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Model comparison

GLM-5.2 vs Qwen3.7 Max

Updated July 31, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

21 confirmed releases in the last 30 daystrack changes
GLM-5.2

Z.AI

62.9/100

Estimated · Public rank #41

90% interval 47.7–78.2

Qwen3.7 Max

Alibaba

71.8/100

Supported · Public rank #13

90% interval 65.3–78.3

Qwen3.7 Max has the higher public score estimate, 71.78 versus 62.94, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

11 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.

  • Agentic work

    Tool use, computer use, and multi-step task completion

    GLM-5.2

    GLM-5.2 leads on the same 1 weighted benchmark row.

    Confidence: limited

Show secondary and unsupported calls

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.

    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.

Shared results
11
GLM-5.2 only
7
Qwen3.7 Max only
24
Like-for-like categories
1 / 8

3 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

Agentic

Like-for-like
GLM-5.2
81.0
Qwen3.7 Max
69.7
Weighted basis
1 vs 1 rows
Reading
GLM-5.2 leads

Coding

Directional only
GLM-5.2
62.1
Qwen3.7 Max
77.9
Weighted basis
1 vs 4 rows
Reading
Directional only

Knowledge

Directional only
GLM-5.2
59.6
Qwen3.7 Max
64.2
Weighted basis
2 vs 4 rows
Reading
Directional only

Math

Directional only
GLM-5.2
95.9
Qwen3.7 Max
97.1
Weighted basis
2 vs 1 rows
Reading
Directional only

Reasoning

Not comparable
GLM-5.2
Not measured
Qwen3.7 Max
90.4
Weighted basis
0 vs 1 rows
Reading
Not comparable

Multilingual

Not comparable
GLM-5.2
Not measured
Qwen3.7 Max
87.0
Weighted basis
0 vs 1 rows
Reading
Not comparable

Multimodal

Not comparable
GLM-5.2
Not measured
Qwen3.7 Max
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
GLM-5.2
Not measured
Qwen3.7 Max
84.4
Weighted basis
0 vs 2 rows
Reading
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.

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.

Context window

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

GLM-5.2

1M

Qwen3.7 Max

1M

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
Qwen3.7 Max has the higher public score estimate, 71.78 versus 62.94, 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 evidence42 rows

Agentic

  • 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

  • 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

  • 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

  • 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?

Qwen3.7 Max has the higher public score estimate, 71.78 versus 62.94, 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?

The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.

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

GLM-5.2 leads the like-for-like agentic tasks comparison across 1 shared weighted benchmark row.

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

Compare API pricing Read the methodology Open the model selector

Last updated July 31, 2026

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