Model A

DeepSeek

—

Evidence status unavailable

90% interval unavailable

DeepSeek V4 Flash 0731 vs Qwen3.5 397B

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

Model B

Qwen3.5 397B

Alibaba

56.5/100

Estimated · Public rank #80

90% interval 45.0–68.0

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.

10 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
DeepSeek V4 Flash 0731
DeepSeek V4 Flash 0731 leads on the same 2 weighted benchmark rows.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
DeepSeek V4 Flash 0731
DeepSeek V4 Flash 0731 leads on the same 2 weighted benchmark rows.
Confidence: limited
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

Show secondary and unsupported calls

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
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
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. Qwen3.5 397B does not fit this workload in one request. Qwen3.5 397B has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback

What is actually comparable

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

23 DeepSeek V4 Flash 0731 only

10 Shared

28 Qwen3.5 397B only

Shared results: 10
DeepSeek V4 Flash 0731 only: 23
Qwen3.5 397B only: 28
Like-for-like categories: 2 / 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.

Agentic

Like-for-like

DeepSeek V4 Flash 0731: 63.8
Qwen3.5 397B: 56.5
Weighted basis: 2 vs 2 rows
Reading: DeepSeek V4 Flash 0731 leads

Coding

Like-for-like

DeepSeek V4 Flash 0731: 68.8
Qwen3.5 397B: 66.5
Weighted basis: 2 vs 2 rows
Reading: DeepSeek V4 Flash 0731 leads

Knowledge

Directional only

DeepSeek V4 Flash 0731: 55.3
Qwen3.5 397B: 56.6
Weighted basis: 4 vs 4 rows
Reading: Directional only

Math

Directional only

DeepSeek V4 Flash 0731: 94.8
Qwen3.5 397B: 90.6
Weighted basis: 1 vs 2 rows
Reading: Directional only

Reasoning

Not comparable

DeepSeek V4 Flash 0731: Not measured
Qwen3.5 397B: 63.2
Weighted basis: 0 vs 1 rows
Reading: Not comparable

Multilingual

Not comparable

DeepSeek V4 Flash 0731: Not measured
Qwen3.5 397B: 84.7
Weighted basis: 0 vs 1 rows
Reading: Not comparable

Multimodal

Not comparable

DeepSeek V4 Flash 0731: Not measured
Qwen3.5 397B: 79.6
Weighted basis: 0 vs 2 rows
Reading: Not comparable

Instruction following

Not comparable

DeepSeek V4 Flash 0731: Not measured
Qwen3.5 397B: 92.6
Weighted basis: 0 vs 1 rows
Reading: Not comparable

Category averages with the server-provided evidence basis for DeepSeek V4 Flash 0731 and Qwen3.5 397B
Category	DeepSeek V4 Flash 0731	Qwen3.5 397B	Weighted basis	Reading
Agentic	63.8	56.5	Like-for-like2 vs 2 rows	DeepSeek V4 Flash 0731 leads
Coding	68.8	66.5	Like-for-like2 vs 2 rows	DeepSeek V4 Flash 0731 leads
Knowledge	55.3	56.6	Directional only4 vs 4 rows	Directional only
Math	94.8	90.6	Directional only1 vs 2 rows	Directional only
Reasoning	Not measured	63.2	Not comparable0 vs 1 rows	Not comparable
Multilingual	Not measured	84.7	Not comparable0 vs 1 rows	Not comparable
Multimodal	Not measured	79.6	Not comparable0 vs 2 rows	Not comparable
Instruction following	Not measured	92.6	Not comparable0 vs 1 rows	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.

BrowseComp
Agentic
DeepSeek V4 Flash 0731: 73.2%Qwen3.5 397B: 62%Normalized gap 11.2DeepSeek V4 Flash 0731 source Qwen3.5 397B source
HMMT Feb 2026
Math
DeepSeek V4 Flash 0731: 94.8%Qwen3.5 397B: 87.9%Normalized gap 6.9DeepSeek V4 Flash 0731 source Qwen3.5 397B source
HLE
Knowledge
DeepSeek V4 Flash 0731: 34.8%Qwen3.5 397B: 28.7%Normalized gap 6.1DeepSeek V4 Flash 0731 source Qwen3.5 397B source
Terminal-Bench 2.0
Agentic
DeepSeek V4 Flash 0731: 56.9%Qwen3.5 397B: 52.5%Normalized gap 4.4DeepSeek V4 Flash 0731 source Qwen3.5 397B source
SWE-bench Verified
Coding
DeepSeek V4 Flash 0731: 79%Qwen3.5 397B: 76.2%Normalized gap 2.8DeepSeek V4 Flash 0731 source Qwen3.5 397B source

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
Qwen3.5 397B: $0.0024; 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
Qwen3.5 397B: $0.0408; 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
Qwen3.5 397B: $0.168; Does not fit in one request; Cached input priced at the published list-input rate

Reasoning

Qwen3.5 397B

Non-Reasoning

Weight access

DeepSeek V4 Flash 0731

Proprietary

Qwen3.5 397B

Open Weight

License

DeepSeek V4 Flash 0731

Proprietary

Qwen3.5 397B

Open Weight

Release date

DeepSeek V4 Flash 0731

2026-07-31

Qwen3.5 397B

2026-02-16

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 $0.0408. Cache-heavy agent loop: $0.00616 vs $0.168.
Context tradeoff: DeepSeek V4 Flash 0731 has the larger documented window (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 evidence61 rows

Agentic

Terminal-Bench 2.0
DeepSeek V4 Flash 073156.9%
Source
Qwen3.5 397B52.5%
Source
DeepSeek V4 Flash 0731 leads this result
BrowseComp
DeepSeek V4 Flash 073173.2%
Source
Qwen3.5 397B62%
Source
DeepSeek V4 Flash 0731 leads this result
HLE w/ tools
DeepSeek V4 Flash 073145.1%
Source
Qwen3.5 397B—
Not directly comparable
MCP Atlas
DeepSeek V4 Flash 073169%
Source
Qwen3.5 397B46.1%
Source
DeepSeek V4 Flash 0731 leads this result
Toolathlon
DeepSeek V4 Flash 073147.8%
Source
Qwen3.5 397B36.3%
Source
DeepSeek V4 Flash 0731 leads this result
Terminal-Bench 2.1
DeepSeek V4 Flash 073182.7%
Source
Qwen3.5 397B—
Not directly comparable
CyberGym
DeepSeek V4 Flash 073176.7%
Source
Qwen3.5 397B—
Not directly comparable
Toolathlon-Verified
DeepSeek V4 Flash 073170.3%
Source
Qwen3.5 397B—
Not directly comparable
Agents' Last Exam
DeepSeek V4 Flash 073125.2%
Source
Qwen3.5 397B—
Not directly comparable
AutomationBench
DeepSeek V4 Flash 073125.1%
Source
Qwen3.5 397B—
Not directly comparable
Claw-Eval
DeepSeek V4 Flash 0731—
Qwen3.5 397B56.8%
Source
Not directly comparable
QwenClawBench
DeepSeek V4 Flash 0731—
Qwen3.5 397B51.8%
Source
Not directly comparable
τ³-bench results
DeepSeek V4 Flash 0731—
Qwen3.5 397B68.4%
Source
Not directly comparable
VITA-Bench
DeepSeek V4 Flash 0731—
Qwen3.5 397B43.7%
Source
Not directly comparable
DeepPlanning
DeepSeek V4 Flash 0731—
Qwen3.5 397B37.6%
Source
Not directly comparable
MCP-Tasks
DeepSeek V4 Flash 0731—
Qwen3.5 397B74.2%
Source
Not directly comparable
WideResearch
DeepSeek V4 Flash 0731—
Qwen3.5 397B74.0%
Source
Not directly comparable
Gert Labs
DeepSeek V4 Flash 0731—
Qwen3.5 397B46.76%
Source
Not directly comparable
ResearchClawBench
DeepSeek V4 Flash 0731—
Qwen3.5 397B14.2%
Source
Not directly comparable

Coding

LiveCodeBench Pass@1-COT
DeepSeek V4 Flash 073191.6%
Source
Qwen3.5 397B—
Not directly comparable
Codeforces
DeepSeek V4 Flash 07313052.0
Source
Qwen3.5 397B—
Not directly comparable
SWE-bench Verified
DeepSeek V4 Flash 073179%
Source
Qwen3.5 397B76.2%
Source
DeepSeek V4 Flash 0731 leads this result
SWE-bench Pro
DeepSeek V4 Flash 073152.6%
Source
Qwen3.5 397B50.9%
Source
DeepSeek V4 Flash 0731 leads this result
SWE Multilingual
DeepSeek V4 Flash 073173.3%
Source
Qwen3.5 397B—
Not directly comparable
Terminal-Bench 2.0
DeepSeek V4 Flash 073156.9%
Source
Qwen3.5 397B—
Not directly comparable
Terminal-Bench 2.1
DeepSeek V4 Flash 073182.7%
Source
Qwen3.5 397B—
Not directly comparable
NL2Repo
DeepSeek V4 Flash 073154.2%
Source
Qwen3.5 397B—
Not directly comparable
deepSwe
DeepSeek V4 Flash 073154.4%
Source
Qwen3.5 397B—
Not directly comparable
DSBench-FullStack
DeepSeek V4 Flash 073168.7%
Source
Qwen3.5 397B—
Not directly comparable
DSBench-Hard
DeepSeek V4 Flash 073159.6%
Source
Qwen3.5 397B—
Not directly comparable
LiveCodeBench v6
DeepSeek V4 Flash 0731—
Qwen3.5 397B83.6%
Source
Not directly comparable

Reasoning

MRCR 1M
DeepSeek V4 Flash 073178.7%
Source
Qwen3.5 397B—
Not directly comparable
CorpusQA 1M
DeepSeek V4 Flash 073160.5%
Source
Qwen3.5 397B—
Not directly comparable
LongBench v2
DeepSeek V4 Flash 0731—
Qwen3.5 397B63.2%
Source
Not directly comparable
AI-Needle
DeepSeek V4 Flash 0731—
Qwen3.5 397B68.7%
Source
Not directly comparable

Knowledge

MMLU-Pro
DeepSeek V4 Flash 073186.2%
Source
Qwen3.5 397B87.8%
Source
Qwen3.5 397B leads this result
SimpleQA
DeepSeek V4 Flash 073134.1%
Source
Qwen3.5 397B—
Not directly comparable
Chinese-SimpleQA
DeepSeek V4 Flash 073178.9%
Source
Qwen3.5 397B—
Not directly comparable
GPQA
DeepSeek V4 Flash 073188.1%
Source
Qwen3.5 397B88.4%
Source
Qwen3.5 397B leads this result
GPQA-D
DeepSeek V4 Flash 073188.1%
Source
Qwen3.5 397B—
Not directly comparable
HLE
DeepSeek V4 Flash 073134.8%
Source
Qwen3.5 397B28.7%
Source
DeepSeek V4 Flash 0731 leads this result
SuperGPQA
DeepSeek V4 Flash 0731—
Qwen3.5 397B70.4%
Source
Not directly comparable
MMLU-Redux
DeepSeek V4 Flash 0731—
Qwen3.5 397B94.9%
Source
Not directly comparable
C-Eval
DeepSeek V4 Flash 0731—
Qwen3.5 397B93%
Source
Not directly comparable

Math

HMMT Feb 2026
DeepSeek V4 Flash 073194.8%
Source
Qwen3.5 397B87.9%
Source
DeepSeek V4 Flash 0731 leads this result
IMOAnswerBench
DeepSeek V4 Flash 073188.4%
Source
Qwen3.5 397B—
Not directly comparable
Apex
DeepSeek V4 Flash 073133.0%
Source
Qwen3.5 397B—
Not directly comparable
Apex Shortlist
DeepSeek V4 Flash 073185.7%
Source
Qwen3.5 397B—
Not directly comparable
AIME26
DeepSeek V4 Flash 0731—
Qwen3.5 397B93.3%
Source
Not directly comparable
HMMT Feb 2025
DeepSeek V4 Flash 0731—
Qwen3.5 397B94.8%
Source
Not directly comparable
HMMT Nov 2025
DeepSeek V4 Flash 0731—
Qwen3.5 397B92.7%
Source
Not directly comparable
MMAnswerBench
DeepSeek V4 Flash 0731—
Qwen3.5 397B80.9%
Source
Not directly comparable

Multilingual

MMLU-ProX
DeepSeek V4 Flash 0731—
Qwen3.5 397B84.7%
Source
Not directly comparable
NOVA-63
DeepSeek V4 Flash 0731—
Qwen3.5 397B59.1%
Source
Not directly comparable

Multimodal

MMMU-Pro
DeepSeek V4 Flash 0731—
Qwen3.5 397B79%
Source
Not directly comparable
MathVision
DeepSeek V4 Flash 0731—
Qwen3.5 397B88.6%
Source
Not directly comparable
CharXiv
DeepSeek V4 Flash 0731—
Qwen3.5 397B80.8%
Source
Not directly comparable
VideoMMMU
DeepSeek V4 Flash 0731—
Qwen3.5 397B84.7%
Source
Not directly comparable
ScreenSpot Pro
DeepSeek V4 Flash 0731—
Qwen3.5 397B65.6%
Source
Not directly comparable
V*
DeepSeek V4 Flash 0731—
Qwen3.5 397B95.8%
Source
Not directly comparable

Instruction following

IFEval
DeepSeek V4 Flash 0731—
Qwen3.5 397B92.6%
Source
Not directly comparable

Frequently asked questions

Which is better, DeepSeek V4 Flash 0731 or Qwen3.5 397B?

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 Qwen3.5 397B?

DeepSeek V4 Flash 0731 leads the like-for-like coding comparison across 2 shared weighted benchmark rows.

Which is better for agentic tasks, DeepSeek V4 Flash 0731 or Qwen3.5 397B?

DeepSeek V4 Flash 0731 leads the like-for-like agentic tasks comparison across 2 shared weighted benchmark rows.

Which costs less, DeepSeek V4 Flash 0731 or Qwen3.5 397B?

For the stated presets, chat costs $0.00028 on DeepSeek V4 Flash 0731 and $0.0024 on Qwen3.5 397B; repository review costs $0.00784 and $0.0408; the cache-heavy agent loop costs $0.00616 and $0.168. Qwen3.5 397B does not fit this workload in one request. Qwen3.5 397B 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 Qwen3.5 397B?

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

Related comparisons

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Last updated August 13, 2026

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DeepSeek V4 Flash 0731 vs Qwen3.5 397B

Which one for your work

Coding work

Agentic work

Long documents

Chat turn cost

Repository review cost

Cache-heavy agent loop cost

What is actually comparable

Category results, on a stated basis

Agentic

Coding

Knowledge

Math

Reasoning

Multilingual

Multimodal

Instruction following

Shape of the matched evidence

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

Benchmark evidence

Agentic

Coding

Reasoning

Knowledge

Math

Multilingual

Multimodal

Instruction following

Frequently asked questions

Which is better, DeepSeek V4 Flash 0731 or Qwen3.5 397B?

Which is better for coding, DeepSeek V4 Flash 0731 or Qwen3.5 397B?

Which is better for agentic tasks, DeepSeek V4 Flash 0731 or Qwen3.5 397B?

Which costs less, DeepSeek V4 Flash 0731 or Qwen3.5 397B?

Which has the larger context window, DeepSeek V4 Flash 0731 or Qwen3.5 397B?

Related comparisons

Watch DeepSeek V4 Flash 0731 vs Qwen3.5 397B