Model comparison

GLM-5.2 vs Qwen3.8 Max

Updated August 3, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

GLM-5.2

Z.AI

62.9/100

Estimated · Public rank #42

90% interval 47.7–78.2

Qwen3.8 Max

Alibaba

65.4/100

Estimated · Public rank #31

90% interval 55.5–75.3

Qwen3.8 Max has the higher public score estimate, 65.4 versus 62.93, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

6 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.

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
Qwen3.8 Max
Qwen3.8 Max leads on the same 1 weighted benchmark row.
Confidence: limited

Show secondary and unsupported calls

Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
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: rate-fallback
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

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

12 GLM-5.2 only

6 Shared

46 Qwen3.8 Max only

Shared results: 6
GLM-5.2 only: 12
Qwen3.8 Max only: 46
Like-for-like categories: 2 / 8

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Coding

Like-for-like

GLM-5.2: 62.1
Qwen3.8 Max: 67.7
Weighted basis: 1 vs 1 rows
Reading: Qwen3.8 Max leads

Knowledge

Like-for-like

GLM-5.2: 59.6
Qwen3.8 Max: 50.2
Weighted basis: 2 vs 2 rows
Reading: GLM-5.2 leads

Agentic

Not comparable

GLM-5.2: 81.0
Qwen3.8 Max: 86.1
Weighted basis: 1 vs 1 rows
Reading: Not comparable

Reasoning

Not comparable

GLM-5.2: Not measured
Qwen3.8 Max: 78.3
Weighted basis: 0 vs 2 rows
Reading: Not comparable

Math

Not comparable

GLM-5.2: 95.9
Qwen3.8 Max: Not measured
Weighted basis: 2 vs 0 rows
Reading: Not comparable

Multilingual

Not comparable

GLM-5.2: Not measured
Qwen3.8 Max: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Multimodal

Not comparable

GLM-5.2: Not measured
Qwen3.8 Max: 86.3
Weighted basis: 0 vs 2 rows
Reading: Not comparable

Instruction following

Not comparable

GLM-5.2: Not measured
Qwen3.8 Max: 82.8
Weighted basis: 0 vs 1 rows
Reading: Not comparable

Category averages with the server-provided evidence basis for GLM-5.2 and Qwen3.8 Max
Category	GLM-5.2	Qwen3.8 Max	Weighted basis	Reading
Coding	62.1	67.7	Like-for-like1 vs 1 rows	Qwen3.8 Max leads
Knowledge	59.6	50.2	Like-for-like2 vs 2 rows	GLM-5.2 leads
Agentic	81.0	86.1	Not comparable1 vs 1 rows	Not comparable
Reasoning	Not measured	78.3	Not comparable0 vs 2 rows	Not comparable
Math	95.9	Not measured	Not comparable2 vs 0 rows	Not comparable
Multilingual	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable
Multimodal	Not measured	86.3	Not comparable0 vs 2 rows	Not comparable
Instruction following	Not measured	82.8	Not comparable0 vs 1 rows	Not comparable

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.

HLE
Knowledge
GLM-5.2: 54.7%Qwen3.8 Max: 43.6%Normalized gap 11.1GLM-5.2 source Qwen3.8 Max source
SWE-bench Pro
Coding
GLM-5.2: 62.1%Qwen3.8 Max: 67.7%Normalized gap 5.6GLM-5.2 source Qwen3.8 Max source
GPQA
Knowledge
GLM-5.2: 91.2%Qwen3.8 Max: 92.6%Normalized gap 1.4GLM-5.2 source Qwen3.8 Max source

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-5.2: $0.0036; Fits in one request
Qwen3.8 Max: API rate not published; Fits in one request

Qwen3.8 Max has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GLM-5.2: $0.0832; Fits in one request
Qwen3.8 Max: API rate not published; Fits in one request

Qwen3.8 Max has no comparable published API token rate.

Cache-heavy agent loop

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

GLM-5.2: $0.352; Fits in one request; Cached input priced at the published list-input rate
Qwen3.8 Max: API rate not published; Fits in one request; Cached-input rate unavailable

GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.8 Max has no comparable published API token rate.

Specification differences

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

SpecificationGLM-5.2Qwen3.8 Max

Context window

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

GLM-5.2

Qwen3.8 Max

Alibaba Cloud Model Studio pricing

API model ID

GLM-5.2

Not sourced

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-5.2

Not published

Qwen3.8 Max

No comparable hosted API rate

Alibaba Cloud Model Studio pricing

Documented inputs

GLM-5.2

Not sourced

Qwen3.8 Max

Not sourced

Documented outputs

GLM-5.2

Not sourced

Qwen3.8 Max

Not sourced

Provider availability

GLM-5.2

Not sourced

Qwen3.8 Max

Not sourced

Reasoning profile

GLM-5.2

Reasoning

Qwen3.8 Max

Reasoning

Weight access

GLM-5.2

Open Weight

Qwen3.8 Max

Proprietary

License

GLM-5.2

Open Weight

Qwen3.8 Max

Proprietary

Release date

GLM-5.2

2026-06-16

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, 65.4 versus 62.93, 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.

Benchmark evidence

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

Browse raw public benchmark evidence64 rows

Agentic

Terminal-Bench 2.0
GLM-5.281%
Source
Qwen3.8 Max—
Not directly comparable
MCP Atlas
GLM-5.276.8%
Source
Qwen3.8 Max—
Not directly comparable
Toolathlon
GLM-5.248.2%
Source
Qwen3.8 Max—
Not directly comparable
ResearchClawBench
GLM-5.220.7%
Source
Qwen3.8 Max—
Not directly comparable
Terminal-Bench 2.1
GLM-5.2—
Qwen3.8 Max86.6%
Source
Not directly comparable
CoWorkBench
GLM-5.2—
Qwen3.8 Max74.8%
Source
Not directly comparable
JobBench
GLM-5.2—
Qwen3.8 Max53.4%
Source
Not directly comparable
skillsBench
GLM-5.2—
Qwen3.8 Max70.2%
Source
Not directly comparable
Agents' Last Exam
GLM-5.2—
Qwen3.8 Max52.4%
Source
Not directly comparable
AutomationBench
GLM-5.2—
Qwen3.8 Max27.3%
Source
Not directly comparable
Toolathlon-Verified
GLM-5.2—
Qwen3.8 Max72.5%
Source
Not directly comparable
WideResearch
GLM-5.2—
Qwen3.8 Max81.9%
Source
Not directly comparable
HLE w/ tools
GLM-5.2—
Qwen3.8 Max56.2%
Source
Not directly comparable
OSWorld-Verified
GLM-5.2—
Qwen3.8 Max86.1%
Source
Not directly comparable
OSWorld 2.0
GLM-5.2—
Qwen3.8 Max19.4%
Source
Not directly comparable
WebArena-Verified
GLM-5.2—
Qwen3.8 Max66.8%
Source
Not directly comparable
AndroidWorld
GLM-5.2—
Qwen3.8 Max85.3%
Source
Not directly comparable
MobileWorld
GLM-5.2—
Qwen3.8 Max77.8%
Source
Not directly comparable

Coding

SWE-bench Pro
GLM-5.262.1%
Source
Qwen3.8 Max67.7%
Source
Qwen3.8 Max leads this result
NL2Repo
GLM-5.248.9%
Source
Qwen3.8 Max55.9%
Source
Qwen3.8 Max leads this result
Terminal-Bench 2.0
GLM-5.281.0%
Source
Qwen3.8 Max—
Not directly comparable
ProgramBench
GLM-5.263.7%
Source
Qwen3.8 Max—
Not directly comparable
cursorBench32
GLM-5.255.0%
Source
Qwen3.8 Max—
Not directly comparable
Terminal-Bench 2.1
GLM-5.2—
Qwen3.8 Max86.6%
Source
Not directly comparable
deepSwe
GLM-5.2—
Qwen3.8 Max56.6%
Source
Not directly comparable
FrontierSWE
GLM-5.2—
Qwen3.8 Max73.5%
Source
Not directly comparable
MLS-Bench Lite
GLM-5.2—
Qwen3.8 Max41.0%
Source
Not directly comparable
PaperBench
GLM-5.2—
Qwen3.8 Max93.0%
Source
Not directly comparable

Reasoning

CritPt
GLM-5.220.9%
Source
Qwen3.8 Max—
Not directly comparable
MRCRv2
GLM-5.2—
Qwen3.8 Max92.9%
Source
Not directly comparable
LongBench v2
GLM-5.2—
Qwen3.8 Max66.3%
Source
Not directly comparable

Knowledge

GPQA
GLM-5.291.2%
Source
Qwen3.8 Max92.6%
Source
Qwen3.8 Max leads this result
GPQA-D
GLM-5.291.2%
Source
Qwen3.8 Max92.6%
Source
Qwen3.8 Max leads this result
HLE
GLM-5.254.7%
Source
Qwen3.8 Max43.6%
Source
GLM-5.2 leads this result
HLE w/o tools
GLM-5.240.5%
Source
Qwen3.8 Max43.6%
Source
Qwen3.8 Max leads this result

Math

AIME26
GLM-5.299.2%
Source
Qwen3.8 Max—
Not directly comparable
HMMT Nov 2025
GLM-5.294.4%
Source
Qwen3.8 Max—
Not directly comparable
HMMT Feb 2026
GLM-5.292.5%
Source
Qwen3.8 Max—
Not directly comparable
MMAnswerBench
GLM-5.291.0%
Source
Qwen3.8 Max—
Not directly comparable

Multimodal

MMMU-Pro
GLM-5.2—
Qwen3.8 Max82.3%
Source
Not directly comparable
MathVision
GLM-5.2—
Qwen3.8 Max95.2%
Source
Not directly comparable
MathVision w/ Python
GLM-5.2—
Qwen3.8 Max97.7%
Source
Not directly comparable
BabyVision
GLM-5.2—
Qwen3.8 Max82.0%
Source
Not directly comparable
BabyVision w/ Python
GLM-5.2—
Qwen3.8 Max91.3%
Source
Not directly comparable
ZeroBench
GLM-5.2—
Qwen3.8 Max24.0%
Source
Not directly comparable
ZeroBench w/ Python
GLM-5.2—
Qwen3.8 Max49.0%
Source
Not directly comparable
MedXpertQA (MM)
GLM-5.2—
Qwen3.8 Max80.4%
Source
Not directly comparable
ScreenSpot Pro
GLM-5.2—
Qwen3.8 Max84.5%
Source
Not directly comparable
Vision2Web
GLM-5.2—
Qwen3.8 Max69.0%
Source
Not directly comparable
CharXiv w/o tools
GLM-5.2—
Qwen3.8 Max88.4%
Source
Not directly comparable
CharXiv
GLM-5.2—
Qwen3.8 Max93.5%
Source
Not directly comparable
OmniDocBench 1.5
GLM-5.2—
Qwen3.8 Max92.1%
Source
Not directly comparable
OCRBench V2
GLM-5.2—
Qwen3.8 Max74.2%
Source
Not directly comparable
CC-OCR
GLM-5.2—
Qwen3.8 Max79.6%
Source
Not directly comparable
RealWorldQA
GLM-5.2—
Qwen3.8 Max88.0%
Source
Not directly comparable
ERQA
GLM-5.2—
Qwen3.8 Max77.8%
Source
Not directly comparable
SimpleVQA
GLM-5.2—
Qwen3.8 Max75.0%
Source
Not directly comparable
PerceptionBench
GLM-5.2—
Qwen3.8 Max63.5%
Source
Not directly comparable
Video-MME (with subtitle)
GLM-5.2—
Qwen3.8 Max90.4%
Source
Not directly comparable
VideoMMMU
GLM-5.2—
Qwen3.8 Max88.7%
Source
Not directly comparable
MMVU
GLM-5.2—
Qwen3.8 Max82.4%
Source
Not directly comparable
MLVU (M-Avg)
GLM-5.2—
Qwen3.8 Max90.8%
Source
Not directly comparable
LVBench
GLM-5.2—
Qwen3.8 Max81.8%
Source
Not directly comparable

Instruction following

IFBench
GLM-5.2—
Qwen3.8 Max82.8%
Source
Not directly comparable

Frequently asked questions

Which is better, GLM-5.2 or Qwen3.8 Max?

Qwen3.8 Max has the higher public score estimate, 65.4 versus 62.93, 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-5.2 or Qwen3.8 Max?

Qwen3.8 Max leads the like-for-like coding comparison across 1 shared weighted benchmark row.

Which is better for agentic tasks, GLM-5.2 or Qwen3.8 Max?

The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.

Which costs less, GLM-5.2 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-5.2 or Qwen3.8 Max?

Both models list the same context window, 1M.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 3, 2026

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GLM-5.2 vs Qwen3.8 Max

Which one for your work

Coding work

Agentic work

Long documents

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

What is actually comparable

Category results, on a stated basis

Coding

Knowledge

Agentic

Reasoning

Math

Multilingual

Multimodal

Instruction following

Shape of the matched evidence

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

Benchmark evidence

Agentic

Coding

Reasoning

Knowledge

Math

Multimodal

Instruction following

Frequently asked questions

Which is better, GLM-5.2 or Qwen3.8 Max?

Which is better for coding, GLM-5.2 or Qwen3.8 Max?

Which is better for agentic tasks, GLM-5.2 or Qwen3.8 Max?

Which costs less, GLM-5.2 or Qwen3.8 Max?

Which has the larger context window, GLM-5.2 or Qwen3.8 Max?

Related comparisons

Watch GLM-5.2 vs Qwen3.8 Max