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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 Kimi K3

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

Model B
Kimi K3

Moonshot AI

80.2/100

Supported · Public rank #5

90% interval 77.3–83.1

Decision reading

Kimi K3 has the higher public score, 80.21 versus 60.93, and the 90% score intervals do not overlap.

9 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

    Kimi K3

    Kimi K3 leads on the same 2 weighted benchmark rows.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    Kimi K3

    Kimi K3 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

  • Cache-heavy agent loop cost

    200K cached + 20K fresh input + 10K 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

  • 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

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

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
9
DeepSeek V4 Pro 0813 only
25
Kimi K3 only
31
Like-for-like categories
1 / 8

1 category uses 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
Kimi K3
89.5
Weighted basis
2 vs 2 rows
Reading
Kimi K3 leads

Knowledge

Directional only
DeepSeek V4 Pro 0813
62.5
Kimi K3
61.0
Weighted basis
4 vs 2 rows
Reading
Directional only

Coding

Not comparable
DeepSeek V4 Pro 0813
70.9
Kimi K3
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
DeepSeek V4 Pro 0813
Not measured
Kimi K3
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
DeepSeek V4 Pro 0813
95.2
Kimi K3
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
DeepSeek V4 Pro 0813
Not measured
Kimi K3
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
DeepSeek V4 Pro 0813
Not measured
Kimi K3
78.5
Weighted basis
0 vs 3 rows
Reading
Not comparable

Instruction following

Not comparable
DeepSeek V4 Pro 0813
Not measured
Kimi K3
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
Kimi K3
$0.0105
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
Kimi K3
$0.195
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
Kimi K3
$0.27
Fits in one request

DeepSeek V4 Pro 0813 has the lower modeled cost

Costs use the listed standard API rates.

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

Kimi K3

1.05M

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

Kimi K3

$0.3 per 1M cached input tokens

Reasoning profile

DeepSeek V4 Pro 0813

Reasoning

Kimi K3

Reasoning

Weight access

DeepSeek V4 Pro 0813

Proprietary

Kimi K3

Pending

License

DeepSeek V4 Pro 0813

Proprietary

Kimi K3

Pending

Release date

DeepSeek V4 Pro 0813

2026-08-13

Kimi K3

2026-07-16

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
Kimi K3 has the higher public score, 80.21 versus 60.93, and the 90% score intervals do not overlap.
Workload cost
Repository review: $0.02436 vs $0.195. Cache-heavy agent loop: $0.01812 vs $0.27.
Context tradeoff
Kimi K3 has the larger documented window (1.05M).

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 evidence65 rows

Agentic

  • Terminal-Bench 2.0

    DeepSeek V4 Pro 081367.9%
    Source
    Kimi K388.3%
    Source

    Kimi K3 leads this result

  • Terminal-Bench 2.1

    DeepSeek V4 Pro 081387.9%
    Source
    Kimi K3—

    Not directly comparable

  • BrowseComp

    DeepSeek V4 Pro 081383.4%
    Source
    Kimi K391.2%
    Source

    Kimi K3 leads this result

  • HLE w/ tools

    DeepSeek V4 Pro 081360.0%
    Source
    Kimi K3—

    Not directly comparable

  • MCP Atlas

    DeepSeek V4 Pro 081373.6%
    Source
    Kimi K384.2%
    Source

    Kimi K3 leads this result

  • Toolathlon

    DeepSeek V4 Pro 081351.8%
    Source
    Kimi K3—

    Not directly comparable

  • CyberGym

    DeepSeek V4 Pro 081383.3%
    Source
    Kimi K3—

    Not directly comparable

  • Toolathlon-Verified

    DeepSeek V4 Pro 081374.1%
    Source
    Kimi K373.2%
    Source

    DeepSeek V4 Pro 0813 leads this result

  • Agents' Last Exam

    DeepSeek V4 Pro 081325.7%
    Source
    Kimi K3—

    Not directly comparable

  • AutomationBench

    DeepSeek V4 Pro 081331.8%
    Source
    Kimi K330.8%
    Source

    DeepSeek V4 Pro 0813 leads this result

  • DeepSearchQA

    DeepSeek V4 Pro 0813—
    Kimi K395.0%
    Source

    Not directly comparable

  • JobBench

    DeepSeek V4 Pro 0813—
    Kimi K352.9%
    Source

    Not directly comparable

  • APEX-Agents

    DeepSeek V4 Pro 0813—
    Kimi K337.6%
    Source

    Not directly comparable

  • SpreadsheetBench 2

    DeepSeek V4 Pro 0813—
    Kimi K334.8%
    Source

    Not directly comparable

  • DECK-Bench

    DeepSeek V4 Pro 0813—
    Kimi K373.5%
    Source

    Not directly comparable

Coding

  • LiveCodeBench Pass@1-COT

    DeepSeek V4 Pro 081393.5%
    Source
    Kimi K3—

    Not directly comparable

  • Codeforces

    DeepSeek V4 Pro 08133206.0
    Source
    Kimi K3—

    Not directly comparable

  • SWE-bench Verified

    DeepSeek V4 Pro 081380.6%
    Source
    Kimi K3—

    Not directly comparable

  • SWE-bench Pro

    DeepSeek V4 Pro 081355.4%
    Source
    Kimi K3—

    Not directly comparable

  • SWE Multilingual

    DeepSeek V4 Pro 081376.2%
    Source
    Kimi K3—

    Not directly comparable

  • Terminal-Bench 2.0

    DeepSeek V4 Pro 081367.9%
    Source
    Kimi K3—

    Not directly comparable

  • Vibe Code Bench

    DeepSeek V4 Pro 081349.93%
    Source
    Kimi K3—

    Not directly comparable

  • Terminal-Bench 2.1

    DeepSeek V4 Pro 081387.9%
    Source
    Kimi K3—

    Not directly comparable

  • NL2Repo

    DeepSeek V4 Pro 081361.5%
    Source
    Kimi K3—

    Not directly comparable

  • deepSwe

    DeepSeek V4 Pro 081362.7%
    Source
    Kimi K367.5%
    Source

    Kimi K3 leads this result

  • DSBench-FullStack

    DeepSeek V4 Pro 081371.1%
    Source
    Kimi K3—

    Not directly comparable

  • DSBench-Hard

    DeepSeek V4 Pro 081367.2%
    Source
    Kimi K3—

    Not directly comparable

  • cursorBench32

    DeepSeek V4 Pro 0813—
    Kimi K360.8%
    Source

    Not directly comparable

  • FrontierSWE

    DeepSeek V4 Pro 0813—
    Kimi K381.2%
    Source

    Not directly comparable

  • ProgramBench

    DeepSeek V4 Pro 0813—
    Kimi K377.8%
    Source

    Not directly comparable

  • Kimi Code Bench v2

    DeepSeek V4 Pro 0813—
    Kimi K372.9%
    Source

    Not directly comparable

  • sweMarathon

    DeepSeek V4 Pro 0813—
    Kimi K342%
    Source

    Not directly comparable

  • PostTrain Bench

    DeepSeek V4 Pro 0813—
    Kimi K336.6%
    Source

    Not directly comparable

  • MLS-Bench Lite

    DeepSeek V4 Pro 0813—
    Kimi K348.3%
    Source

    Not directly comparable

  • VulcanBench v3

    DeepSeek V4 Pro 0813—
    Kimi K373.9%
    Source

    Not directly comparable

  • APEX-SWE

    DeepSeek V4 Pro 0813—
    Kimi K348.0%
    Source

    Not directly comparable

  • EEBench

    DeepSeek V4 Pro 0813—
    Kimi K338.3%
    Source

    Not directly comparable

  • InferenceEval

    DeepSeek V4 Pro 0813—
    Kimi K339.8%
    Source

    Not directly comparable

  • KernelBench Internal

    DeepSeek V4 Pro 0813—
    Kimi K368.5%
    Source

    Not directly comparable

Reasoning

  • MRCR 1M

    DeepSeek V4 Pro 081383.5%
    Source
    Kimi K3—

    Not directly comparable

  • CorpusQA 1M

    DeepSeek V4 Pro 081362.0%
    Source
    Kimi K3—

    Not directly comparable

Knowledge

  • MMLU-Pro

    DeepSeek V4 Pro 081387.5%
    Source
    Kimi K3—

    Not directly comparable

  • SimpleQA

    DeepSeek V4 Pro 081357.9%
    Source
    Kimi K3—

    Not directly comparable

  • Chinese-SimpleQA

    DeepSeek V4 Pro 081384.4%
    Source
    Kimi K3—

    Not directly comparable

  • GPQA

    DeepSeek V4 Pro 081390.1%
    Source
    Kimi K393.5%
    Source

    Kimi K3 leads this result

  • GPQA-D

    DeepSeek V4 Pro 081390.1%
    Source
    Kimi K393.5%
    Source

    Kimi K3 leads this result

  • HLE

    DeepSeek V4 Pro 081342.7%
    Source
    Kimi K356%
    Source

    Kimi K3 leads this result

  • HLE w/o tools

    DeepSeek V4 Pro 0813—
    Kimi K343.5%
    Source

    Not directly comparable

Math

  • HMMT Feb 2026

    DeepSeek V4 Pro 081395.2%
    Source
    Kimi K3—

    Not directly comparable

  • IMOAnswerBench

    DeepSeek V4 Pro 081389.8%
    Source
    Kimi K3—

    Not directly comparable

  • Apex

    DeepSeek V4 Pro 081338.3%
    Source
    Kimi K3—

    Not directly comparable

  • Apex Shortlist

    DeepSeek V4 Pro 081390.2%
    Source
    Kimi K3—

    Not directly comparable

Multimodal

  • OfficeQA Pro

    DeepSeek V4 Pro 0813—
    Kimi K363.3%
    Source

    Not directly comparable

  • MMMU-Pro

    DeepSeek V4 Pro 0813—
    Kimi K381.6%
    Source

    Not directly comparable

  • MMMU-Pro w/ Python

    DeepSeek V4 Pro 0813—
    Kimi K383.4%
    Source

    Not directly comparable

  • CharXiv w/o tools

    DeepSeek V4 Pro 0813—
    Kimi K384.8%
    Source

    Not directly comparable

  • CharXiv

    DeepSeek V4 Pro 0813—
    Kimi K391.3%
    Source

    Not directly comparable

  • MathVision

    DeepSeek V4 Pro 0813—
    Kimi K394.3%
    Source

    Not directly comparable

  • MathVision w/ Python

    DeepSeek V4 Pro 0813—
    Kimi K397.8%
    Source

    Not directly comparable

  • BabyVision w/ Python

    DeepSeek V4 Pro 0813—
    Kimi K385.7%
    Source

    Not directly comparable

  • ZeroBench

    DeepSeek V4 Pro 0813—
    Kimi K323.0%
    Source

    Not directly comparable

  • ZeroBench w/ Python

    DeepSeek V4 Pro 0813—
    Kimi K341.0%
    Source

    Not directly comparable

  • WorldVQA ForceAnswer

    DeepSeek V4 Pro 0813—
    Kimi K351.0%
    Source

    Not directly comparable

  • OmniDocBench

    DeepSeek V4 Pro 0813—
    Kimi K391.1%
    Source

    Not directly comparable

  • PerceptionBench

    DeepSeek V4 Pro 0813—
    Kimi K358.5%
    Source

    Not directly comparable

Frequently asked questions

Which is better, DeepSeek V4 Pro 0813 or Kimi K3?

Kimi K3 has the higher public score, 80.21 versus 60.93, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.

Which is better for coding, DeepSeek V4 Pro 0813 or Kimi K3?

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 0813 or Kimi K3?

Kimi K3 leads the like-for-like agentic tasks comparison across 2 shared weighted benchmark rows.

Which costs less, DeepSeek V4 Pro 0813 or Kimi K3?

For the stated presets, chat costs $0.00087 on DeepSeek V4 Pro 0813 and $0.0105 on Kimi K3; repository review costs $0.02436 and $0.195; the cache-heavy agent loop costs $0.01812 and $0.27. Costs use the listed standard API rates.

Which has the larger context window, DeepSeek V4 Pro 0813 or Kimi K3?

Kimi K3 has the larger documented context window: 1.05M, compared with 1M.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 13, 2026

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