comparable
OSWorld 2.0
GLM-5.3—
Qwen3.8 Max19.4%
Source
Not directly comparable
WebArena-Verified
GLM-5.3—
Qwen3.8 Max66.8%
Source
Not directly comparable
AndroidWorld
GLM-5.3—
Qwen3.8 Max85.3%
Source
Not directly comparable
MobileWorld
GLM-5.3—
Qwen3.8 Max77.8%
Source
Not directly comparable

Coding

Terminal-Bench 2.1
GLM-5.388.2%
Source
Qwen3.8 Max86.6%
Source
GLM-5.3 leads this result
terminalBench3
GLM-5.328.3%
Source
Qwen3.8 Max—
Not directly comparable
DeepSWE
GLM-5.366.9%
Source
Qwen3.8 Max56.6%
Source
GLM-5.3 leads this result
NL2Repo
GLM-5.358%
Source
Qwen3.8 Max55.9%
Source
GLM-5.3 leads this result
ProgramBench
GLM-5.319.0%
Source
Qwen3.8 Max—
Not directly comparable
FrontierSWE
GLM-5.378.1%
Source
Qwen3.8 Max73.5%
Source
GLM-5.3 leads this result
sweMarathon
GLM-5.342.5%
Source
Qwen3.8 Max—
Not directly comparable
PostTrain Bench
GLM-5.339.8%
Source
Qwen3.8 Max—
Not directly comparable
VulcanBench v3
Shared source
GLM-5.378.3%
Qwen3.8 Max81.2%
Qwen3.8 Max leads this result
OpenHarmony Bench
Shared source
GLM-5.360.8%
Qwen3.8 Max60.8%
Tie
FrontierSWE v2
Shared source
GLM-5.330.2%
Qwen3.8 Max15.8%
GLM-5.3 leads this result
LiveCodeBench (Vals)
GLM-5.380.5%
Source
Qwen3.8 Max87.9%
Source
Qwen3.8 Max leads this result
SWE-bench (Vals)
GLM-5.395.4%
Source
Qwen3.8 Max85.6%
Source
GLM-5.3 leads this result
SWE-bench Pro
GLM-5.3—
Qwen3.8 Max67.7%
Source
Not directly comparable
MLS-Bench Lite
GLM-5.3—
Qwen3.8 Max41.0%
Source
Not directly comparable
PaperBench
GLM-5.3—
Qwen3.8 Max93.0%
Source
Not directly comparable

Reasoning

MRCRv2
GLM-5.3—
Qwen3.8 Max92.9%
Source
Not directly comparable
LongBench v2
GLM-5.3—
Qwen3.8 Max66.3%
Source
Not directly comparable

Multimodal

MMMU-Pro
GLM-5.3—
Qwen3.8 Max82.3%
Source
Not directly comparable
MathVision
GLM-5.3—
Qwen3.8 Max95.2%
Source
Not directly comparable
MathVision w/ Python
GLM-5.3—
Qwen3.8 Max97.7%
Source
Not directly comparable
BabyVision
GLM-5.3—
Qwen3.8 Max82.0%
Source
Not directly comparable
BabyVision w/ Python
GLM-5.3—
Qwen3.8 Max91.3%
Source
Not directly comparable
ZeroBench
GLM-5.3—
Qwen3.8 Max24.0%
Source
Not directly comparable
ZeroBench w/ Python
GLM-5.3—
Qwen3.8 Max49.0%
Source
Not directly comparable
MedXpertQA (MM)
GLM-5.3—
Qwen3.8 Max80.4%
Source
Not directly comparable
ScreenSpot Pro
GLM-5.3—
Qwen3.8 Max84.5%
Source
Not directly comparable
Vision2Web
GLM-5.3—
Qwen3.8 Max69.0%
Source
Not directly comparable
CharXiv w/o tools
GLM-5.3—
Qwen3.8 Max88.4%
Source
Not directly comparable
CharXiv
GLM-5.3—
Qwen3.8 Max93.5%
Source
Not directly comparable
OmniDocBench 1.5
GLM-5.3—
Qwen3.8 Max92.1%
Source
Not directly comparable
OCRBench V2
GLM-5.3—
Qwen3.8 Max74.2%
Source
Not directly comparable
CC-OCR
GLM-5.3—
Qwen3.8 Max79.6%
Source
Not directly comparable
RealWorldQA
GLM-5.3—
Qwen3.8 Max88.0%
Source
Not directly comparable
ERQA
GLM-5.3—
Qwen3.8 Max77.8%
Source
Not directly comparable
SimpleVQA
GLM-5.3—
Qwen3.8 Max75.0%
Source
Not directly comparable
PerceptionBench
GLM-5.3—
Qwen3.8 Max63.5%
Source
Not directly comparable
Video-MME (with subtitle)
GLM-5.3—
Qwen3.8 Max90.4%
Source
Not directly comparable
VideoMMMU
GLM-5.3—
Qwen3.8 Max88.7%
Source
Not directly comparable
MMVU
GLM-5.3—
Qwen3.8 Max82.4%
Source
Not directly comparable
MLVU (M-Avg)
GLM-5.3—
Qwen3.8 Max90.8%
Source
Not directly comparable
LVBench
GLM-5.3—
Qwen3.8 Max81.8%
Source
Not directly comparable

Knowledge

GPQA Diamond (Vals)
GLM-5.388.1%
Source
Qwen3.8 Max93.7%
Source
Qwen3.8 Max leads this result
MMLU-Pro (Vals)
GLM-5.386.8%
Source
Qwen3.8 Max88.6%
Source
Qwen3.8 Max leads this result
GPQA
GLM-5.3—
Qwen3.8 Max92.6%
Source
Not directly comparable
GPQA-D
GLM-5.3—
Qwen3.8 Max92.6%
Source
Not directly comparable
HLE
GLM-5.3—
Qwen3.8 Max43.6%
Source
Not directly comparable
HLE w/o tools
GLM-5.3—
Qwen3.8 Max43.6%
Source
Not directly comparable

Instruction following

IFBench
GLM-5.3—
Qwen3.8 Max82.8%
Source
Not directly comparable

67 public results · 17 shared

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Last updated September 28, 2026

GLM-5.3 vs Qwen3.8 Max

Decision reading

Which one for your work

Coding work

Agentic work

Long documents

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Agentic

Coding

Knowledge

Reasoning

Multimodal

Multilingual

Instruction following

Math

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

If you already use one of these models

Questions

Which is better, GLM-5.3 or Qwen3.8 Max?

Which is better for coding, GLM-5.3 or Qwen3.8 Max?

Which is better for agentic tasks, GLM-5.3 or Qwen3.8 Max?

Which costs less, GLM-5.3 or Qwen3.8 Max?

Which has the larger context window, GLM-5.3 or Qwen3.8 Max?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Instruction following

Related comparisons

Watch GLM-5.3 vs Qwen3.8 Max