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BenchLM
Model A
DeepSeek V4 Flash 0731

DeepSeek

—

Evidence status unavailable

90% interval unavailable

DeepSeek V4 Flash 0731 vs GPT-5.4 Pro

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

Model B
GPT-5.4 Pro

OpenAI

60.3/100

Estimated · Public rank #52

90% interval 43.4–77.2

Decision reading

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.

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 Pro

    GPT-5.4 Pro has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    DeepSeek V4 Flash 0731

    DeepSeek V4 Flash 0731 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

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

    DeepSeek V4 Flash 0731

    DeepSeek V4 Flash 0731 has the lower estimated token cost for this stated workload. GPT-5.4 Pro has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

Show secondary and unsupported calls

  • Repository review cost

    50K fresh input + 3K output tokens

    DeepSeek V4 Flash 0731

    DeepSeek V4 Flash 0731 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

    The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.

    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
2
DeepSeek V4 Flash 0731 only
31
GPT-5.4 Pro only
9
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.

Agentic

Directional only
DeepSeek V4 Flash 0731
63.8
GPT-5.4 Pro
89.3
Weighted basis
2 vs 1 rows
Reading
Directional only

Knowledge

Directional only
DeepSeek V4 Flash 0731
55.3
GPT-5.4 Pro
58.7
Weighted basis
4 vs 1 rows
Reading
Directional only

Coding

Not comparable
DeepSeek V4 Flash 0731
68.8
GPT-5.4 Pro
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
DeepSeek V4 Flash 0731
Not measured
GPT-5.4 Pro
83.3
Weighted basis
0 vs 1 rows
Reading
Not comparable

Math

Not comparable
DeepSeek V4 Flash 0731
94.8
GPT-5.4 Pro
46.9
Weighted basis
1 vs 2 rows
Reading
Not comparable

Multilingual

Not comparable
DeepSeek V4 Flash 0731
Not measured
GPT-5.4 Pro
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
DeepSeek V4 Flash 0731
Not measured
GPT-5.4 Pro
94.0
Weighted basis
0 vs 1 rows
Reading
Not comparable

Instruction following

Not comparable
DeepSeek V4 Flash 0731
Not measured
GPT-5.4 Pro
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 Flash 0731
$0.00028
Fits in one request
GPT-5.4 Pro
$0.12
Fits in one request

DeepSeek V4 Flash 0731 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

DeepSeek V4 Flash 0731
$0.00784
Fits in one request
GPT-5.4 Pro
$2.04
Fits in one request

DeepSeek V4 Flash 0731 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 Flash 0731
$0.00616
Fits in one request
GPT-5.4 Pro
$8.40
Fits in one request
Cached input priced at the published list-input rate

DeepSeek V4 Flash 0731 has the lower modeled cost

GPT-5.4 Pro 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.

Cached-input rate

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

DeepSeek V4 Flash 0731

$0.0028 per 1M cached input tokens

GPT-5.4 Pro

Not published

OpenAI pricing

Reasoning profile

DeepSeek V4 Flash 0731

Reasoning

GPT-5.4 Pro

Reasoning

Weight access

DeepSeek V4 Flash 0731

Proprietary

GPT-5.4 Pro

Proprietary

License

DeepSeek V4 Flash 0731

Proprietary

GPT-5.4 Pro

Proprietary

Release date

DeepSeek V4 Flash 0731

2026-07-31

GPT-5.4 Pro

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
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
Workload cost
Repository review: $0.00784 vs $2.04. Cache-heavy agent loop: $0.00616 vs $8.40.
Context tradeoff
GPT-5.4 Pro 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 evidence42 rows

Agentic

  • Terminal-Bench 2.0

    DeepSeek V4 Flash 073156.9%
    Source
    GPT-5.4 Pro—

    Not directly comparable

  • BrowseComp

    DeepSeek V4 Flash 073173.2%
    Source
    GPT-5.4 Pro89.3%
    Source

    GPT-5.4 Pro leads this result

  • HLE w/ tools

    DeepSeek V4 Flash 073145.1%
    Source
    GPT-5.4 Pro—

    Not directly comparable

  • MCP Atlas

    DeepSeek V4 Flash 073169%
    Source
    GPT-5.4 Pro—

    Not directly comparable

  • Toolathlon

    DeepSeek V4 Flash 073147.8%
    Source
    GPT-5.4 Pro—

    Not directly comparable

  • Terminal-Bench 2.1

    DeepSeek V4 Flash 073182.7%
    Source
    GPT-5.4 Pro—

    Not directly comparable

  • CyberGym

    DeepSeek V4 Flash 073176.7%
    Source
    GPT-5.4 Pro—

    Not directly comparable

  • Toolathlon-Verified

    DeepSeek V4 Flash 073170.3%
    Source
    GPT-5.4 Pro—

    Not directly comparable

  • Agents' Last Exam

    DeepSeek V4 Flash 073125.2%
    Source
    GPT-5.4 Pro—

    Not directly comparable

  • AutomationBench

    DeepSeek V4 Flash 073125.1%
    Source
    GPT-5.4 Pro—

    Not directly comparable

Coding

  • LiveCodeBench Pass@1-COT

    DeepSeek V4 Flash 073191.6%
    Source
    GPT-5.4 Pro—

    Not directly comparable

  • Codeforces

    DeepSeek V4 Flash 07313052.0
    Source
    GPT-5.4 Pro—

    Not directly comparable

  • SWE-bench Verified

    DeepSeek V4 Flash 073179%
    Source
    GPT-5.4 Pro—

    Not directly comparable

  • SWE-bench Pro

    DeepSeek V4 Flash 073152.6%
    Source
    GPT-5.4 Pro—

    Not directly comparable

  • SWE Multilingual

    DeepSeek V4 Flash 073173.3%
    Source
    GPT-5.4 Pro—

    Not directly comparable

  • Terminal-Bench 2.0

    DeepSeek V4 Flash 073156.9%
    Source
    GPT-5.4 Pro—

    Not directly comparable

  • Terminal-Bench 2.1

    DeepSeek V4 Flash 073182.7%
    Source
    GPT-5.4 Pro—

    Not directly comparable

  • NL2Repo

    DeepSeek V4 Flash 073154.2%
    Source
    GPT-5.4 Pro—

    Not directly comparable

  • deepSwe

    DeepSeek V4 Flash 073154.4%
    Source
    GPT-5.4 Pro—

    Not directly comparable

  • DSBench-FullStack

    DeepSeek V4 Flash 073168.7%
    Source
    GPT-5.4 Pro—

    Not directly comparable

  • DSBench-Hard

    DeepSeek V4 Flash 073159.6%
    Source
    GPT-5.4 Pro—

    Not directly comparable

Reasoning

  • MRCR 1M

    DeepSeek V4 Flash 073178.7%
    Source
    GPT-5.4 Pro—

    Not directly comparable

  • CorpusQA 1M

    DeepSeek V4 Flash 073160.5%
    Source
    GPT-5.4 Pro—

    Not directly comparable

  • ARC-AGI-2

    DeepSeek V4 Flash 0731—
    GPT-5.4 Pro83.3%
    Source

    Not directly comparable

Knowledge

  • MMLU-Pro

    DeepSeek V4 Flash 073186.2%
    Source
    GPT-5.4 Pro—

    Not directly comparable

  • SimpleQA

    DeepSeek V4 Flash 073134.1%
    Source
    GPT-5.4 Pro—

    Not directly comparable

  • Chinese-SimpleQA

    DeepSeek V4 Flash 073178.9%
    Source
    GPT-5.4 Pro—

    Not directly comparable

  • GPQA

    DeepSeek V4 Flash 073188.1%
    Source
    GPT-5.4 Pro—

    Not directly comparable

  • GPQA-D

    DeepSeek V4 Flash 073188.1%
    Source
    GPT-5.4 Pro—

    Not directly comparable

  • HLE

    DeepSeek V4 Flash 073134.8%
    Source
    GPT-5.4 Pro58.7%
    Source

    GPT-5.4 Pro leads this result

  • FrontierScience

    DeepSeek V4 Flash 0731—
    GPT-5.4 Pro36.7%
    Source

    Not directly comparable

  • FrontierScience Research

    DeepSeek V4 Flash 0731—
    GPT-5.4 Pro36.7%
    Source

    Not directly comparable

  • HLE w/o tools

    DeepSeek V4 Flash 0731—
    GPT-5.4 Pro42.7%
    Source

    Not directly comparable

Math

  • HMMT Feb 2026

    DeepSeek V4 Flash 073194.8%
    Source
    GPT-5.4 Pro—

    Not directly comparable

  • IMOAnswerBench

    DeepSeek V4 Flash 073188.4%
    Source
    GPT-5.4 Pro—

    Not directly comparable

  • Apex

    DeepSeek V4 Flash 073133.0%
    Source
    GPT-5.4 Pro—

    Not directly comparable

  • Apex Shortlist

    DeepSeek V4 Flash 073185.7%
    Source
    GPT-5.4 Pro—

    Not directly comparable

  • IPhO 2025 (Theory)

    DeepSeek V4 Flash 0731—
    GPT-5.4 Pro93.5%
    Source

    Not directly comparable

  • FrontierMath (legacy)

    DeepSeek V4 Flash 0731—
    GPT-5.4 Pro50%
    Source

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    DeepSeek V4 Flash 0731—
    GPT-5.4 Pro50.000%
    Source

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    DeepSeek V4 Flash 0731—
    GPT-5.4 Pro37.500%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    DeepSeek V4 Flash 0731—
    GPT-5.4 Pro94%
    Source

    Not directly comparable

Frequently asked questions

Which is better, DeepSeek V4 Flash 0731 or GPT-5.4 Pro?

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, DeepSeek V4 Flash 0731 or GPT-5.4 Pro?

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 Flash 0731 or GPT-5.4 Pro?

The current agentic tasks 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 costs less, DeepSeek V4 Flash 0731 or GPT-5.4 Pro?

For the stated presets, chat costs $0.00028 on DeepSeek V4 Flash 0731 and $0.12 on GPT-5.4 Pro; repository review costs $0.00784 and $2.04; the cache-heavy agent loop costs $0.00616 and $8.40. GPT-5.4 Pro has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, DeepSeek V4 Flash 0731 or GPT-5.4 Pro?

GPT-5.4 Pro 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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