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BenchLM
Model A
DeepSeek-R1

DeepSeek

—

Evidence status unavailable

90% interval unavailable

DeepSeek-R1 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

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.

0 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

    DeepSeek V4 Flash 0731

    DeepSeek V4 Flash 0731 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

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

    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-R1 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
0
DeepSeek-R1 only
0
DeepSeek V4 Flash 0731 only
33
Like-for-like categories
0 / 8

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

Not comparable
DeepSeek-R1
Not measured
DeepSeek V4 Flash 0731
63.8
Weighted basis
0 vs 2 rows
Reading
Not comparable

Coding

Not comparable
DeepSeek-R1
Not measured
DeepSeek V4 Flash 0731
68.8
Weighted basis
0 vs 2 rows
Reading
Not comparable

Reasoning

Not comparable
DeepSeek-R1
Not measured
DeepSeek V4 Flash 0731
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
DeepSeek-R1
Not measured
DeepSeek V4 Flash 0731
55.3
Weighted basis
0 vs 4 rows
Reading
Not comparable

Math

Not comparable
DeepSeek-R1
Not measured
DeepSeek V4 Flash 0731
94.8
Weighted basis
0 vs 1 rows
Reading
Not comparable

Multilingual

Not comparable
DeepSeek-R1
Not measured
DeepSeek V4 Flash 0731
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
DeepSeek-R1
Not measured
DeepSeek V4 Flash 0731
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
DeepSeek-R1
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.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

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-R1
$0.00165
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

DeepSeek-R1
$0.03407
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

DeepSeek-R1
$0.0609
Does not fit in one request
DeepSeek V4 Flash 0731
$0.00616
Fits in one request

DeepSeek-R1 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-R1

128K

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.

DeepSeek-R1

$0.14 per 1M cached input tokens

DeepSeek V4 Flash 0731

$0.0028 per 1M cached input tokens

Reasoning profile

DeepSeek-R1

Reasoning

DeepSeek V4 Flash 0731

Reasoning

Weight access

DeepSeek-R1

Open Weight

DeepSeek V4 Flash 0731

Proprietary

License

DeepSeek-R1

Open Weight

DeepSeek V4 Flash 0731

Proprietary

Release date

DeepSeek-R1

2025-01-20

DeepSeek V4 Flash 0731

2026-07-31

If you already use one of these models
Deployment change
Both entries list DeepSeek as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
Quality signal
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
Repository review: $0.03407 vs $0.00784. Cache-heavy agent loop: $0.0609 vs $0.00616.
Context tradeoff
DeepSeek V4 Flash 0731 has the larger documented window (1M).

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-R1
API / mo$2,055
Self-host / mo$18,221
Break-even583M/day
DeepSeek V4 Flash 0731
API / mo$0
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 evidence33 rows

Agentic

  • Terminal-Bench 2.0

    DeepSeek-R1—
    DeepSeek V4 Flash 073156.9%
    Source

    Not directly comparable

  • BrowseComp

    DeepSeek-R1—
    DeepSeek V4 Flash 073173.2%
    Source

    Not directly comparable

  • HLE w/ tools

    DeepSeek-R1—
    DeepSeek V4 Flash 073145.1%
    Source

    Not directly comparable

  • MCP Atlas

    DeepSeek-R1—
    DeepSeek V4 Flash 073169%
    Source

    Not directly comparable

  • Toolathlon

    DeepSeek-R1—
    DeepSeek V4 Flash 073147.8%
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    DeepSeek-R1—
    DeepSeek V4 Flash 073182.7%
    Source

    Not directly comparable

  • CyberGym

    DeepSeek-R1—
    DeepSeek V4 Flash 073176.7%
    Source

    Not directly comparable

  • Toolathlon-Verified

    DeepSeek-R1—
    DeepSeek V4 Flash 073170.3%
    Source

    Not directly comparable

  • Agents' Last Exam

    DeepSeek-R1—
    DeepSeek V4 Flash 073125.2%
    Source

    Not directly comparable

  • AutomationBench

    DeepSeek-R1—
    DeepSeek V4 Flash 073125.1%
    Source

    Not directly comparable

Coding

  • LiveCodeBench Pass@1-COT

    DeepSeek-R1—
    DeepSeek V4 Flash 073191.6%
    Source

    Not directly comparable

  • Codeforces

    DeepSeek-R1—
    DeepSeek V4 Flash 07313052.0
    Source

    Not directly comparable

  • SWE-bench Verified

    DeepSeek-R1—
    DeepSeek V4 Flash 073179%
    Source

    Not directly comparable

  • SWE-bench Pro

    DeepSeek-R1—
    DeepSeek V4 Flash 073152.6%
    Source

    Not directly comparable

  • SWE Multilingual

    DeepSeek-R1—
    DeepSeek V4 Flash 073173.3%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    DeepSeek-R1—
    DeepSeek V4 Flash 073156.9%
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    DeepSeek-R1—
    DeepSeek V4 Flash 073182.7%
    Source

    Not directly comparable

  • NL2Repo

    DeepSeek-R1—
    DeepSeek V4 Flash 073154.2%
    Source

    Not directly comparable

  • deepSwe

    DeepSeek-R1—
    DeepSeek V4 Flash 073154.4%
    Source

    Not directly comparable

  • DSBench-FullStack

    DeepSeek-R1—
    DeepSeek V4 Flash 073168.7%
    Source

    Not directly comparable

  • DSBench-Hard

    DeepSeek-R1—
    DeepSeek V4 Flash 073159.6%
    Source

    Not directly comparable

Reasoning

  • MRCR 1M

    DeepSeek-R1—
    DeepSeek V4 Flash 073178.7%
    Source

    Not directly comparable

  • CorpusQA 1M

    DeepSeek-R1—
    DeepSeek V4 Flash 073160.5%
    Source

    Not directly comparable

Knowledge

  • MMLU-Pro

    DeepSeek-R1—
    DeepSeek V4 Flash 073186.2%
    Source

    Not directly comparable

  • SimpleQA

    DeepSeek-R1—
    DeepSeek V4 Flash 073134.1%
    Source

    Not directly comparable

  • Chinese-SimpleQA

    DeepSeek-R1—
    DeepSeek V4 Flash 073178.9%
    Source

    Not directly comparable

  • GPQA

    DeepSeek-R1—
    DeepSeek V4 Flash 073188.1%
    Source

    Not directly comparable

  • GPQA-D

    DeepSeek-R1—
    DeepSeek V4 Flash 073188.1%
    Source

    Not directly comparable

  • HLE

    DeepSeek-R1—
    DeepSeek V4 Flash 073134.8%
    Source

    Not directly comparable

Math

  • HMMT Feb 2026

    DeepSeek-R1—
    DeepSeek V4 Flash 073194.8%
    Source

    Not directly comparable

  • IMOAnswerBench

    DeepSeek-R1—
    DeepSeek V4 Flash 073188.4%
    Source

    Not directly comparable

  • Apex

    DeepSeek-R1—
    DeepSeek V4 Flash 073133.0%
    Source

    Not directly comparable

  • Apex Shortlist

    DeepSeek-R1—
    DeepSeek V4 Flash 073185.7%
    Source

    Not directly comparable

Frequently asked questions

Which is better, DeepSeek-R1 or DeepSeek V4 Flash 0731?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, DeepSeek-R1 or DeepSeek V4 Flash 0731?

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-R1 or DeepSeek V4 Flash 0731?

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-R1 or DeepSeek V4 Flash 0731?

For the stated presets, chat costs $0.00164 on DeepSeek-R1 and $0.00028 on DeepSeek V4 Flash 0731; repository review costs $0.03407 and $0.00784; the cache-heavy agent loop costs $0.0609 and $0.00616. DeepSeek-R1 does not fit this workload in one request.

Which has the larger context window, DeepSeek-R1 or DeepSeek V4 Flash 0731?

DeepSeek V4 Flash 0731 has the larger documented context window: 1M, compared with 128K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 13, 2026

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