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Model comparison

DeepSeek V3 vs GPT-5.4

Updated July 31, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

DeepSeek V3

DeepSeek

44.1/100

Supported · Public rank #155

90% interval 25.5–62.7

GPT-5.4

OpenAI

73.2/100

Supported · Public rank #10

90% interval 70.3–76.2

GPT-5.4 has the higher public score, 73.24 versus 44.11, and the 90% score intervals do not overlap.

2 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.

  • Long documents

    Prompts that approach the documented context limit

    GPT-5.4

    GPT-5.4 has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    DeepSeek V3

    DeepSeek V3 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 V3

    DeepSeek V3 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

  • 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

  • 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. DeepSeek V3 does not fit this workload in one request.

    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
2
DeepSeek V3 only
4
GPT-5.4 only
35
Like-for-like categories
0 / 8

2 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.

Knowledge

Directional only
DeepSeek V3
72.7
GPT-5.4
57.6
Weighted basis
2 vs 2 rows
Reading
Directional only

Math

Directional only
DeepSeek V3
1.7
GPT-5.4
42.5
Weighted basis
1 vs 2 rows
Reading
Directional only

Agentic

Not comparable
DeepSeek V3
Not measured
GPT-5.4
77.2
Weighted basis
0 vs 3 rows
Reading
Not comparable

Coding

Not comparable
DeepSeek V3
38.9
GPT-5.4
57.7
Weighted basis
2 vs 1 rows
Reading
Not comparable

Reasoning

Not comparable
DeepSeek V3
Not measured
GPT-5.4
74.0
Weighted basis
0 vs 1 rows
Reading
Not comparable

Multilingual

Not comparable
DeepSeek V3
Not measured
GPT-5.4
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
DeepSeek V3
Not measured
GPT-5.4
73.2
Weighted basis
0 vs 3 rows
Reading
Not comparable

Instruction following

Not comparable
DeepSeek V3
86.1
GPT-5.4
Not measured
Weighted basis
1 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 V3
$0.00082
Fits in one request
GPT-5.4
$0.01
Fits in one request

DeepSeek V3 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

DeepSeek V3
$0.0168
Fits in one request
GPT-5.4
$0.17
Fits in one request

DeepSeek V3 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 V3
$0.0304
Does not fit in one request
GPT-5.4
$0.25
Fits in one request

DeepSeek V3 does not fit this workload in one request.

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 V3

128K

GPT-5.4

1.05M

OpenAI pricing

Cached-input rate

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

DeepSeek V3

$0.07 per 1M cached input tokens

GPT-5.4

$0.25 per 1M cached input tokens

OpenAI pricing

Documented inputs

DeepSeek V3

Not sourced

GPT-5.4

Not sourced

Documented outputs

DeepSeek V3

Not sourced

GPT-5.4

Not sourced

Provider availability

DeepSeek V3

Not sourced

GPT-5.4

Not sourced

Reasoning profile

DeepSeek V3

Non-Reasoning

GPT-5.4

Reasoning

Weight access

DeepSeek V3

Open Weight

GPT-5.4

Proprietary

License

DeepSeek V3

Open Weight

GPT-5.4

Proprietary

Release date

DeepSeek V3

2024-12-26

GPT-5.4

2026-03-05

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
GPT-5.4 has the higher public score, 73.24 versus 44.11, and the 90% score intervals do not overlap.
Workload cost
Repository review: $0.0168 vs $0.17. Cache-heavy agent loop: $0.0304 vs $0.25.
Context tradeoff
GPT-5.4 has the larger documented window (1.05M).

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 V3
API / mo$1,028
Self-host / mo$18,221
Break-even1.2B/day
GPT-5.4
API / mo$13,125
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
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 evidence41 rows

Agentic

  • Terminal-Bench 2.0

    DeepSeek V3—
    GPT-5.475.1%
    Source

    Not directly comparable

  • CyberGym

    DeepSeek V3—
    GPT-5.479.0%
    Source

    Not directly comparable

  • BrowseComp

    DeepSeek V3—
    GPT-5.482.7%
    Source

    Not directly comparable

  • OSWorld-Verified

    DeepSeek V3—
    GPT-5.475%
    Source

    Not directly comparable

  • MCP Atlas

    DeepSeek V3—
    GPT-5.470.6%
    Source

    Not directly comparable

  • Toolathlon

    DeepSeek V3—
    GPT-5.454.6%
    Source

    Not directly comparable

  • τ²-bench results

    DeepSeek V3—
    GPT-5.498.9%
    Source

    Not directly comparable

  • Claw-Eval

    DeepSeek V3—
    GPT-5.460.3%
    Source

    Not directly comparable

  • DeepSearchQA

    DeepSeek V3—
    GPT-5.473.6%
    Source

    Not directly comparable

  • Gert Labs

    DeepSeek V3—
    GPT-5.464.89%
    Source

    Not directly comparable

  • ResearchClawBench

    DeepSeek V3—
    GPT-5.415.3%
    Source

    Not directly comparable

  • JobBench

    DeepSeek V3—
    GPT-5.438.9%
    Source

    Not directly comparable

  • ExploitGym

    DeepSeek V3—
    GPT-5.46.0%
    Source

    Not directly comparable

Coding

  • LiveCodeBench

    DeepSeek V337.6%
    Source
    GPT-5.4—

    Not directly comparable

  • SWE-bench Verified

    DeepSeek V342%
    Source
    GPT-5.4—

    Not directly comparable

  • LiveCodeBench Pro

    DeepSeek V3—
    GPT-5.487.5%
    Source

    Not directly comparable

  • SWE-bench Pro

    DeepSeek V3—
    GPT-5.457.7%
    Source

    Not directly comparable

  • React Native Evals

    DeepSeek V3—
    GPT-5.485.3%
    Source

    Not directly comparable

  • Vibe Code Bench

    DeepSeek V3—
    GPT-5.467.42%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    DeepSeek V3—
    GPT-5.474.0%
    Source

    Not directly comparable

  • ARC-AGI-3

    DeepSeek V3—
    GPT-5.40.2%
    Source

    Not directly comparable

Knowledge

  • GPQA

    DeepSeek V359.1%
    Source
    GPT-5.492.8%
    Source

    GPT-5.4 leads this result

  • MMLU-Pro

    DeepSeek V375.9%
    Source
    GPT-5.4—

    Not directly comparable

  • HLE

    DeepSeek V3—
    GPT-5.452.1%
    Source

    Not directly comparable

  • HLE w/o tools

    DeepSeek V3—
    GPT-5.439.8%
    Source

    Not directly comparable

  • GPQA-D

    DeepSeek V3—
    GPT-5.492.8%
    Source

    Not directly comparable

  • HealthBench Hard

    DeepSeek V3—
    GPT-5.440.1%
    Source

    Not directly comparable

  • MedXpertQA (Text)

    DeepSeek V3—
    GPT-5.459.6%
    Source

    Not directly comparable

  • HealthBench Professional

    DeepSeek V3—
    GPT-5.448.1%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Shared source
    DeepSeek V31.724%
    GPT-5.447.600%

    GPT-5.4 leads this result

  • FrontierMath v2 (Tier 4)

    DeepSeek V3—
    GPT-5.427.100%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    DeepSeek V3—
    GPT-5.481.2%
    Source

    Not directly comparable

  • OfficeQA Pro

    DeepSeek V3—
    GPT-5.453.2%
    Source

    Not directly comparable

  • MMMU-Pro w/ Python

    DeepSeek V3—
    GPT-5.482.1%
    Source

    Not directly comparable

  • CharXiv

    DeepSeek V3—
    GPT-5.482.8%
    Source

    Not directly comparable

  • ERQA

    DeepSeek V3—
    GPT-5.465.4%
    Source

    Not directly comparable

  • SimpleVQA

    DeepSeek V3—
    GPT-5.461.1%
    Source

    Not directly comparable

  • ScreenSpot Pro

    DeepSeek V3—
    GPT-5.485.4%
    Source

    Not directly comparable

  • ZeroBench

    DeepSeek V3—
    GPT-5.441.0%
    Source

    Not directly comparable

  • MedXpertQA (MM)

    DeepSeek V3—
    GPT-5.477.1%
    Source

    Not directly comparable

Instruction following

  • IFEval

    DeepSeek V386.1%
    Source
    GPT-5.4—

    Not directly comparable

Frequently asked questions

Which is better, DeepSeek V3 or GPT-5.4?

GPT-5.4 has the higher public score, 73.24 versus 44.11, 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 V3 or GPT-5.4?

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 V3 or GPT-5.4?

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 V3 or GPT-5.4?

For the stated presets, chat costs $0.00082 on DeepSeek V3 and $0.01 on GPT-5.4; repository review costs $0.0168 and $0.17; the cache-heavy agent loop costs $0.0304 and $0.25. DeepSeek V3 does not fit this workload in one request.

Which has the larger context window, DeepSeek V3 or GPT-5.4?

GPT-5.4 has the larger documented context window: 1.05M, compared with 128K.

Related comparisons

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Last updated July 31, 2026

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