Model comparison

DeepSeek V4 Pro vs Qwen3.8 Max

Updated August 3, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

DeepSeek V4 Pro

DeepSeek

59.9/100

Supported · Public rank #50

90% interval 41.5–78.3

Qwen3.8 Max

Alibaba

65.4/100

Estimated · Public rank #31

90% interval 55.5–75.3

Qwen3.8 Max has the higher public score estimate, 65.4 versus 59.91, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

4 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.

No workload recommendation clears the current evidence threshold.

Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.

Show secondary and unsupported calls

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
No shared weighted benchmark basis supports a winner.
Confidence: limited
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Repository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
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.

19 DeepSeek V4 Pro only

4 Shared

48 Qwen3.8 Max only

Shared results: 4
DeepSeek V4 Pro only: 19
Qwen3.8 Max only: 48
Like-for-like categories: 0 / 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.

Coding

Directional only

DeepSeek V4 Pro: 65.3
Qwen3.8 Max: 67.7
Weighted basis: 2 vs 1 rows
Reading: Directional only

Knowledge

Directional only

DeepSeek V4 Pro: 41.3
Qwen3.8 Max: 50.2
Weighted basis: 4 vs 2 rows
Reading: Directional only

Agentic

Not comparable

DeepSeek V4 Pro: 59.1
Qwen3.8 Max: 86.1
Weighted basis: 1 vs 1 rows
Reading: Not comparable

Reasoning

Not comparable

DeepSeek V4 Pro: Not measured
Qwen3.8 Max: 78.3
Weighted basis: 0 vs 2 rows
Reading: Not comparable

Math

Not comparable

DeepSeek V4 Pro: 31.7
Qwen3.8 Max: Not measured
Weighted basis: 1 vs 0 rows
Reading: Not comparable

Multilingual

Not comparable

DeepSeek V4 Pro: Not measured
Qwen3.8 Max: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Multimodal

Not comparable

DeepSeek V4 Pro: Not measured
Qwen3.8 Max: 86.3
Weighted basis: 0 vs 2 rows
Reading: Not comparable

Instruction following

Not comparable

DeepSeek V4 Pro: Not measured
Qwen3.8 Max: 82.8
Weighted basis: 0 vs 1 rows
Reading: Not comparable

Category averages with the server-provided evidence basis for DeepSeek V4 Pro and Qwen3.8 Max
Category	DeepSeek V4 Pro	Qwen3.8 Max	Weighted basis	Reading
Coding	65.3	67.7	Directional only2 vs 1 rows	Directional only
Knowledge	41.3	50.2	Directional only4 vs 2 rows	Directional only
Agentic	59.1	86.1	Not comparable1 vs 1 rows	Not comparable
Reasoning	Not measured	78.3	Not comparable0 vs 2 rows	Not comparable
Math	31.7	Not measured	Not comparable1 vs 0 rows	Not comparable
Multilingual	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable
Multimodal	Not measured	86.3	Not comparable0 vs 2 rows	Not comparable
Instruction following	Not measured	82.8	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.

HLE
Knowledge
DeepSeek V4 Pro: 7.7%Qwen3.8 Max: 43.6%Normalized gap 35.9DeepSeek V4 Pro source Qwen3.8 Max source
GPQA
Knowledge
DeepSeek V4 Pro: 72.9%Qwen3.8 Max: 92.6%Normalized gap 19.7DeepSeek V4 Pro source Qwen3.8 Max source
SWE-bench Pro
Coding
DeepSeek V4 Pro: 52.1%Qwen3.8 Max: 67.7%Normalized gap 15.6DeepSeek V4 Pro source Qwen3.8 Max 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 Pro: $0.00087; Fits in one request
Qwen3.8 Max: API rate not published; Fits in one request

Qwen3.8 Max has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

DeepSeek V4 Pro: $0.02436; Fits in one request
Qwen3.8 Max: API rate not published; Fits in one request

Qwen3.8 Max has no comparable published API token rate.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

DeepSeek V4 Pro: $0.01812; Fits in one request
Qwen3.8 Max: API rate not published; Fits in one request; Cached-input rate unavailable

Qwen3.8 Max has no comparable published API token rate.

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

SpecificationDeepSeek V4 ProQwen3.8 Max

Context window

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

DeepSeek V4 Pro

DeepSeek models and pricing

Qwen3.8 Max

Alibaba Cloud Model Studio pricing

API model ID

DeepSeek V4 Pro

deepseek-v4-pro

DeepSeek models and pricing

Qwen3.8 Max

qwen3.8-max

Alibaba Cloud Model Studio pricing

Cached-input rate

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

DeepSeek V4 Pro

$0.003625 per 1M cached input tokens

Qwen3.8 Max

No comparable hosted API rate

Alibaba Cloud Model Studio pricing

Documented inputs

DeepSeek V4 Pro

text

DeepSeek GitHub Copilot integration

Qwen3.8 Max

Not sourced

Documented outputs

DeepSeek V4 Pro

text

DeepSeek GitHub Copilot integration

Qwen3.8 Max

Not sourced

Provider availability

DeepSeek V4 Pro

Preview · DeepSeek API, open weights

DeepSeek V4 Flash 0731 update

Qwen3.8 Max

Not sourced

Reasoning profile

DeepSeek V4 Pro

Non-Reasoning

Qwen3.8 Max

Reasoning

Weight access

DeepSeek V4 Pro

Open Weight

Qwen3.8 Max

Proprietary

License

DeepSeek V4 Pro

Open Weight

Qwen3.8 Max

Proprietary

Release date

DeepSeek V4 Pro

2026-04-24

Qwen3.8 Max

2026-08-03

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: Qwen3.8 Max has the higher public score estimate, 65.4 versus 59.91, but the 90% score intervals overlap.
Workload cost: A complete comparable API-rate estimate is not available for both models.
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 evidence71 rows

Agentic

Terminal-Bench 2.0
DeepSeek V4 Pro59.1%
Source
Qwen3.8 Max—
Not directly comparable
MCP Atlas
DeepSeek V4 Pro69.4%
Source
Qwen3.8 Max—
Not directly comparable
Toolathlon
DeepSeek V4 Pro46.3%
Source
Qwen3.8 Max—
Not directly comparable
Claw-Eval
DeepSeek V4 Pro59.8%
Source
Qwen3.8 Max—
Not directly comparable
Gert Labs
DeepSeek V4 Pro50.28%
Source
Qwen3.8 Max—
Not directly comparable
ResearchClawBench
DeepSeek V4 Pro17.1%
Source
Qwen3.8 Max—
Not directly comparable
Terminal-Bench 2.1
DeepSeek V4 Pro—
Qwen3.8 Max86.6%
Source
Not directly comparable
CoWorkBench
DeepSeek V4 Pro—
Qwen3.8 Max74.8%
Source
Not directly comparable
JobBench
DeepSeek V4 Pro—
Qwen3.8 Max53.4%
Source
Not directly comparable
skillsBench
DeepSeek V4 Pro—
Qwen3.8 Max70.2%
Source
Not directly comparable
Agents' Last Exam
DeepSeek V4 Pro—
Qwen3.8 Max52.4%
Source
Not directly comparable
AutomationBench
DeepSeek V4 Pro—
Qwen3.8 Max27.3%
Source
Not directly comparable
Toolathlon-Verified
DeepSeek V4 Pro—
Qwen3.8 Max72.5%
Source
Not directly comparable
WideResearch
DeepSeek V4 Pro—
Qwen3.8 Max81.9%
Source
Not directly comparable
HLE w/ tools
DeepSeek V4 Pro—
Qwen3.8 Max56.2%
Source
Not directly comparable
OSWorld-Verified
DeepSeek V4 Pro—
Qwen3.8 Max86.1%
Source
Not directly comparable
OSWorld 2.0
DeepSeek V4 Pro—
Qwen3.8 Max19.4%
Source
Not directly comparable
WebArena-Verified
DeepSeek V4 Pro—
Qwen3.8 Max66.8%
Source
Not directly comparable
AndroidWorld
DeepSeek V4 Pro—
Qwen3.8 Max85.3%
Source
Not directly comparable
MobileWorld
DeepSeek V4 Pro—
Qwen3.8 Max77.8%
Source
Not directly comparable

Coding

LiveCodeBench Pass@1-COT
DeepSeek V4 Pro56.8%
Source
Qwen3.8 Max—
Not directly comparable
SWE-bench Verified
DeepSeek V4 Pro73.6%
Source
Qwen3.8 Max—
Not directly comparable
SWE-bench Pro
DeepSeek V4 Pro52.1%
Source
Qwen3.8 Max67.7%
Source
Qwen3.8 Max leads this result
SWE Multilingual
DeepSeek V4 Pro69.8%
Source
Qwen3.8 Max—
Not directly comparable
Terminal-Bench 2.0
DeepSeek V4 Pro59.1%
Source
Qwen3.8 Max—
Not directly comparable
Terminal-Bench 2.1
DeepSeek V4 Pro—
Qwen3.8 Max86.6%
Source
Not directly comparable
deepSwe
DeepSeek V4 Pro—
Qwen3.8 Max56.6%
Source
Not directly comparable
NL2Repo
DeepSeek V4 Pro—
Qwen3.8 Max55.9%
Source
Not directly comparable
FrontierSWE
DeepSeek V4 Pro—
Qwen3.8 Max73.5%
Source
Not directly comparable
MLS-Bench Lite
DeepSeek V4 Pro—
Qwen3.8 Max41.0%
Source
Not directly comparable
PaperBench
DeepSeek V4 Pro—
Qwen3.8 Max93.0%
Source
Not directly comparable

Reasoning

MRCR 1M
DeepSeek V4 Pro44.7%
Source
Qwen3.8 Max—
Not directly comparable
CorpusQA 1M
DeepSeek V4 Pro35.6%
Source
Qwen3.8 Max—
Not directly comparable
MRCRv2
DeepSeek V4 Pro—
Qwen3.8 Max92.9%
Source
Not directly comparable
LongBench v2
DeepSeek V4 Pro—
Qwen3.8 Max66.3%
Source
Not directly comparable

Knowledge

MMLU-Pro
DeepSeek V4 Pro82.9%
Source
Qwen3.8 Max—
Not directly comparable
SimpleQA
DeepSeek V4 Pro45%
Source
Qwen3.8 Max—
Not directly comparable
Chinese-SimpleQA
DeepSeek V4 Pro75.8%
Source
Qwen3.8 Max—
Not directly comparable
GPQA
DeepSeek V4 Pro72.9%
Source
Qwen3.8 Max92.6%
Source
Qwen3.8 Max leads this result
GPQA-D
DeepSeek V4 Pro72.9%
Source
Qwen3.8 Max92.6%
Source
Qwen3.8 Max leads this result
HLE
DeepSeek V4 Pro7.7%
Source
Qwen3.8 Max43.6%
Source
Qwen3.8 Max leads this result
HLE w/o tools
DeepSeek V4 Pro—
Qwen3.8 Max43.6%
Source
Not directly comparable

Math

HMMT Feb 2026
DeepSeek V4 Pro31.7%
Source
Qwen3.8 Max—
Not directly comparable
IMOAnswerBench
DeepSeek V4 Pro35.3%
Source
Qwen3.8 Max—
Not directly comparable
Apex
DeepSeek V4 Pro0.4%
Source
Qwen3.8 Max—
Not directly comparable
Apex Shortlist
DeepSeek V4 Pro9.2%
Source
Qwen3.8 Max—
Not directly comparable

Multimodal

MMMU-Pro
DeepSeek V4 Pro—
Qwen3.8 Max82.3%
Source
Not directly comparable
MathVision
DeepSeek V4 Pro—
Qwen3.8 Max95.2%
Source
Not directly comparable
MathVision w/ Python
DeepSeek V4 Pro—
Qwen3.8 Max97.7%
Source
Not directly comparable
BabyVision
DeepSeek V4 Pro—
Qwen3.8 Max82.0%
Source
Not directly comparable
BabyVision w/ Python
DeepSeek V4 Pro—
Qwen3.8 Max91.3%
Source
Not directly comparable
ZeroBench
DeepSeek V4 Pro—
Qwen3.8 Max24.0%
Source
Not directly comparable
ZeroBench w/ Python
DeepSeek V4 Pro—
Qwen3.8 Max49.0%
Source
Not directly comparable
MedXpertQA (MM)
DeepSeek V4 Pro—
Qwen3.8 Max80.4%
Source
Not directly comparable
ScreenSpot Pro
DeepSeek V4 Pro—
Qwen3.8 Max84.5%
Source
Not directly comparable
Vision2Web
DeepSeek V4 Pro—
Qwen3.8 Max69.0%
Source
Not directly comparable
CharXiv w/o tools
DeepSeek V4 Pro—
Qwen3.8 Max88.4%
Source
Not directly comparable
CharXiv
DeepSeek V4 Pro—
Qwen3.8 Max93.5%
Source
Not directly comparable
OmniDocBench 1.5
DeepSeek V4 Pro—
Qwen3.8 Max92.1%
Source
Not directly comparable
OCRBench V2
DeepSeek V4 Pro—
Qwen3.8 Max74.2%
Source
Not directly comparable
CC-OCR
DeepSeek V4 Pro—
Qwen3.8 Max79.6%
Source
Not directly comparable
RealWorldQA
DeepSeek V4 Pro—
Qwen3.8 Max88.0%
Source
Not directly comparable
ERQA
DeepSeek V4 Pro—
Qwen3.8 Max77.8%
Source
Not directly comparable
SimpleVQA
DeepSeek V4 Pro—
Qwen3.8 Max75.0%
Source
Not directly comparable
PerceptionBench
DeepSeek V4 Pro—
Qwen3.8 Max63.5%
Source
Not directly comparable
Video-MME (with subtitle)
DeepSeek V4 Pro—
Qwen3.8 Max90.4%
Source
Not directly comparable
VideoMMMU
DeepSeek V4 Pro—
Qwen3.8 Max88.7%
Source
Not directly comparable
MMVU
DeepSeek V4 Pro—
Qwen3.8 Max82.4%
Source
Not directly comparable
MLVU (M-Avg)
DeepSeek V4 Pro—
Qwen3.8 Max90.8%
Source
Not directly comparable
LVBench
DeepSeek V4 Pro—
Qwen3.8 Max81.8%
Source
Not directly comparable

Instruction following

IFBench
DeepSeek V4 Pro—
Qwen3.8 Max82.8%
Source
Not directly comparable

Frequently asked questions

Which is better, DeepSeek V4 Pro or Qwen3.8 Max?

Qwen3.8 Max has the higher public score estimate, 65.4 versus 59.91, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, DeepSeek V4 Pro or Qwen3.8 Max?

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, DeepSeek V4 Pro or Qwen3.8 Max?

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 V4 Pro or Qwen3.8 Max?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, DeepSeek V4 Pro or Qwen3.8 Max?

Both models list the same context window, 1M.

Related comparisons

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Last updated August 3, 2026

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DeepSeek V4 Pro vs Qwen3.8 Max

Which one for your work

Coding work

Agentic work

Long documents

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

What is actually comparable

Category results, on a stated basis

Coding

Knowledge

Agentic

Reasoning

Math

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

Multimodal

Instruction following

Frequently asked questions

Which is better, DeepSeek V4 Pro or Qwen3.8 Max?

Which is better for coding, DeepSeek V4 Pro or Qwen3.8 Max?

Which is better for agentic tasks, DeepSeek V4 Pro or Qwen3.8 Max?

Which costs less, DeepSeek V4 Pro or Qwen3.8 Max?

Which has the larger context window, DeepSeek V4 Pro or Qwen3.8 Max?

Related comparisons

Watch DeepSeek V4 Pro vs Qwen3.8 Max