Model A

Z.AI

68.19/100

Supported · Public rank #28

90% interval 61.7–74.7

GLM-5.2 vs Qwen3.7 Max

Updated September 14, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B

Qwen3.7 Max

Alibaba

67.16/100

Supported · Public rank #32

90% interval 59.0–75.3

Decision reading

GLM-5.2 has the higher public score estimate, 68.19 versus 67.16, 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.

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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-5.2
GLM-5.2 leads on the public coding lane, 61 to 49.4, 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-5.2
GLM-5.2 leads on the public agentic lane, 58.5 to 41.3, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger

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: 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.

8 GLM-5.2 only

17 Shared

24 Qwen3.7 Max only

Shared results: 17
GLM-5.2 only: 8
Qwen3.7 Max only: 24
Like-for-like categories: 3 / 8

1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign 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-5.2: 58.5; Supported · #29/153
Qwen3.7 Max: 41.3; Supported · #112/153
Basis: BenchAlign lane · 6 vs 10 public rows
Reading: GLM-5.2 leads

Coding

Like-for-like

GLM-5.2: 61.0; Supported · #19/152
Qwen3.7 Max: 49.4; Supported · #65/152
Basis: BenchAlign lane · 8 vs 10 public rows
Reading: GLM-5.2 leads · intervals overlap

Knowledge

Like-for-like

GLM-5.2: 60.7; Supported · #35/183
Qwen3.7 Max: 61.8; Supported · #28/183
Basis: BenchAlign lane · 6 vs 9 public rows
Reading: Qwen3.7 Max leads · intervals overlap

Instruction following

Directional only

GLM-5.2: 89.8; #22/123
Qwen3.7 Max: 91.1; #16/123
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Directional only

Reasoning

Not comparable

GLM-5.2: 74.8; Unranked · 2 rankable rows
Qwen3.7 Max: 75.1; Unranked · 3 rankable rows
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Math

Not comparable

GLM-5.2: 80.7; Unranked · 4 rankable rows
Qwen3.7 Max: 82.1; Unranked · 3 rankable rows
Basis: Provisional lane · 2 vs 1 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GLM-5.2: Not ranked
Qwen3.7 Max: 100.0; #1/12
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Multimodal

Not comparable

GLM-5.2: Not ranked
Qwen3.7 Max: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GLM-5.2 and Qwen3.7 Max
Category	GLM-5.2	Qwen3.7 Max	Basis	Reading
Agentic	58.5Supported · #29/153	41.3Supported · #112/153	Like-for-likeBenchAlign lane · 6 vs 10 public rows	GLM-5.2 leads
Coding	61.0Supported · #19/152	49.4Supported · #65/152	Like-for-likeBenchAlign lane · 8 vs 10 public rows	GLM-5.2 leads · intervals overlap
Knowledge	60.7Supported · #35/183	61.8Supported · #28/183	Like-for-likeBenchAlign lane · 6 vs 9 public rows	Qwen3.7 Max leads · intervals overlap
Instruction following	89.8#22/123	91.1#16/123	Directional onlyProvisional lane · 0 vs 1 weighted rows	Directional only
Reasoning	74.8Unranked · 2 rankable rows	75.1Unranked · 3 rankable rows	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Math	80.7Unranked · 4 rankable rows	82.1Unranked · 3 rankable rows	Not comparableProvisional lane · 2 vs 1 weighted rows	Not comparable
Multilingual	Not ranked	100.0#1/12	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Multimodal	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) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

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.

LiveCodeBench (Vals)
Coding
GLM-5.2: 69.5%Qwen3.7 Max: 87.1%Normalized gap 17.6GLM-5.2 source Qwen3.7 Max source
HLE
Knowledge
GLM-5.2: 54.7%Qwen3.7 Max: 41.4%Normalized gap 13.3GLM-5.2 source Qwen3.7 Max source
Terminal-Bench 2.0
Agentic
GLM-5.2: 81%Qwen3.7 Max: 69.7%Normalized gap 11.3GLM-5.2 source Qwen3.7 Max source
HMMT Feb 2026
Math
GLM-5.2: 92.5%Qwen3.7 Max: 97.1%Normalized gap 4.6GLM-5.2 source Qwen3.7 Max source
MMLU-Pro (Vals)
Knowledge
GLM-5.2: 86.7%Qwen3.7 Max: 89.3%Normalized gap 2.6GLM-5.2 source Qwen3.7 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
Qwen3.7 Max: API rate not published; Fits in one request

Qwen3.7 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
Qwen3.7 Max: API rate not published; Fits in one request

Qwen3.7 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
Qwen3.7 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. Qwen3.7 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-5.2Qwen3.7 Max

Context window

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

GLM-5.2

Qwen3.7 Max

API model ID

GLM-5.2

Not sourced

Qwen3.7 Max

Not sourced

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

Qwen3.7 Max

No comparable hosted API rate

Documented inputs

GLM-5.2

Not sourced

Qwen3.7 Max

Not sourced

Documented outputs

GLM-5.2

Not sourced

Qwen3.7 Max

Not sourced

Provider availability

GLM-5.2

Not sourced

Qwen3.7 Max

Not sourced

Reasoning profile

GLM-5.2

Reasoning

Qwen3.7 Max

Reasoning

Weight access

GLM-5.2

Open Weight

Qwen3.7 Max

Proprietary

License

GLM-5.2

Open Weight

Qwen3.7 Max

Proprietary

Release date

GLM-5.2

2026-06-16

Qwen3.7 Max

2026-05-16

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: GLM-5.2 has the higher public score estimate, 68.19 versus 67.16, 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 evidence49 rows

Agentic

Terminal-Bench 3.0
GLM-5.24.6%
Source
Qwen3.7 Max—
Not directly comparable
Terminal-Bench 2.0
GLM-5.281%
Source
Qwen3.7 Max69.7%
Source
GLM-5.2 leads this result
MCP Atlas
GLM-5.276.8%
Source
Qwen3.7 Max76.4%
Source
GLM-5.2 leads this result
Toolathlon
GLM-5.248.2%
Source
Qwen3.7 Max—
Not directly comparable
ResearchClawBench
Shared source
GLM-5.220.7%
Qwen3.7 Max18.7%
GLM-5.2 leads this result
Terminal-Bench 2.1 (Vals)
GLM-5.267.8%
Source
Qwen3.7 Max61.0%
Source
GLM-5.2 leads this result
QwenClawBench
GLM-5.2—
Qwen3.7 Max64.3%
Source
Not directly comparable
Claw-Eval
GLM-5.2—
Qwen3.7 Max65.2%
Source
Not directly comparable
BFCL v4
GLM-5.2—
Qwen3.7 Max75.0%
Source
Not directly comparable
VITA-Bench
GLM-5.2—
Qwen3.7 Max47.9%
Source
Not directly comparable
HLE w/ tools
GLM-5.2—
Qwen3.7 Max53.5%
Source
Not directly comparable
Gert Labs
GLM-5.2—
Qwen3.7 Max64.27%
Source
Not directly comparable

Coding

SWE-bench Pro
GLM-5.262.1%
Source
Qwen3.7 Max60.6%
Source
GLM-5.2 leads this result
NL2Repo
GLM-5.248.9%
Source
Qwen3.7 Max47.2%
Source
GLM-5.2 leads this result
Terminal-Bench 2.0
GLM-5.281.0%
Source
Qwen3.7 Max69.7%
Source
GLM-5.2 leads this result
ProgramBench
GLM-5.263.7%
Source
Qwen3.7 Max—
Not directly comparable
cursorBench32
GLM-5.255.0%
Source
Qwen3.7 Max—
Not directly comparable
OpenHarmony Bench
Shared source
GLM-5.258.4%
Qwen3.7 Max53.4%
GLM-5.2 leads this result
LiveCodeBench (Vals)
GLM-5.269.5%
Source
Qwen3.7 Max87.1%
Source
Qwen3.7 Max leads this result
SWE-bench (Vals)
GLM-5.282.8%
Source
Qwen3.7 Max68.8%
Source
GLM-5.2 leads this result
SWE-bench Verified
GLM-5.2—
Qwen3.7 Max80.4%
Source
Not directly comparable
SWE Multilingual
GLM-5.2—
Qwen3.7 Max78.3%
Source
Not directly comparable
SciCode
GLM-5.2—
Qwen3.7 Max53.5%
Source
Not directly comparable
LiveCodeBench
GLM-5.2—
Qwen3.7 Max91.6%
Source
Not directly comparable

Reasoning

CritPt
GLM-5.220.9%
Source
Qwen3.7 Max13.4%
Source
GLM-5.2 leads this result
MRCRv2
GLM-5.2—
Qwen3.7 Max90.4%
Source
Not directly comparable

Knowledge

GPQA
GLM-5.291.2%
Source
Qwen3.7 Max92.4%
Source
Qwen3.7 Max leads this result
GPQA-D
GLM-5.291.2%
Source
Qwen3.7 Max92.4%
Source
Qwen3.7 Max leads this result
HLE
GLM-5.254.7%
Source
Qwen3.7 Max41.4%
Source
GLM-5.2 leads this result
HLE w/o tools
GLM-5.240.5%
Source
Qwen3.7 Max—
Not directly comparable
GPQA Diamond (Vals)
GLM-5.285.6%
Source
Qwen3.7 Max90.2%
Source
Qwen3.7 Max leads this result
MMLU-Pro (Vals)
GLM-5.286.7%
Source
Qwen3.7 Max89.3%
Source
Qwen3.7 Max leads this result
MMLU-Pro
GLM-5.2—
Qwen3.7 Max89.6%
Source
Not directly comparable
MMLU-Redux
GLM-5.2—
Qwen3.7 Max95%
Source
Not directly comparable
SuperGPQA
GLM-5.2—
Qwen3.7 Max73.6%
Source
Not directly comparable
MMMLU
GLM-5.2—
Qwen3.7 Max90.3%
Source
Not directly comparable

Math

AIME26
GLM-5.299.2%
Source
Qwen3.7 Max—
Not directly comparable
HMMT Nov 2025
GLM-5.294.4%
Source
Qwen3.7 Max—
Not directly comparable
HMMT Feb 2026
GLM-5.292.5%
Source
Qwen3.7 Max97.1%
Source
Qwen3.7 Max leads this result
MMAnswerBench
GLM-5.291.0%
Source
Qwen3.7 Max—
Not directly comparable
IMOAnswerBench
GLM-5.2—
Qwen3.7 Max90.0%
Source
Not directly comparable
Apex
GLM-5.2—
Qwen3.7 Max44.5%
Source
Not directly comparable

Multilingual

MMLU-ProX
GLM-5.2—
Qwen3.7 Max87%
Source
Not directly comparable
NOVA-63
GLM-5.2—
Qwen3.7 Max59.0%
Source
Not directly comparable
INCLUDE
GLM-5.2—
Qwen3.7 Max86.2%
Source
Not directly comparable
MAXIFE
GLM-5.2—
Qwen3.7 Max89.2%
Source
Not directly comparable
PolyMath
GLM-5.2—
Qwen3.7 Max86.5%
Source
Not directly comparable

Instruction following

IFEval
GLM-5.2—
Qwen3.7 Max94.3%
Source
Not directly comparable
IFBench
GLM-5.2—
Qwen3.7 Max79.1%
Source
Not directly comparable

Frequently asked questions

Which is better, GLM-5.2 or Qwen3.7 Max?

GLM-5.2 has the higher public score estimate, 68.19 versus 67.16, 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 Qwen3.7 Max?

GLM-5.2 leads the public coding lane, 61 to 49.4, with Supported evidence for both models, although the 90% intervals overlap.

Which is better for agentic tasks, GLM-5.2 or Qwen3.7 Max?

GLM-5.2 leads the public agentic tasks lane, 58.5 to 41.3, with Supported evidence for both models and non-overlapping 90% intervals.

Which costs less, GLM-5.2 or Qwen3.7 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 Qwen3.7 Max?

Both models list the same context window, 1M.

Related comparisons

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Last updated September 14, 2026

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GLM-5.2 vs Qwen3.7 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

Agentic

Coding

Knowledge

Instruction following

Reasoning

Math

Multilingual

Multimodal

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

Multilingual

Instruction following

Frequently asked questions

Which is better, GLM-5.2 or Qwen3.7 Max?

Which is better for coding, GLM-5.2 or Qwen3.7 Max?

Which is better for agentic tasks, GLM-5.2 or Qwen3.7 Max?

Which costs less, GLM-5.2 or Qwen3.7 Max?

Which has the larger context window, GLM-5.2 or Qwen3.7 Max?

Related comparisons

Watch GLM-5.2 vs Qwen3.7 Max