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Model comparison

DeepSeek V4 Pro vs GLM-5.2

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
DeepSeek V4 Pro

DeepSeek

60.0/100

Supported · Public rank #50

90% interval 41.6–78.3

GLM-5.2

Z.AI

62.9/100

Estimated · Public rank #41

90% interval 47.7–78.2

GLM-5.2 has the higher public score estimate, 62.94 versus 59.95, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

10 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

  • Chat turn cost

    1K fresh input + 500 output tokens

    DeepSeek V4 Pro

    DeepSeek V4 Pro has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

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

    DeepSeek V4 Pro

    DeepSeek V4 Pro has the lower estimated token cost for this stated workload. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

Show secondary and unsupported calls

  • Repository review cost

    50K fresh input + 3K output tokens

    DeepSeek V4 Pro

    DeepSeek V4 Pro has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • 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

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
10
DeepSeek V4 Pro only
13
GLM-5.2 only
8
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
DeepSeek V4 Pro
59.1
GLM-5.2
81.0
Weighted basis
1 vs 1 rows
Reading
GLM-5.2 leads

Coding

Directional only
DeepSeek V4 Pro
65.3
GLM-5.2
62.1
Weighted basis
2 vs 1 rows
Reading
Directional only

Knowledge

Directional only
DeepSeek V4 Pro
41.3
GLM-5.2
59.6
Weighted basis
4 vs 2 rows
Reading
Directional only

Math

Directional only
DeepSeek V4 Pro
31.7
GLM-5.2
95.9
Weighted basis
1 vs 2 rows
Reading
Directional only

Reasoning

Not comparable
DeepSeek V4 Pro
Not measured
GLM-5.2
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
DeepSeek V4 Pro
Not measured
GLM-5.2
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
DeepSeek V4 Pro
Not measured
GLM-5.2
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
DeepSeek V4 Pro
Not measured
GLM-5.2
Not measured
Weighted basis
0 vs 0 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

DeepSeek V4 Pro
$0.00087
Fits in one request
GLM-5.2
$0.0036
Fits in one request

DeepSeek V4 Pro has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

DeepSeek V4 Pro
$0.02436
Fits in one request
GLM-5.2
$0.0832
Fits in one request

DeepSeek V4 Pro has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

DeepSeek V4 Pro
$0.01812
Fits in one request
GLM-5.2
$0.352
Fits in one request
Cached input priced at the published list-input rate

DeepSeek V4 Pro has the lower modeled cost

GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate.

Specification differences

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

Cached-input rate

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

DeepSeek V4 Pro

$0.003625 per 1M cached input tokens

GLM-5.2

Not published

Reasoning profile

DeepSeek V4 Pro

Non-Reasoning

GLM-5.2

Reasoning

Weight access

DeepSeek V4 Pro

Open Weight

GLM-5.2

Open Weight

License

DeepSeek V4 Pro

Open Weight

GLM-5.2

Open Weight

Release date

DeepSeek V4 Pro

2026-04-24

GLM-5.2

2026-06-16

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, 62.94 versus 59.95, but the 90% score intervals overlap.
Workload cost
Repository review: $0.02436 vs $0.0832. Cache-heavy agent loop: $0.01812 vs $0.352.
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 evidence31 rows

Agentic

  • Terminal-Bench 2.0

    DeepSeek V4 Pro59.1%
    Source
    GLM-5.281%
    Source

    GLM-5.2 leads this result

  • MCP Atlas

    DeepSeek V4 Pro69.4%
    Source
    GLM-5.276.8%
    Source

    GLM-5.2 leads this result

  • Toolathlon

    DeepSeek V4 Pro46.3%
    Source
    GLM-5.248.2%
    Source

    GLM-5.2 leads this result

  • Claw-Eval

    DeepSeek V4 Pro59.8%
    Source
    GLM-5.2—

    Not directly comparable

  • Gert Labs

    DeepSeek V4 Pro50.28%
    Source
    GLM-5.2—

    Not directly comparable

  • ResearchClawBench

    Shared source
    DeepSeek V4 Pro17.1%
    GLM-5.220.7%

    GLM-5.2 leads this result

Coding

  • LiveCodeBench Pass@1-COT

    DeepSeek V4 Pro56.8%
    Source
    GLM-5.2—

    Not directly comparable

  • SWE-bench Verified

    DeepSeek V4 Pro73.6%
    Source
    GLM-5.2—

    Not directly comparable

  • SWE-bench Pro

    DeepSeek V4 Pro52.1%
    Source
    GLM-5.262.1%
    Source

    GLM-5.2 leads this result

  • SWE Multilingual

    DeepSeek V4 Pro69.8%
    Source
    GLM-5.2—

    Not directly comparable

  • Terminal-Bench 2.0

    DeepSeek V4 Pro59.1%
    Source
    GLM-5.281.0%
    Source

    GLM-5.2 leads this result

  • NL2Repo

    DeepSeek V4 Pro—
    GLM-5.248.9%
    Source

    Not directly comparable

  • ProgramBench

    DeepSeek V4 Pro—
    GLM-5.263.7%
    Source

    Not directly comparable

  • cursorBench32

    DeepSeek V4 Pro—
    GLM-5.255.0%
    Source

    Not directly comparable

Reasoning

  • MRCR 1M

    DeepSeek V4 Pro44.7%
    Source
    GLM-5.2—

    Not directly comparable

  • CorpusQA 1M

    DeepSeek V4 Pro35.6%
    Source
    GLM-5.2—

    Not directly comparable

  • CritPt

    DeepSeek V4 Pro—
    GLM-5.220.9%
    Source

    Not directly comparable

Knowledge

  • MMLU-Pro

    DeepSeek V4 Pro82.9%
    Source
    GLM-5.2—

    Not directly comparable

  • SimpleQA

    DeepSeek V4 Pro45%
    Source
    GLM-5.2—

    Not directly comparable

  • Chinese-SimpleQA

    DeepSeek V4 Pro75.8%
    Source
    GLM-5.2—

    Not directly comparable

  • GPQA

    DeepSeek V4 Pro72.9%
    Source
    GLM-5.291.2%
    Source

    GLM-5.2 leads this result

  • GPQA-D

    DeepSeek V4 Pro72.9%
    Source
    GLM-5.291.2%
    Source

    GLM-5.2 leads this result

  • HLE

    DeepSeek V4 Pro7.7%
    Source
    GLM-5.254.7%
    Source

    GLM-5.2 leads this result

  • HLE w/o tools

    DeepSeek V4 Pro—
    GLM-5.240.5%
    Source

    Not directly comparable

Math

  • HMMT Feb 2026

    DeepSeek V4 Pro31.7%
    Source
    GLM-5.292.5%
    Source

    GLM-5.2 leads this result

  • IMOAnswerBench

    DeepSeek V4 Pro35.3%
    Source
    GLM-5.2—

    Not directly comparable

  • Apex

    DeepSeek V4 Pro0.4%
    Source
    GLM-5.2—

    Not directly comparable

  • Apex Shortlist

    DeepSeek V4 Pro9.2%
    Source
    GLM-5.2—

    Not directly comparable

  • AIME26

    DeepSeek V4 Pro—
    GLM-5.299.2%
    Source

    Not directly comparable

  • HMMT Nov 2025

    DeepSeek V4 Pro—
    GLM-5.294.4%
    Source

    Not directly comparable

  • MMAnswerBench

    DeepSeek V4 Pro—
    GLM-5.291.0%
    Source

    Not directly comparable

Frequently asked questions

Which is better, DeepSeek V4 Pro or GLM-5.2?

GLM-5.2 has the higher public score estimate, 62.94 versus 59.95, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, DeepSeek V4 Pro or GLM-5.2?

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, DeepSeek V4 Pro or GLM-5.2?

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

Which costs less, DeepSeek V4 Pro or GLM-5.2?

For the stated presets, chat costs $0.00087 on DeepSeek V4 Pro and $0.0036 on GLM-5.2; repository review costs $0.02436 and $0.0832; the cache-heavy agent loop costs $0.01812 and $0.352. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, DeepSeek V4 Pro or GLM-5.2?

Both models list the same context window, 1M.

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Last updated July 31, 2026

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