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BenchLM
Model A
DeepSeek V4 Pro 0813

DeepSeek

60.9/100

Estimated · Public rank #48

90% interval 51.1–70.8

DeepSeek V4 Pro 0813 vs GLM-5.1

Updated August 13, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
GLM-5.1

Z.AI

66.6/100

Supported · Public rank #26

90% interval 56.4–76.8

Decision reading

GLM-5.1 has the higher public score estimate, 66.63 versus 60.93, 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

    DeepSeek V4 Pro 0813

    DeepSeek V4 Pro 0813 leads on the same 2 weighted benchmark rows.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    DeepSeek V4 Pro 0813

    DeepSeek V4 Pro 0813 has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    DeepSeek V4 Pro 0813

    DeepSeek V4 Pro 0813 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

Show secondary and unsupported calls

  • Repository review cost

    50K fresh input + 3K output tokens

    DeepSeek V4 Pro 0813

    DeepSeek V4 Pro 0813 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

  • Cache-heavy agent loop cost

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

    Not enough matched evidence

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. GLM-5.1 does not fit this workload in one request. GLM-5.1 has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

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 0813 only
24
GLM-5.1 only
10
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 0813
74.5
GLM-5.1
65.4
Weighted basis
2 vs 2 rows
Reading
DeepSeek V4 Pro 0813 leads

Coding

Directional only
DeepSeek V4 Pro 0813
70.9
GLM-5.1
61.3
Weighted basis
2 vs 2 rows
Reading
Directional only

Knowledge

Directional only
DeepSeek V4 Pro 0813
62.5
GLM-5.1
52.3
Weighted basis
4 vs 1 rows
Reading
Directional only

Math

Directional only
DeepSeek V4 Pro 0813
95.2
GLM-5.1
62.0
Weighted basis
1 vs 4 rows
Reading
Directional only

Reasoning

Not comparable
DeepSeek V4 Pro 0813
Not measured
GLM-5.1
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
DeepSeek V4 Pro 0813
Not measured
GLM-5.1
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
DeepSeek V4 Pro 0813
Not measured
GLM-5.1
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
DeepSeek V4 Pro 0813
Not measured
GLM-5.1
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 0813
$0.00087
Fits in one request
GLM-5.1
$0.0036
Fits in one request

DeepSeek V4 Pro 0813 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

DeepSeek V4 Pro 0813
$0.02436
Fits in one request
GLM-5.1
$0.0832
Fits in one request

DeepSeek V4 Pro 0813 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 0813
$0.01812
Fits in one request
GLM-5.1
$0.352
Does not fit in one request
Cached input priced at the published list-input rate

GLM-5.1 does not fit this workload in one request. GLM-5.1 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.

Context window

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

DeepSeek V4 Pro 0813

1M

DeepSeek models and pricing

GLM-5.1

203K

Cached-input rate

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

DeepSeek V4 Pro 0813

$0.003625 per 1M cached input tokens

GLM-5.1

Not published

Reasoning profile

DeepSeek V4 Pro 0813

Reasoning

GLM-5.1

Reasoning

Weight access

DeepSeek V4 Pro 0813

Proprietary

GLM-5.1

Open Weight

License

DeepSeek V4 Pro 0813

Proprietary

GLM-5.1

Open Weight

Release date

DeepSeek V4 Pro 0813

2026-08-13

GLM-5.1

2026-04-07

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-5.1 has the higher public score estimate, 66.63 versus 60.93, 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
DeepSeek V4 Pro 0813 has the larger documented window (1M).

Run the same representative tasks against both endpoints before changing production traffic.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

DeepSeek V4 Pro 0813
API / mo$0
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
GLM-5.1
API / mo$4,350
Self-host / mo$18,221
Break-even264M/day
Model the full break-even

Benchmark evidence

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

Browse raw public benchmark evidence44 rows

Agentic

  • Terminal-Bench 2.0

    DeepSeek V4 Pro 081367.9%
    Source
    GLM-5.163.5%
    Source

    DeepSeek V4 Pro 0813 leads this result

  • Terminal-Bench 2.1

    DeepSeek V4 Pro 081387.9%
    Source
    GLM-5.1—

    Not directly comparable

  • BrowseComp

    DeepSeek V4 Pro 081383.4%
    Source
    GLM-5.168%
    Source

    DeepSeek V4 Pro 0813 leads this result

  • HLE w/ tools

    DeepSeek V4 Pro 081360.0%
    Source
    GLM-5.1—

    Not directly comparable

  • MCP Atlas

    DeepSeek V4 Pro 081373.6%
    Source
    GLM-5.171.8%
    Source

    DeepSeek V4 Pro 0813 leads this result

  • Toolathlon

    DeepSeek V4 Pro 081351.8%
    Source
    GLM-5.1—

    Not directly comparable

  • CyberGym

    DeepSeek V4 Pro 081383.3%
    Source
    GLM-5.168.7%
    Source

    DeepSeek V4 Pro 0813 leads this result

  • Toolathlon-Verified

    DeepSeek V4 Pro 081374.1%
    Source
    GLM-5.1—

    Not directly comparable

  • Agents' Last Exam

    DeepSeek V4 Pro 081325.7%
    Source
    GLM-5.1—

    Not directly comparable

  • AutomationBench

    DeepSeek V4 Pro 081331.8%
    Source
    GLM-5.1—

    Not directly comparable

  • τ³-bench results

    DeepSeek V4 Pro 0813—
    GLM-5.170.6%
    Source

    Not directly comparable

  • Claw-Eval

    DeepSeek V4 Pro 0813—
    GLM-5.162.3%
    Source

    Not directly comparable

  • Gert Labs

    DeepSeek V4 Pro 0813—
    GLM-5.160.11%
    Source

    Not directly comparable

  • ResearchClawBench

    DeepSeek V4 Pro 0813—
    GLM-5.118.2%
    Source

    Not directly comparable

Coding

  • LiveCodeBench Pass@1-COT

    DeepSeek V4 Pro 081393.5%
    Source
    GLM-5.1—

    Not directly comparable

  • Codeforces

    DeepSeek V4 Pro 08133206.0
    Source
    GLM-5.1—

    Not directly comparable

  • SWE-bench Verified

    DeepSeek V4 Pro 081380.6%
    Source
    GLM-5.1—

    Not directly comparable

  • SWE-bench Pro

    DeepSeek V4 Pro 081355.4%
    Source
    GLM-5.158.4%
    Source

    GLM-5.1 leads this result

  • SWE Multilingual

    DeepSeek V4 Pro 081376.2%
    Source
    GLM-5.1—

    Not directly comparable

  • Terminal-Bench 2.0

    DeepSeek V4 Pro 081367.9%
    Source
    GLM-5.1—

    Not directly comparable

  • Vibe Code Bench

    Shared source
    DeepSeek V4 Pro 081349.93%
    GLM-5.131.46%

    DeepSeek V4 Pro 0813 leads this result

  • Terminal-Bench 2.1

    DeepSeek V4 Pro 081387.9%
    Source
    GLM-5.1—

    Not directly comparable

  • NL2Repo

    DeepSeek V4 Pro 081361.5%
    Source
    GLM-5.142.7%
    Source

    DeepSeek V4 Pro 0813 leads this result

  • deepSwe

    DeepSeek V4 Pro 081362.7%
    Source
    GLM-5.1—

    Not directly comparable

  • DSBench-FullStack

    DeepSeek V4 Pro 081371.1%
    Source
    GLM-5.1—

    Not directly comparable

  • DSBench-Hard

    DeepSeek V4 Pro 081367.2%
    Source
    GLM-5.1—

    Not directly comparable

  • SWE-Rebench

    DeepSeek V4 Pro 0813—
    GLM-5.162.7%
    Source

    Not directly comparable

Reasoning

  • MRCR 1M

    DeepSeek V4 Pro 081383.5%
    Source
    GLM-5.1—

    Not directly comparable

  • CorpusQA 1M

    DeepSeek V4 Pro 081362.0%
    Source
    GLM-5.1—

    Not directly comparable

Knowledge

  • MMLU-Pro

    DeepSeek V4 Pro 081387.5%
    Source
    GLM-5.1—

    Not directly comparable

  • SimpleQA

    DeepSeek V4 Pro 081357.9%
    Source
    GLM-5.1—

    Not directly comparable

  • Chinese-SimpleQA

    DeepSeek V4 Pro 081384.4%
    Source
    GLM-5.1—

    Not directly comparable

  • GPQA

    DeepSeek V4 Pro 081390.1%
    Source
    GLM-5.1—

    Not directly comparable

  • GPQA-D

    DeepSeek V4 Pro 081390.1%
    Source
    GLM-5.186.2%
    Source

    DeepSeek V4 Pro 0813 leads this result

  • HLE

    DeepSeek V4 Pro 081342.7%
    Source
    GLM-5.152.3%
    Source

    GLM-5.1 leads this result

Math

  • HMMT Feb 2026

    DeepSeek V4 Pro 081395.2%
    Source
    GLM-5.182.6%
    Source

    DeepSeek V4 Pro 0813 leads this result

  • IMOAnswerBench

    DeepSeek V4 Pro 081389.8%
    Source
    GLM-5.1—

    Not directly comparable

  • Apex

    DeepSeek V4 Pro 081338.3%
    Source
    GLM-5.1—

    Not directly comparable

  • Apex Shortlist

    DeepSeek V4 Pro 081390.2%
    Source
    GLM-5.1—

    Not directly comparable

  • AIME26

    DeepSeek V4 Pro 0813—
    GLM-5.195.3%
    Source

    Not directly comparable

  • HMMT Nov 2025

    DeepSeek V4 Pro 0813—
    GLM-5.194.0%
    Source

    Not directly comparable

  • MMAnswerBench

    DeepSeek V4 Pro 0813—
    GLM-5.183.8%
    Source

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    DeepSeek V4 Pro 0813—
    GLM-5.133.448%
    Source

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    DeepSeek V4 Pro 0813—
    GLM-5.112.500%
    Source

    Not directly comparable

Frequently asked questions

Which is better, DeepSeek V4 Pro 0813 or GLM-5.1?

GLM-5.1 has the higher public score estimate, 66.63 versus 60.93, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, DeepSeek V4 Pro 0813 or GLM-5.1?

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 0813 or GLM-5.1?

DeepSeek V4 Pro 0813 leads the like-for-like agentic tasks comparison across 2 shared weighted benchmark rows.

Which costs less, DeepSeek V4 Pro 0813 or GLM-5.1?

For the stated presets, chat costs $0.00087 on DeepSeek V4 Pro 0813 and $0.0036 on GLM-5.1; repository review costs $0.02436 and $0.0832; the cache-heavy agent loop costs $0.01812 and $0.352. GLM-5.1 does not fit this workload in one request. GLM-5.1 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, DeepSeek V4 Pro 0813 or GLM-5.1?

DeepSeek V4 Pro 0813 has the larger documented context window: 1M, compared with 203K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 13, 2026

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