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BenchLM
Model A
Claude Opus 4.6

Anthropic

67.7/100

Supported · Public rank #20

90% interval 54.5–81.0

Claude Opus 4.6 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.

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.

  • 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. Claude Opus 4.6 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

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

    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

  • 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
9
Claude Opus 4.6 only
24
DeepSeek V4 Flash 0731 only
24
Like-for-like categories
0 / 8

3 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
Claude Opus 4.6
73.0
DeepSeek V4 Flash 0731
63.8
Weighted basis
3 vs 2 rows
Reading
Directional only

Coding

Directional only
Claude Opus 4.6
68.1
DeepSeek V4 Flash 0731
68.8
Weighted basis
3 vs 2 rows
Reading
Directional only

Knowledge

Directional only
Claude Opus 4.6
69.1
DeepSeek V4 Flash 0731
55.3
Weighted basis
4 vs 4 rows
Reading
Directional only

Reasoning

Not comparable
Claude Opus 4.6
Not measured
DeepSeek V4 Flash 0731
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
Claude Opus 4.6
36.3
DeepSeek V4 Flash 0731
94.8
Weighted basis
2 vs 1 rows
Reading
Not comparable

Multilingual

Not comparable
Claude Opus 4.6
Not measured
DeepSeek V4 Flash 0731
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Claude Opus 4.6
77.3
DeepSeek V4 Flash 0731
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Claude Opus 4.6
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 Opus 4.6
$0.0175
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 Opus 4.6
$0.325
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 Opus 4.6
$1.35
Fits in one request
Cached input priced at the published list-input rate
DeepSeek V4 Flash 0731
$0.00616
Fits in one request

DeepSeek V4 Flash 0731 has the lower modeled cost

Claude Opus 4.6 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.

Context window

Maximum documented context; output-token limits may be lower.

Claude Opus 4.6

1M

DeepSeek V4 Flash 0731

1M

DeepSeek models and pricing

Cached-input rate

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

Claude Opus 4.6

Not published

DeepSeek V4 Flash 0731

$0.0028 per 1M cached input tokens

Reasoning profile

Claude Opus 4.6

Non-Reasoning

DeepSeek V4 Flash 0731

Reasoning

Weight access

Claude Opus 4.6

Proprietary

DeepSeek V4 Flash 0731

Proprietary

License

Claude Opus 4.6

Proprietary

DeepSeek V4 Flash 0731

Proprietary

Release date

Claude Opus 4.6

2026-02-01

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: $0.325 vs $0.00784. Cache-heavy agent loop: $1.35 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 evidence57 rows

Agentic

  • Terminal-Bench 2.0

    Claude Opus 4.665.4%
    Source
    DeepSeek V4 Flash 073156.9%
    Source

    Claude Opus 4.6 leads this result

  • BrowseComp

    Claude Opus 4.683.7%
    Source
    DeepSeek V4 Flash 073173.2%
    Source

    Claude Opus 4.6 leads this result

  • OSWorld-Verified

    Claude Opus 4.672.7%
    Source
    DeepSeek V4 Flash 0731—

    Not directly comparable

  • Claw-Eval

    Claude Opus 4.670.4%
    Source
    DeepSeek V4 Flash 0731—

    Not directly comparable

  • DeepSearchQA

    Claude Opus 4.673.7%
    Source
    DeepSeek V4 Flash 0731—

    Not directly comparable

  • CyberGym

    Claude Opus 4.666.6%
    Source
    DeepSeek V4 Flash 073176.7%
    Source

    DeepSeek V4 Flash 0731 leads this result

  • Gert Labs

    Claude Opus 4.661.85%
    Source
    DeepSeek V4 Flash 0731—

    Not directly comparable

  • ResearchClawBench

    Claude Opus 4.619.9%
    Source
    DeepSeek V4 Flash 0731—

    Not directly comparable

  • JobBench

    Claude Opus 4.636.7%
    Source
    DeepSeek V4 Flash 0731—

    Not directly comparable

  • HLE w/ tools

    Claude Opus 4.6—
    DeepSeek V4 Flash 073145.1%
    Source

    Not directly comparable

  • MCP Atlas

    Claude Opus 4.6—
    DeepSeek V4 Flash 073169%
    Source

    Not directly comparable

  • Toolathlon

    Claude Opus 4.6—
    DeepSeek V4 Flash 073147.8%
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    Claude Opus 4.6—
    DeepSeek V4 Flash 073182.7%
    Source

    Not directly comparable

  • Toolathlon-Verified

    Claude Opus 4.6—
    DeepSeek V4 Flash 073170.3%
    Source

    Not directly comparable

  • Agents' Last Exam

    Claude Opus 4.6—
    DeepSeek V4 Flash 073125.2%
    Source

    Not directly comparable

  • AutomationBench

    Claude Opus 4.6—
    DeepSeek V4 Flash 073125.1%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Claude Opus 4.680.8%
    Source
    DeepSeek V4 Flash 073179%
    Source

    Claude Opus 4.6 leads this result

  • SWE-bench Verified*

    Claude Opus 4.675.6%
    Source
    DeepSeek V4 Flash 0731—

    Not directly comparable

  • LiveCodeBench Pro

    Claude Opus 4.670.7%
    Source
    DeepSeek V4 Flash 0731—

    Not directly comparable

  • SWE-bench Pro

    Claude Opus 4.653.4%
    Source
    DeepSeek V4 Flash 073152.6%
    Source

    Claude Opus 4.6 leads this result

  • SWE-Rebench

    Claude Opus 4.665.3%
    Source
    DeepSeek V4 Flash 0731—

    Not directly comparable

  • React Native Evals

    Claude Opus 4.684.1%
    Source
    DeepSeek V4 Flash 0731—

    Not directly comparable

  • Vibe Code Bench

    Claude Opus 4.657.57%
    Source
    DeepSeek V4 Flash 0731—

    Not directly comparable

  • FrontierCode 1.1 Main

    Claude Opus 4.626.9%
    Source
    DeepSeek V4 Flash 0731—

    Not directly comparable

  • LiveCodeBench Pass@1-COT

    Claude Opus 4.6—
    DeepSeek V4 Flash 073191.6%
    Source

    Not directly comparable

  • Codeforces

    Claude Opus 4.6—
    DeepSeek V4 Flash 07313052.0
    Source

    Not directly comparable

  • SWE Multilingual

    Claude Opus 4.6—
    DeepSeek V4 Flash 073173.3%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    Claude Opus 4.6—
    DeepSeek V4 Flash 073156.9%
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    Claude Opus 4.6—
    DeepSeek V4 Flash 073182.7%
    Source

    Not directly comparable

  • NL2Repo

    Claude Opus 4.6—
    DeepSeek V4 Flash 073154.2%
    Source

    Not directly comparable

  • deepSwe

    Claude Opus 4.6—
    DeepSeek V4 Flash 073154.4%
    Source

    Not directly comparable

  • DSBench-FullStack

    Claude Opus 4.6—
    DeepSeek V4 Flash 073168.7%
    Source

    Not directly comparable

  • DSBench-Hard

    Claude Opus 4.6—
    DeepSeek V4 Flash 073159.6%
    Source

    Not directly comparable

Reasoning

  • MRCR 1M

    Claude Opus 4.6—
    DeepSeek V4 Flash 073178.7%
    Source

    Not directly comparable

  • CorpusQA 1M

    Claude Opus 4.6—
    DeepSeek V4 Flash 073160.5%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Claude Opus 4.691.3%
    Source
    DeepSeek V4 Flash 073188.1%
    Source

    Claude Opus 4.6 leads this result

  • GPQA-D

    Claude Opus 4.689.2%
    Source
    DeepSeek V4 Flash 073188.1%
    Source

    Claude Opus 4.6 leads this result

  • SuperGPQA

    Claude Opus 4.695%
    Source
    DeepSeek V4 Flash 0731—

    Not directly comparable

  • MMLU-Pro

    Claude Opus 4.682%
    Source
    DeepSeek V4 Flash 073186.2%
    Source

    DeepSeek V4 Flash 0731 leads this result

  • MMLU-Pro (Arcee)

    Claude Opus 4.689.1%
    Source
    DeepSeek V4 Flash 0731—

    Not directly comparable

  • HLE

    Claude Opus 4.653%
    Source
    DeepSeek V4 Flash 073134.8%
    Source

    Claude Opus 4.6 leads this result

  • HLE w/o tools

    Claude Opus 4.640%
    Source
    DeepSeek V4 Flash 0731—

    Not directly comparable

  • HealthBench Hard

    Claude Opus 4.614.8%
    Source
    DeepSeek V4 Flash 0731—

    Not directly comparable

  • MedXpertQA (Text)

    Claude Opus 4.652.1%
    Source
    DeepSeek V4 Flash 0731—

    Not directly comparable

  • SimpleQA

    Claude Opus 4.6—
    DeepSeek V4 Flash 073134.1%
    Source

    Not directly comparable

  • Chinese-SimpleQA

    Claude Opus 4.6—
    DeepSeek V4 Flash 073178.9%
    Source

    Not directly comparable

Math

  • AIME25 (Arcee)

    Claude Opus 4.699.8%
    Source
    DeepSeek V4 Flash 0731—

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    Claude Opus 4.640.700%
    Source
    DeepSeek V4 Flash 0731—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Claude Opus 4.622.900%
    Source
    DeepSeek V4 Flash 0731—

    Not directly comparable

  • HMMT Feb 2026

    Claude Opus 4.6—
    DeepSeek V4 Flash 073194.8%
    Source

    Not directly comparable

  • IMOAnswerBench

    Claude Opus 4.6—
    DeepSeek V4 Flash 073188.4%
    Source

    Not directly comparable

  • Apex

    Claude Opus 4.6—
    DeepSeek V4 Flash 073133.0%
    Source

    Not directly comparable

  • Apex Shortlist

    Claude Opus 4.6—
    DeepSeek V4 Flash 073185.7%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    Claude Opus 4.677.3%
    Source
    DeepSeek V4 Flash 0731—

    Not directly comparable

  • ERQA

    Claude Opus 4.651.6%
    Source
    DeepSeek V4 Flash 0731—

    Not directly comparable

  • ScreenSpot Pro

    Claude Opus 4.683.1%
    Source
    DeepSeek V4 Flash 0731—

    Not directly comparable

  • MedXpertQA (MM)

    Claude Opus 4.664.8%
    Source
    DeepSeek V4 Flash 0731—

    Not directly comparable

Frequently asked questions

Which is better, Claude Opus 4.6 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 Opus 4.6 or DeepSeek V4 Flash 0731?

The current coding 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 is better for agentic tasks, Claude Opus 4.6 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 Opus 4.6 or DeepSeek V4 Flash 0731?

For the stated presets, chat costs $0.0175 on Claude Opus 4.6 and $0.00028 on DeepSeek V4 Flash 0731; repository review costs $0.325 and $0.00784; the cache-heavy agent loop costs $1.35 and $0.00616. Claude Opus 4.6 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Claude Opus 4.6 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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