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DeepSeek V4 Pro 0813 vs GLM-5.1

Updated October 16, 2026. Rank says DeepSeek V4 Pro 0813 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

DeepSeek V4 Pro 0813 has the higher public point estimate, 63.18 versus 56.29. Their conditional score ranges overlap. These ranges do not establish rank confidence. 18 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
DeepSeek logo
DeepSeek V4 Pro 0813

DeepSeek

63.18/100

Estimated · Public rank #42

Conditional range 48.8–77.5

Model B
Z.AI logo
GLM-5.1

Z.AI

56.29/100

Supported · Public rank #62

90% interval 45.6–67.0

Shared results
18
DeepSeek V4 Pro 0813 only
23
GLM-5.1 only
9
Like-for-like categories
1 / 8
Estimated: DeepSeek V4 Pro 0813 · Supported: GLM-5.1. Conditional ranges do not establish rank confidence.How the comparison works

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.

  • 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

    No clear pick

    The like-for-like coding result is a practical tie on the public lane (within 0.5 points).

    Confidence: limited

  • Agentic work

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

    Not enough matched evidence

    GLM-5.1 is scored on Estimated evidence for agentic, so the reading is 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

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

47.2DeepSeek V4 Pro 081347.2GLM-5.1

Like-for-like · BenchAlign v5.8

The coding point estimates are equal. Conditional score ranges do not establish rank confidence.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboardShortlist models for this job
Same basis rules as the category table below

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.8 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.

Coding

Like-for-like
DeepSeek V4 Pro 0813
47.2
Supported · #49/150
GLM-5.1
47.2
Supported · #50/150
Basis
BenchAlign v5.8 lane · 15 vs 8 public rows
Reading
Tie

Agentic

Directional only
DeepSeek V4 Pro 0813
54.5
Supported · #43/128
GLM-5.1
43.5
Estimated · #62/128
Basis
BenchAlign v5.8 lane · 11 vs 10 public rows
Reading
Directional only

Knowledge

Directional only
DeepSeek V4 Pro 0813
63.5
Estimated · #41/184
GLM-5.1
52.6
Supported · #70/184
Basis
BenchAlign v5.8 lane · 7 vs 4 public rows
Reading
Directional only

Reasoning

Not comparable
DeepSeek V4 Pro 0813
60.7
Unranked · 4 rankable rows
GLM-5.1
76.5
Unranked · 2 rankable rows
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
DeepSeek V4 Pro 0813
Not ranked
GLM-5.1
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
DeepSeek V4 Pro 0813
Not ranked
GLM-5.1
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
DeepSeek V4 Pro 0813
Not ranked
GLM-5.1
93.8
#6/148
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
DeepSeek V4 Pro 0813
87.4
Unranked · 4 rankable rows
GLM-5.1
66.7
#7/12
Basis
Provisional lane · 1 vs 3 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

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
$0.0033
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
$0.07788
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
$0.0748
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.

Cached input falls back to the list input rate only where a cached rate is unpublished

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

$0.044 per 1M cached input tokens

DeepSeek: Models & Pricing

GLM-5.1

Not published

Reasoning profile

DeepSeek V4 Pro 0813

Reasoning

GLM-5.1

Reasoning

Weight access

DeepSeek V4 Pro 0813

Open Weight

GLM-5.1

Open Weight

License

DeepSeek V4 Pro 0813

Open Weight

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
DeepSeek V4 Pro 0813 has the higher public point estimate, 63.18 versus 56.29. Their conditional score ranges overlap. These ranges do not establish rank confidence.
Workload cost
Repository review: $0.07788 vs $0.0832. Cache-heavy agent loop: $0.0748 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.

Questions

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

DeepSeek V4 Pro 0813 has the higher public point estimate, 63.18 versus 56.29. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.

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

The like-for-like coding row is tied on the public lane, 47.2 each.

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

DeepSeek V4 Pro 0813 scores higher for agentic tasks on the public lane, 54.5 to 43.5. GLM-5.1 is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

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

For the stated presets, chat costs $0.0033 on DeepSeek V4 Pro 0813 and $0.0036 on GLM-5.1; repository review costs $0.07788 and $0.0832; the cache-heavy agent loop costs $0.0748 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.

Self-host vs API cost

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

DeepSeek V4 Pro 0813
API / mo$0
Self-host / mo$37,843
Break-even—
GLM-5.1
API / mo$4,350
Self-host / mo$27,962
Break-even405M/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 evidence50 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-5.152.3%
    Source

    DeepSeek V4 Pro 0813 leads this result

  • 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

  • Terminal-Bench 2.1 (Vals)

    DeepSeek V4 Pro 081354.7%
    Source
    GLM-5.156.9%
    Source

    GLM-5.1 leads this result

  • 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-5.163.5%
    Source

    DeepSeek V4 Pro 0813 leads this result

  • 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

  • OpenHarmony Bench

    Shared source
    DeepSeek V4 Pro 081359.0%
    GLM-5.152.3%

    DeepSeek V4 Pro 0813 leads this result

  • LiveCodeBench (Vals)

    DeepSeek V4 Pro 081387.5%
    Source
    GLM-5.181.4%
    Source

    DeepSeek V4 Pro 0813 leads this result

  • SWE-bench (Vals)

    DeepSeek V4 Pro 081396.4%
    Source
    GLM-5.176.4%
    Source

    DeepSeek V4 Pro 0813 leads this result

  • 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

  • ARC-AGI-1

    DeepSeek V4 Pro 081390.00%
    Source
    GLM-5.1—

    Not directly comparable

  • ARC-AGI-2

    DeepSeek V4 Pro 081361.3%
    Source
    GLM-5.1—

    Not directly comparable

Knowledge

  • MMLU-Pro

    DeepSeek V4 Pro 081387.5%
    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-5.131%
    Source

    DeepSeek V4 Pro 0813 leads this result

  • GPQA Diamond (Vals)

    DeepSeek V4 Pro 081392.4%
    Source
    GLM-5.184.5%
    Source

    DeepSeek V4 Pro 0813 leads this result

  • MMLU-Pro (Vals)

    DeepSeek V4 Pro 081387.0%
    Source
    GLM-5.186.9%
    Source

    DeepSeek V4 Pro 0813 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-5.136.842%
    Source

    Not directly comparable

50 public results · 18 shared

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Last updated October 16, 2026