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BenchLM

DeepSeek V4 Pro 0813 vs GLM-5V-Turbo

Updated September 27, 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

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 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

63.48/100

Estimated · Public rank #33

90% interval 52.0–75.0

Model B
Z.AI logo
GLM-5V-Turbo

Z.AI

48.89/100

Estimated · Public rank #80

90% interval 33.6–64.2

Shared results
0
DeepSeek V4 Pro 0813 only
42
GLM-5V-Turbo only
2
Like-for-like categories
0 / 8
Estimated: DeepSeek V4 Pro 0813 and GLM-5V-TurboHow 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

    GLM-5V-Turbo

    GLM-5V-Turbo 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

    GLM-5V-Turbo

    GLM-5V-Turbo 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

    GLM-5V-Turbo is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.

    Confidence: limited

  • Agentic work

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

    Not enough matched evidence

    GLM-5V-Turbo is not ranked on the public lane for agentic, so no winner is named for agentic.

    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-5V-Turbo does not fit this workload in one request. GLM-5V-Turbo 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.

50.3DeepSeek V4 Pro 081334.4GLM-5V-Turbo

Directional only · BenchAlign v5.7

DeepSeek V4 Pro 0813 scores higher, but at least one score rests on Estimated evidence or a different benchmark set. Directional only, no winner.

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.

A shared-evidence shape is not available.

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

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.7 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

Directional only
DeepSeek V4 Pro 0813
50.3
Supported · #39/135
GLM-5V-Turbo
34.4
Estimated · #76/135
Basis
BenchAlign v5.7 lane · 15 vs 0 public rows
Reading
Directional only

Knowledge

Directional only
DeepSeek V4 Pro 0813
63.4
Estimated · #29/158
GLM-5V-Turbo
43.5
Estimated · #76/158
Basis
BenchAlign v5.7 lane · 8 vs 0 public rows
Reading
Directional only

Agentic

Not comparable
DeepSeek V4 Pro 0813
55.0
Supported · #29/105
GLM-5V-Turbo
Not ranked
Basis
BenchAlign v5.7 lane · 11 vs 2 public rows
Reading
Not comparable

Reasoning

Not comparable
DeepSeek V4 Pro 0813
56.9
Unranked · 4 rankable rows
GLM-5V-Turbo
70.4
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-5V-Turbo
67.3
Unranked · 1 rankable row
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
DeepSeek V4 Pro 0813
Not ranked
GLM-5V-Turbo
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
DeepSeek V4 Pro 0813
Not ranked
GLM-5V-Turbo
72.5
#61/124
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
DeepSeek V4 Pro 0813
80.2
Unranked · 4 rankable rows
GLM-5V-Turbo
Not ranked
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) 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-5V-Turbo
$0.0032
Fits in one request

GLM-5V-Turbo 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-5V-Turbo
$0.072
Fits in one request

GLM-5V-Turbo 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-5V-Turbo
$0.304
Does not fit in one request
Cached input priced at the published list-input rate

GLM-5V-Turbo does not fit this workload in one request. GLM-5V-Turbo 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-5V-Turbo

200K

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-5V-Turbo

Not published

Reasoning profile

DeepSeek V4 Pro 0813

Reasoning

GLM-5V-Turbo

Non-Reasoning

Weight access

DeepSeek V4 Pro 0813

Open Weight

GLM-5V-Turbo

Proprietary

License

DeepSeek V4 Pro 0813

Open Weight

GLM-5V-Turbo

Proprietary

Release date

DeepSeek V4 Pro 0813

2026-08-13

GLM-5V-Turbo

2026-03-01

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
Repository review: $0.07788 vs $0.072. Cache-heavy agent loop: $0.0748 vs $0.304.
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-5V-Turbo?

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 0813 or GLM-5V-Turbo?

DeepSeek V4 Pro 0813 scores higher for coding on the public lane, 50.3 to 34.4. GLM-5V-Turbo is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; 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-5V-Turbo?

GLM-5V-Turbo is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, DeepSeek V4 Pro 0813 or GLM-5V-Turbo?

For the stated presets, chat costs $0.0033 on DeepSeek V4 Pro 0813 and $0.0032 on GLM-5V-Turbo; repository review costs $0.07788 and $0.072; the cache-heavy agent loop costs $0.0748 and $0.304. GLM-5V-Turbo does not fit this workload in one request. GLM-5V-Turbo 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-5V-Turbo?

DeepSeek V4 Pro 0813 has the larger documented context window: 1M, compared with 200K.

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-5V-Turbo—

    Not directly comparable

  • Terminal-Bench 2.1

    DeepSeek V4 Pro 081387.9%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • BrowseComp

    DeepSeek V4 Pro 081383.4%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • HLE w/ tools

    DeepSeek V4 Pro 081360.0%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • MCP Atlas

    DeepSeek V4 Pro 081373.6%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • Toolathlon

    DeepSeek V4 Pro 081351.8%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • CyberGym

    DeepSeek V4 Pro 081383.3%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • Toolathlon-Verified

    DeepSeek V4 Pro 081374.1%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • Agents' Last Exam

    DeepSeek V4 Pro 081325.7%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • AutomationBench

    DeepSeek V4 Pro 081331.8%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    DeepSeek V4 Pro 081354.7%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • Claw-Eval

    DeepSeek V4 Pro 0813—
    GLM-5V-Turbo53.8%
    Source

    Not directly comparable

  • Gert Labs

    DeepSeek V4 Pro 0813—
    GLM-5V-Turbo30.76%
    Source

    Not directly comparable

Coding

  • LiveCodeBench Pass@1-COT

    DeepSeek V4 Pro 081393.5%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • Codeforces

    DeepSeek V4 Pro 08133206.0
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • SWE-bench Verified

    DeepSeek V4 Pro 081380.6%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • SWE-bench Pro

    DeepSeek V4 Pro 081355.4%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • SWE Multilingual

    DeepSeek V4 Pro 081376.2%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • Terminal-Bench 2.0

    DeepSeek V4 Pro 081367.9%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • Vibe Code Bench

    DeepSeek V4 Pro 081349.93%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • Terminal-Bench 2.1

    DeepSeek V4 Pro 081387.9%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • NL2Repo

    DeepSeek V4 Pro 081361.5%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • DeepSWE

    DeepSeek V4 Pro 081362.7%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • DSBench-FullStack

    DeepSeek V4 Pro 081371.1%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • DSBench-Hard

    DeepSeek V4 Pro 081367.2%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • OpenHarmony Bench

    DeepSeek V4 Pro 081359.0%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • LiveCodeBench (Vals)

    DeepSeek V4 Pro 081387.5%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • SWE-bench (Vals)

    DeepSeek V4 Pro 081396.4%
    Source
    GLM-5V-Turbo—

    Not directly comparable

Reasoning

  • MRCR 1M

    DeepSeek V4 Pro 081383.5%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • CorpusQA 1M

    DeepSeek V4 Pro 081362.0%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • ARC-AGI-1

    DeepSeek V4 Pro 081390.00%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • ARC-AGI-2

    DeepSeek V4 Pro 081361.3%
    Source
    GLM-5V-Turbo—

    Not directly comparable

Knowledge

  • MMLU-Pro

    DeepSeek V4 Pro 081387.5%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • SimpleQA

    DeepSeek V4 Pro 081357.9%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • Chinese-SimpleQA

    DeepSeek V4 Pro 081384.4%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • GPQA

    DeepSeek V4 Pro 081390.1%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • GPQA-D

    DeepSeek V4 Pro 081390.1%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • HLE

    DeepSeek V4 Pro 081342.7%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • GPQA Diamond (Vals)

    DeepSeek V4 Pro 081392.4%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • MMLU-Pro (Vals)

    DeepSeek V4 Pro 081387.0%
    Source
    GLM-5V-Turbo—

    Not directly comparable

Math

  • HMMT Feb 2026

    DeepSeek V4 Pro 081395.2%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • IMOAnswerBench

    DeepSeek V4 Pro 081389.8%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • Apex

    DeepSeek V4 Pro 081338.3%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • Apex Shortlist

    DeepSeek V4 Pro 081390.2%
    Source
    GLM-5V-Turbo—

    Not directly comparable

44 public results · 0 shared

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Last updated September 27, 2026