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BenchLM
Model A
Claude Sonnet 5

Anthropic

64.5/100

Estimated · Public rank #36

90% interval 50.2–78.8

Claude Sonnet 5 vs DeepSeek V4 Flash 0731

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

Model B
DeepSeek V4 Flash 0731

DeepSeek

—

Evidence status unavailable

90% interval unavailable

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.

8 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.

  • Coding work

    Code generation, repair, and software-engineering tasks

    Claude Sonnet 5

    Claude Sonnet 5 leads on the same 2 weighted benchmark rows.

    Confidence: limited

  • 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. 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 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

  • 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

  • Long documents

    Prompts that approach the documented context limit

    No clear pick

    The documented context windows are equal.

    Confidence: documented

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
8
Claude Sonnet 5 only
11
DeepSeek V4 Flash 0731 only
25
Like-for-like categories
1 / 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.

Coding

Like-for-like
Claude Sonnet 5
76.7
DeepSeek V4 Flash 0731
68.8
Weighted basis
2 vs 2 rows
Reading
Claude Sonnet 5 leads

Agentic

Directional only
Claude Sonnet 5
81.9
DeepSeek V4 Flash 0731
63.8
Weighted basis
3 vs 2 rows
Reading
Directional only

Knowledge

Directional only
Claude Sonnet 5
57.4
DeepSeek V4 Flash 0731
55.3
Weighted basis
1 vs 4 rows
Reading
Directional only

Reasoning

Not comparable
Claude Sonnet 5
Not measured
DeepSeek V4 Flash 0731
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
Claude Sonnet 5
Not measured
DeepSeek V4 Flash 0731
94.8
Weighted basis
0 vs 1 rows
Reading
Not comparable

Multilingual

Not comparable
Claude Sonnet 5
Not measured
DeepSeek V4 Flash 0731
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Claude Sonnet 5
88.3
DeepSeek V4 Flash 0731
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Claude Sonnet 5
Not measured
DeepSeek V4 Flash 0731
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

Claude Sonnet 5
$0.007
Fits in one request
DeepSeek V4 Flash 0731
$0.00028
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

Claude Sonnet 5
$0.13
Fits in one request
DeepSeek V4 Flash 0731
$0.00784
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

Claude Sonnet 5
$0.18
Fits in one request
DeepSeek V4 Flash 0731
$0.00616
Fits in one request

DeepSeek V4 Flash 0731 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.

Cached-input rate

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

Claude Sonnet 5

$0.2 per 1M cached input tokens

Claude API pricing

DeepSeek V4 Flash 0731

$0.0028 per 1M cached input tokens

Reasoning profile

Claude Sonnet 5

Reasoning

DeepSeek V4 Flash 0731

Reasoning

Weight access

Claude Sonnet 5

Proprietary

DeepSeek V4 Flash 0731

Proprietary

License

Claude Sonnet 5

Proprietary

DeepSeek V4 Flash 0731

Proprietary

Release date

Claude Sonnet 5

2026-06-30

DeepSeek V4 Flash 0731

2026-07-31

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: $0.13 vs $0.00784. Cache-heavy agent loop: $0.18 vs $0.00616.
Context tradeoff
Both models list 1M.

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 evidence44 rows

Agentic

  • Terminal-Bench 3.0

    Claude Sonnet 514.6%
    Source
    DeepSeek V4 Flash 0731—

    Not directly comparable

  • Terminal-Bench 2.0

    Claude Sonnet 580.4%
    Source
    DeepSeek V4 Flash 073156.9%
    Source

    Claude Sonnet 5 leads this result

  • BrowseComp

    Claude Sonnet 584.7%
    Source
    DeepSeek V4 Flash 073173.2%
    Source

    Claude Sonnet 5 leads this result

  • HLE w/ tools

    Claude Sonnet 557.4%
    Source
    DeepSeek V4 Flash 073145.1%
    Source

    Claude Sonnet 5 leads this result

  • OSWorld-Verified

    Claude Sonnet 581.2%
    Source
    DeepSeek V4 Flash 0731—

    Not directly comparable

  • MCP Atlas

    Claude Sonnet 5—
    DeepSeek V4 Flash 073169%
    Source

    Not directly comparable

  • Toolathlon

    Claude Sonnet 5—
    DeepSeek V4 Flash 073147.8%
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    Claude Sonnet 5—
    DeepSeek V4 Flash 073182.7%
    Source

    Not directly comparable

  • CyberGym

    Claude Sonnet 5—
    DeepSeek V4 Flash 073176.7%
    Source

    Not directly comparable

  • Toolathlon-Verified

    Claude Sonnet 5—
    DeepSeek V4 Flash 073170.3%
    Source

    Not directly comparable

  • Agents' Last Exam

    Claude Sonnet 5—
    DeepSeek V4 Flash 073125.2%
    Source

    Not directly comparable

  • AutomationBench

    Claude Sonnet 5—
    DeepSeek V4 Flash 073125.1%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Claude Sonnet 585.2%
    Source
    DeepSeek V4 Flash 073179%
    Source

    Claude Sonnet 5 leads this result

  • SWE-bench Pro

    Claude Sonnet 563.2%
    Source
    DeepSeek V4 Flash 073152.6%
    Source

    Claude Sonnet 5 leads this result

  • SWE Multilingual

    Claude Sonnet 578.3%
    Source
    DeepSeek V4 Flash 073173.3%
    Source

    Claude Sonnet 5 leads this result

  • SWE Multimodal

    Claude Sonnet 528.1%
    Source
    DeepSeek V4 Flash 0731—

    Not directly comparable

  • Terminal-Bench 2.0

    Claude Sonnet 580.4%
    Source
    DeepSeek V4 Flash 073156.9%
    Source

    Claude Sonnet 5 leads this result

  • FrontierCode 1.1 Main

    Claude Sonnet 542.7%
    Source
    DeepSeek V4 Flash 0731—

    Not directly comparable

  • cursorBench32

    Claude Sonnet 561.5%
    Source
    DeepSeek V4 Flash 0731—

    Not directly comparable

  • APEX-SWE

    Claude Sonnet 546.4%
    Source
    DeepSeek V4 Flash 0731—

    Not directly comparable

  • EEBench

    Claude Sonnet 540.3%
    Source
    DeepSeek V4 Flash 0731—

    Not directly comparable

  • 3DCodeBench

    Claude Sonnet 539.2%
    Source
    DeepSeek V4 Flash 0731—

    Not directly comparable

  • LiveCodeBench Pass@1-COT

    Claude Sonnet 5—
    DeepSeek V4 Flash 073191.6%
    Source

    Not directly comparable

  • Codeforces

    Claude Sonnet 5—
    DeepSeek V4 Flash 07313052.0
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    Claude Sonnet 5—
    DeepSeek V4 Flash 073182.7%
    Source

    Not directly comparable

  • NL2Repo

    Claude Sonnet 5—
    DeepSeek V4 Flash 073154.2%
    Source

    Not directly comparable

  • deepSwe

    Claude Sonnet 5—
    DeepSeek V4 Flash 073154.4%
    Source

    Not directly comparable

  • DSBench-FullStack

    Claude Sonnet 5—
    DeepSeek V4 Flash 073168.7%
    Source

    Not directly comparable

  • DSBench-Hard

    Claude Sonnet 5—
    DeepSeek V4 Flash 073159.6%
    Source

    Not directly comparable

Reasoning

  • MRCR 1M

    Claude Sonnet 5—
    DeepSeek V4 Flash 073178.7%
    Source

    Not directly comparable

  • CorpusQA 1M

    Claude Sonnet 5—
    DeepSeek V4 Flash 073160.5%
    Source

    Not directly comparable

Knowledge

  • HLE

    Claude Sonnet 557.4%
    Source
    DeepSeek V4 Flash 073134.8%
    Source

    Claude Sonnet 5 leads this result

  • HLE w/o tools

    Claude Sonnet 543.2%
    Source
    DeepSeek V4 Flash 0731—

    Not directly comparable

  • MMLU-Pro

    Claude Sonnet 5—
    DeepSeek V4 Flash 073186.2%
    Source

    Not directly comparable

  • SimpleQA

    Claude Sonnet 5—
    DeepSeek V4 Flash 073134.1%
    Source

    Not directly comparable

  • Chinese-SimpleQA

    Claude Sonnet 5—
    DeepSeek V4 Flash 073178.9%
    Source

    Not directly comparable

  • GPQA

    Claude Sonnet 5—
    DeepSeek V4 Flash 073188.1%
    Source

    Not directly comparable

  • GPQA-D

    Claude Sonnet 5—
    DeepSeek V4 Flash 073188.1%
    Source

    Not directly comparable

Math

  • HMMT Feb 2026

    Claude Sonnet 5—
    DeepSeek V4 Flash 073194.8%
    Source

    Not directly comparable

  • IMOAnswerBench

    Claude Sonnet 5—
    DeepSeek V4 Flash 073188.4%
    Source

    Not directly comparable

  • Apex

    Claude Sonnet 5—
    DeepSeek V4 Flash 073133.0%
    Source

    Not directly comparable

  • Apex Shortlist

    Claude Sonnet 5—
    DeepSeek V4 Flash 073185.7%
    Source

    Not directly comparable

Multimodal

  • CharXiv

    Claude Sonnet 588.3%
    Source
    DeepSeek V4 Flash 0731—

    Not directly comparable

  • CharXiv w/o tools

    Claude Sonnet 577%
    Source
    DeepSeek V4 Flash 0731—

    Not directly comparable

Frequently asked questions

Which is better, Claude Sonnet 5 or DeepSeek V4 Flash 0731?

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, Claude Sonnet 5 or DeepSeek V4 Flash 0731?

Claude Sonnet 5 leads the like-for-like coding comparison across 2 shared weighted benchmark rows.

Which is better for agentic tasks, Claude Sonnet 5 or DeepSeek V4 Flash 0731?

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, Claude Sonnet 5 or DeepSeek V4 Flash 0731?

For the stated presets, chat costs $0.007 on Claude Sonnet 5 and $0.00028 on DeepSeek V4 Flash 0731; repository review costs $0.13 and $0.00784; the cache-heavy agent loop costs $0.18 and $0.00616. Costs use the listed standard API rates.

Which has the larger context window, Claude Sonnet 5 or DeepSeek V4 Flash 0731?

Both models list the same context window, 1M.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 13, 2026

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