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DeepSeek V4 Flash 0731 vs Step 3.7 Flash

Updated November 11, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

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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. 6 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
DeepSeek logo
DeepSeek V4 Flash 0731

DeepSeek

—

Evidence status unavailable

90% interval unavailable

Model B
StepFun logo
Step 3.7 Flash

StepFun

45.39/100

Estimated · Public rank #121

Conditional range 31.0–59.7

API price per 1M tokens

DeepSeek V4 Flash 0731: $0.14 input / $0.28 output

Step 3.7 Flash: $0.20 input / $1.15 output

Context window

DeepSeek V4 Flash 0731: 1M

Step 3.7 Flash: 256K

Released

DeepSeek V4 Flash 0731: July 31, 2026

Step 3.7 Flash: May 29, 2026

Shared results
6
DeepSeek V4 Flash 0731 only
36
Step 3.7 Flash only
5
Like-for-like categories
0 / 8
Estimated: Step 3.7 Flash. Conditional ranges do not establish rank confidence.How the comparison works

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

  • 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. Step 3.7 Flash 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

    DeepSeek V4 Flash 0731 is not ranked on the public lane for coding, so no winner is named for coding.

    Confidence: limited

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    DeepSeek V4 Flash 0731 is not ranked on the public lane for agentic, so no winner is named for agentic.

    Confidence: limited

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

—DeepSeek V4 Flash 073134.1Step 3.7 Flash

Not comparable · BenchAlign v5.8

The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboardShortlist models for this job
Same basis rules as the category table below

Or describe your own job

The LLM Selector reads what you need a model for and returns a shortlist, ranked in the same order the job pages publish.

Describe the task and constraints. This selector recommends models; it cannot carry out the task or access your files and accounts.

We keep what you type here, drafts included, for 180 days, and after that only the job and requirements read from it. Nothing is tied to your IP address or account. Leave out names and other personal details. Privacy

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

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.

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, reasoning, multimodal, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.

Agentic

Not comparable
DeepSeek V4 Flash 0731
Not ranked
Step 3.7 Flash
40.5
Estimated · #71/132
Basis
BenchAlign v5.8 lane · 12 vs 7 public rows
Reading
Not comparable

Coding

Not comparable
DeepSeek V4 Flash 0731
Not ranked
Step 3.7 Flash
34.1
Estimated · #93/158
Basis
BenchAlign v5.8 lane · 15 vs 2 public rows
Reading
Not comparable

Reasoning

Not comparable
DeepSeek V4 Flash 0731
Not ranked
Step 3.7 Flash
38.6
Estimated · #89/158
Basis
BenchAlign v5.8 lane · 4 vs 0 public rows
Reading
Not comparable

Multimodal

Not comparable
DeepSeek V4 Flash 0731
Not ranked
Step 3.7 Flash
Not ranked
Basis
BenchAlign v5.8 lane · 0 vs 2 public rows
Reading
Not comparable

Knowledge

Not comparable
DeepSeek V4 Flash 0731
Not ranked
Step 3.7 Flash
45.3
Estimated · #97/179
Basis
BenchAlign v5.8 lane · 7 vs 0 public rows
Reading
Not comparable

Multilingual

Not comparable
DeepSeek V4 Flash 0731
Not ranked
Step 3.7 Flash
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
DeepSeek V4 Flash 0731
Not ranked
Step 3.7 Flash
82.1
#60/157
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
DeepSeek V4 Flash 0731
83.4
Unranked · 4 rankable rows
Step 3.7 Flash
Not ranked
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, reasoning, multimodal, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Bars run 0–100Methodology

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
Step 3.7 Flash
$0.00077
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
Step 3.7 Flash
$0.01345
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
Step 3.7 Flash
$0.0555
Fits in one request
Cached input priced at the published list-input rate

DeepSeek V4 Flash 0731 has the lower modeled cost

Step 3.7 Flash has no published cached-input rate, so cached tokens use its listed input rate.

Your workload

Enter a month of tokens to price it at both models’ listed API rates.

DeepSeek V4 Flash 0731
$1.96
per month
Step 3.7 Flash
$4.30
per month

Standard listed rates, no batch or volume discounts.

Cached input falls back to the list input rate only where a cached rate is unpublished

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 V4 Flash 0731

1M

DeepSeek models and pricing

Step 3.7 Flash

256K

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

Step 3.7 Flash

Not published

Reasoning profile

DeepSeek V4 Flash 0731

Reasoning

Step 3.7 Flash

Reasoning

Weight access

DeepSeek V4 Flash 0731

Open Weight

Step 3.7 Flash

Open Weight

License

DeepSeek V4 Flash 0731

Open Weight

Step 3.7 Flash

Open Weight

Release date

DeepSeek V4 Flash 0731

2026-07-31

Step 3.7 Flash

2026-05-29

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 $0.01345. Cache-heavy agent loop: $0.00616 vs $0.0555.
Context tradeoff
DeepSeek V4 Flash 0731 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, DeepSeek V4 Flash 0731 or Step 3.7 Flash?

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 Step 3.7 Flash?

DeepSeek V4 Flash 0731 is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, DeepSeek V4 Flash 0731 or Step 3.7 Flash?

DeepSeek V4 Flash 0731 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, DeepSeek V4 Flash 0731 or Step 3.7 Flash?

For the stated presets, chat costs $0.00028 on DeepSeek V4 Flash 0731 and $0.00077 on Step 3.7 Flash; repository review costs $0.00784 and $0.01345; the cache-heavy agent loop costs $0.00616 and $0.0555. Step 3.7 Flash 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 Step 3.7 Flash?

DeepSeek V4 Flash 0731 has the larger documented context window: 1M, compared with 256K.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

DeepSeek V4 Flash 0731
API / mo$315
Self-host / mo$13,981
Break-evenNot reached at 70% load
Step 3.7 Flash
API / mo$1,012
Self-host / mo$3,504
Break-even173M/day
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 evidence47 rows

Agentic

  • Terminal-Bench 2.0

    DeepSeek V4 Flash 073156.9%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • BrowseComp

    DeepSeek V4 Flash 073173.2%
    Source
    Step 3.7 Flash75.8%
    Source

    Step 3.7 Flash leads this result

  • HLE w/ tools

    DeepSeek V4 Flash 073145.1%
    Source
    Step 3.7 Flash47.2%
    Source

    Step 3.7 Flash leads this result

  • MCP Atlas

    DeepSeek V4 Flash 073169%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • Toolathlon

    DeepSeek V4 Flash 073147.8%
    Source
    Step 3.7 Flash49.5%
    Source

    Step 3.7 Flash leads this result

  • Terminal-Bench 2.1

    DeepSeek V4 Flash 073182.7%
    Source
    Step 3.7 Flash59.5%
    Source

    DeepSeek V4 Flash 0731 leads this result

  • CyberGym

    DeepSeek V4 Flash 073176.7%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • Toolathlon-Verified

    DeepSeek V4 Flash 073170.3%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • Agents' Last Exam

    DeepSeek V4 Flash 073125.2%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • AutomationBench

    DeepSeek V4 Flash 073125.1%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    DeepSeek V4 Flash 073167.0%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • MazeBench

    DeepSeek V4 Flash 07310%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • DeepSearchQA

    DeepSeek V4 Flash 0731—
    Step 3.7 Flash92.8%
    Source

    Not directly comparable

  • Claw-Eval

    DeepSeek V4 Flash 0731—
    Step 3.7 Flash67.1%
    Source

    Not directly comparable

  • Gert Labs

    DeepSeek V4 Flash 0731—
    Step 3.7 Flash51.57%
    Source

    Not directly comparable

Coding

  • LiveCodeBench Pass@1-COT

    DeepSeek V4 Flash 073191.6%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • Codeforces

    DeepSeek V4 Flash 07313052.0
    Source
    Step 3.7 Flash—

    Not directly comparable

  • SWE-bench Verified

    DeepSeek V4 Flash 073179%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • SWE-bench Pro

    DeepSeek V4 Flash 073152.6%
    Source
    Step 3.7 Flash56.3%
    Source

    Step 3.7 Flash leads this result

  • SWE Multilingual

    DeepSeek V4 Flash 073173.3%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • Terminal-Bench 2.0

    DeepSeek V4 Flash 073156.9%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • Terminal-Bench 2.1

    DeepSeek V4 Flash 073182.7%
    Source
    Step 3.7 Flash59.5%
    Source

    DeepSeek V4 Flash 0731 leads this result

  • NL2Repo

    DeepSeek V4 Flash 073154.2%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • DeepSWE

    DeepSeek V4 Flash 073154.4%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • DSBench-FullStack

    DeepSeek V4 Flash 073168.7%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • DSBench-Hard

    DeepSeek V4 Flash 073159.6%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • VulcanBench v3

    DeepSeek V4 Flash 073188.4%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • OpenHarmony Bench

    DeepSeek V4 Flash 073153.8%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • LiveCodeBench (Vals)

    DeepSeek V4 Flash 073187.3%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • SWE-bench (Vals)

    DeepSeek V4 Flash 073188.8%
    Source
    Step 3.7 Flash—

    Not directly comparable

Reasoning

  • MRCR 1M

    DeepSeek V4 Flash 073178.7%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • CorpusQA 1M

    DeepSeek V4 Flash 073160.5%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • ARC-AGI-1

    DeepSeek V4 Flash 073189.00%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • ARC-AGI-2

    DeepSeek V4 Flash 073161.4%
    Source
    Step 3.7 Flash—

    Not directly comparable

Multimodal

  • SimpleVQA

    DeepSeek V4 Flash 0731—
    Step 3.7 Flash79.2%
    Source

    Not directly comparable

  • V*

    DeepSeek V4 Flash 0731—
    Step 3.7 Flash95.3%
    Source

    Not directly comparable

Knowledge

  • MMLU-Pro

    DeepSeek V4 Flash 073186.2%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • Chinese-SimpleQA

    DeepSeek V4 Flash 073178.9%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • GPQA

    DeepSeek V4 Flash 073188.1%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • GPQA-D

    DeepSeek V4 Flash 073188.1%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • HLE

    DeepSeek V4 Flash 073134.8%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • GPQA Diamond (Vals)

    DeepSeek V4 Flash 073189.9%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • MMLU-Pro (Vals)

    DeepSeek V4 Flash 073186.2%
    Source
    Step 3.7 Flash—

    Not directly comparable

Math

  • HMMT Feb 2026

    DeepSeek V4 Flash 073194.8%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • IMOAnswerBench

    DeepSeek V4 Flash 073188.4%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • Apex

    DeepSeek V4 Flash 073133.0%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • Apex Shortlist

    DeepSeek V4 Flash 073185.7%
    Source
    Step 3.7 Flash—

    Not directly comparable

47 public results · 6 shared

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Last updated November 11, 2026