AI model comparison

DeepSeek V4 Flash 0731 vs Step 3.5 Flash

Compare recorded price, context, output limits, speed, latency, capabilities, and input/output support. Every missing value stays visible, and unsourced benchmarks are excluded.

DeepSeek V4 Flash 0731 and Step 3.5 Flash both come up when teams are choosing a model for production work, and the honest answer usually depends on your workload rather than a leaderboard. This page puts their published specifications side by side — context window, token pricing, supported inputs and outputs — so you can see where they actually differ.

Side-by-sideReviewed by our team

DeepSeek

DeepSeek V4 Flash 0731

Official DeepSeek V4 Flash release with enhanced agentic capabilities and integrated DSpark speculative decoding

Context
1M
Max output
384K
Input / 1M
$0.16
Output / 1M
$0.40
Output speed

Accepts

  • Text input

Produces

  • Text output
VS

StepFun

Step 3.5 Flash

StepFun flash lane for quick multimodal reasoning and coding assistance

Context
256K
Max output
256K
Input / 1M
$0.10
Output / 1M
$0.30
Output speed

Accepts

  • Text input

Produces

  • Text output

At a glance

Decision snapshot

This is a directional summary of the values recorded in this directory—not a universal quality verdict. Test the finalists on your own prompts before committing.

Where DeepSeek V4 Flash 0731 leads in recorded data

  • Context capacity: 1M
  • Maximum output: 384K

Where Step 3.5 Flash leads in recorded data

  • Input price: $0.10
  • Output price: $0.30

Context capacity

Higher listed limit

DeepSeek V4 Flash 0731:1M

Step 3.5 Flash:256K

DeepSeek V4 Flash 0731

Maximum output

Higher listed limit

DeepSeek V4 Flash 0731:384K

Step 3.5 Flash:256K

DeepSeek V4 Flash 0731

Input price

Lower recorded price

DeepSeek V4 Flash 0731:$0.16

Step 3.5 Flash:$0.10

Step 3.5 Flash

Output price

Lower recorded price

DeepSeek V4 Flash 0731:$0.40

Step 3.5 Flash:$0.30

Step 3.5 Flash

Output speed

Higher recorded throughput

DeepSeek V4 Flash 0731:

Step 3.5 Flash:

No complete comparison

Time to first token

Lower recorded latency

DeepSeek V4 Flash 0731:

Step 3.5 Flash:

No complete comparison

Intelligence Index

Higher directory index

DeepSeek V4 Flash 0731:

Step 3.5 Flash:

No complete comparison

Evidence

Verified shared benchmarks

A score appears only when both records include a verified source. It is not treated as a universal ranking.

These two models haven't been measured on the same benchmark yet, so there's nothing meaningful to chart. Comparing scores from different tests would tell you more about the tests than the models — the specs and pricing above are the better guide here.

Specifications

Pricing, limits and capabilities

SpecificationDeepSeek V4 Flash 0731Step 3.5 Flash
ProviderDeepSeekStepFun
Model familydeepseek-flash
Accepted inputstexttext
Produced outputstexttext
Context window1MRecorded lead256K
Max output384KRecorded lead256K
Intelligence Index
Input price / 1M$0.16$0.10Recorded lead
Output price / 1M$0.40$0.30Recorded lead
Output speed
Time to first token
Reasoning modeDocumentedDocumented
Tool callingDocumentedDocumented
Image inputNot documentedNot documented
Audio inputNot documentedNot documented
Structured outputDocumentedNot documented
LicenseOpen weightsOpen weights
ReleasedJul 31, 2026Jan 29, 2026

Workload guidance

Which model should you choose?

On the published numbers, DeepSeek V4 Flash 0731 accepts the larger context window and Step 3.5 Flash is cheaper per input token. Which matters more depends on whether your bottleneck is document size or spend — the table above has the exact figures.

Provenance

Sources and data limits

Prices and hosted performance can change. SyncDev records source references and clearly separates documented specifications from measured values; confirm commercial terms with the provider before deployment.

Questions people ask

It compares recorded context and output limits, token prices, output speed, time to first token, Intelligence Index, modality support, tool and structured-output capabilities, release information, and only source-linked benchmarks verified on both records. Missing information is labeled rather than inferred.

Compare a different pair

Choose any two approved model records and review them with the same evidence rules.

Open model comparison