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Model comparison

DeepSeek V4 Pro vs GLM Realtime Flash

Updated August 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

DeepSeek V4 Pro

DeepSeek

60.0/100

Supported · Public rank #50

90% interval 41.7–78.3

GLM Realtime Flash

Zhipu AI

—

Evidence status unavailable

90% interval unavailable

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.

0 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.

No workload recommendation clears the current evidence threshold.

Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.

Show secondary and unsupported calls

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • 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

    Not enough matched evidence

    A complete context comparison is not sourced.

    Confidence: limited

  • 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

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
0
DeepSeek V4 Pro only
23
GLM Realtime Flash only
0
Like-for-like categories
0 / 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.

Agentic

Not comparable
DeepSeek V4 Pro
59.1
GLM Realtime Flash
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Coding

Not comparable
DeepSeek V4 Pro
65.3
GLM Realtime Flash
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
DeepSeek V4 Pro
Not measured
GLM Realtime Flash
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
DeepSeek V4 Pro
41.3
GLM Realtime Flash
Not measured
Weighted basis
4 vs 0 rows
Reading
Not comparable

Math

Not comparable
DeepSeek V4 Pro
31.7
GLM Realtime Flash
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
DeepSeek V4 Pro
Not measured
GLM Realtime Flash
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
DeepSeek V4 Pro
Not measured
GLM Realtime Flash
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
DeepSeek V4 Pro
Not measured
GLM Realtime Flash
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.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

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 Realtime Flash
API rate not published
Fit state unavailable

GLM Realtime Flash has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

DeepSeek V4 Pro
$0.02436
Fits in one request
GLM Realtime Flash
API rate not published
Fit state unavailable

GLM Realtime Flash has no comparable published API token rate.

Cache-heavy agent loop

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

DeepSeek V4 Pro
$0.01812
Fits in one request
GLM Realtime Flash
API rate not published
Fit state unavailable
Cached-input rate unavailable

GLM Realtime Flash 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 Realtime Flash

N/A

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 Realtime Flash

No comparable hosted API rate

Zhipu AI model documentation

Reasoning profile

DeepSeek V4 Pro

Non-Reasoning

GLM Realtime Flash

Non-Reasoning

Weight access

DeepSeek V4 Pro

Open Weight

GLM Realtime Flash

Proprietary

License

DeepSeek V4 Pro

Open Weight

GLM Realtime Flash

Proprietary

Release date

DeepSeek V4 Pro

2026-04-24

GLM Realtime Flash

Not sourced

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
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
A complete documented context comparison is not available.

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 evidence23 rows

Agentic

  • Terminal-Bench 2.0

    DeepSeek V4 Pro59.1%
    Source
    GLM Realtime Flash—

    Not directly comparable

  • MCP Atlas

    DeepSeek V4 Pro69.4%
    Source
    GLM Realtime Flash—

    Not directly comparable

  • Toolathlon

    DeepSeek V4 Pro46.3%
    Source
    GLM Realtime Flash—

    Not directly comparable

  • Claw-Eval

    DeepSeek V4 Pro59.8%
    Source
    GLM Realtime Flash—

    Not directly comparable

  • Gert Labs

    DeepSeek V4 Pro50.28%
    Source
    GLM Realtime Flash—

    Not directly comparable

  • ResearchClawBench

    DeepSeek V4 Pro17.1%
    Source
    GLM Realtime Flash—

    Not directly comparable

Coding

  • LiveCodeBench Pass@1-COT

    DeepSeek V4 Pro56.8%
    Source
    GLM Realtime Flash—

    Not directly comparable

  • SWE-bench Verified

    DeepSeek V4 Pro73.6%
    Source
    GLM Realtime Flash—

    Not directly comparable

  • SWE-bench Pro

    DeepSeek V4 Pro52.1%
    Source
    GLM Realtime Flash—

    Not directly comparable

  • SWE Multilingual

    DeepSeek V4 Pro69.8%
    Source
    GLM Realtime Flash—

    Not directly comparable

  • Terminal-Bench 2.0

    DeepSeek V4 Pro59.1%
    Source
    GLM Realtime Flash—

    Not directly comparable

Reasoning

  • MRCR 1M

    DeepSeek V4 Pro44.7%
    Source
    GLM Realtime Flash—

    Not directly comparable

  • CorpusQA 1M

    DeepSeek V4 Pro35.6%
    Source
    GLM Realtime Flash—

    Not directly comparable

Knowledge

  • MMLU-Pro

    DeepSeek V4 Pro82.9%
    Source
    GLM Realtime Flash—

    Not directly comparable

  • SimpleQA

    DeepSeek V4 Pro45%
    Source
    GLM Realtime Flash—

    Not directly comparable

  • Chinese-SimpleQA

    DeepSeek V4 Pro75.8%
    Source
    GLM Realtime Flash—

    Not directly comparable

  • GPQA

    DeepSeek V4 Pro72.9%
    Source
    GLM Realtime Flash—

    Not directly comparable

  • GPQA-D

    DeepSeek V4 Pro72.9%
    Source
    GLM Realtime Flash—

    Not directly comparable

  • HLE

    DeepSeek V4 Pro7.7%
    Source
    GLM Realtime Flash—

    Not directly comparable

Math

  • HMMT Feb 2026

    DeepSeek V4 Pro31.7%
    Source
    GLM Realtime Flash—

    Not directly comparable

  • IMOAnswerBench

    DeepSeek V4 Pro35.3%
    Source
    GLM Realtime Flash—

    Not directly comparable

  • Apex

    DeepSeek V4 Pro0.4%
    Source
    GLM Realtime Flash—

    Not directly comparable

  • Apex Shortlist

    DeepSeek V4 Pro9.2%
    Source
    GLM Realtime Flash—

    Not directly comparable

Frequently asked questions

Which is better, DeepSeek V4 Pro or GLM Realtime Flash?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, DeepSeek V4 Pro or GLM Realtime Flash?

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

Which is better for agentic tasks, DeepSeek V4 Pro or GLM Realtime Flash?

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, DeepSeek V4 Pro or GLM Realtime Flash?

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, DeepSeek V4 Pro or GLM Realtime Flash?

A complete documented context-window comparison is not available.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 4, 2026

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