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DeepSeek V3 vs Qwen3.7 Max

Updated October 2, 2026. Rank says Qwen3.7 Max is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

Qwen3.7 Max has the higher public score, 63.46 versus 32.59, and the 90% score intervals do not overlap. 5 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 V3

DeepSeek

32.59/100

Supported · Public rank #153

90% interval 16.4–48.7

Model B
Alibaba logo
Qwen3.7 Max

Alibaba

63.46/100

Supported · Public rank #43

90% interval 54.0–72.9

Shared results
5
DeepSeek V3 only
1
Qwen3.7 Max only
36
Like-for-like categories
0 / 8
Supported: DeepSeek V3 and Qwen3.7 MaxHow 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

    Qwen3.7 Max

    Qwen3.7 Max has the larger documented context window.

    Confidence: documented
Show secondary and unsupported calls

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    DeepSeek V3 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.

    Confidence: limited

  • Agentic work

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

    Not enough matched evidence

    DeepSeek V3 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.

    Confidence: limited

  • 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

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. DeepSeek V3 does not fit this workload in one request. Qwen3.7 Max has no comparable published API token rate.

    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

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.

18.2DeepSeek V345.4Qwen3.7 Max

Directional only · BenchAlign v5.8

Qwen3.7 Max has the higher coding point estimate. Conditional score ranges do not establish rank confidence.

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

What is actually comparable

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

4 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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, 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

Directional only
DeepSeek V3
10.4
Estimated · #112/119
Qwen3.7 Max
39.3
Supported · #60/119
Basis
BenchAlign v5.8 lane · 0 vs 10 public rows
Reading
Directional only

Coding

Directional only
DeepSeek V3
18.2
Estimated · #126/144
Qwen3.7 Max
45.4
Supported · #54/144
Basis
BenchAlign v5.8 lane · 2 vs 10 public rows
Reading
Directional only

Knowledge

Directional only
DeepSeek V3
30.2
Estimated · #136/171
Qwen3.7 Max
59.8
Supported · #41/171
Basis
BenchAlign v5.8 lane · 2 vs 9 public rows
Reading
Directional only

Instruction following

Directional only
DeepSeek V3
38.2
#104/125
Qwen3.7 Max
89.2
#17/125
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Directional only

Reasoning

Not comparable
DeepSeek V3
42.3
Unranked · 2 rankable rows
Qwen3.7 Max
76.3
Unranked · 3 rankable rows
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Multimodal

Not comparable
DeepSeek V3
Not ranked
Qwen3.7 Max
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
DeepSeek V3
Not ranked
Qwen3.7 Max
100.0
#1/16
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Math

Not comparable
DeepSeek V3
26.0
Unranked · 1 rankable row
Qwen3.7 Max
81.9
Unranked · 3 rankable rows
Basis
Provisional lane · 1 vs 1 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, 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.

Supported evidence per lane · 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 V3
$0.00082
Fits in one request
Qwen3.7 Max
API rate not published
Fits in one request

Qwen3.7 Max has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

DeepSeek V3
$0.0168
Fits in one request
Qwen3.7 Max
API rate not published
Fits in one request

Qwen3.7 Max has no comparable published API token rate.

Cache-heavy agent loop

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

DeepSeek V3
$0.0304
Does not fit in one request
Qwen3.7 Max
API rate not published
Fits in one request
Cached-input rate unavailable

DeepSeek V3 does not fit this workload in one request. Qwen3.7 Max has no comparable published API token rate.

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 V3

128K

Qwen3.7 Max

1M

API model ID

DeepSeek V3

Not sourced

Qwen3.7 Max

Not sourced

Cached-input rate

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

DeepSeek V3

$0.07 per 1M cached input tokens

Qwen3.7 Max

No comparable hosted API rate

Documented inputs

DeepSeek V3

Not sourced

Qwen3.7 Max

Not sourced

Documented outputs

DeepSeek V3

Not sourced

Qwen3.7 Max

Not sourced

Provider availability

DeepSeek V3

Not sourced

Qwen3.7 Max

Not sourced

Reasoning profile

DeepSeek V3

Non-Reasoning

Qwen3.7 Max

Reasoning

Weight access

DeepSeek V3

Open Weight

Qwen3.7 Max

Proprietary

License

DeepSeek V3

Open Weight

Qwen3.7 Max

Proprietary

Release date

DeepSeek V3

2024-12-26

Qwen3.7 Max

2026-05-16

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
Qwen3.7 Max has the higher public score, 63.46 versus 32.59, and the 90% score intervals do not overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Qwen3.7 Max has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, DeepSeek V3 or Qwen3.7 Max?

Qwen3.7 Max has the higher public score, 63.46 versus 32.59, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.

Which is better for coding, DeepSeek V3 or Qwen3.7 Max?

Qwen3.7 Max scores higher for coding on the public lane, 45.4 to 18.2. DeepSeek V3 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, DeepSeek V3 or Qwen3.7 Max?

Qwen3.7 Max scores higher for agentic tasks on the public lane, 39.3 to 10.4. DeepSeek V3 is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, DeepSeek V3 or Qwen3.7 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 V3 or Qwen3.7 Max?

Qwen3.7 Max has the larger documented context window: 1M, compared with 128K.

Self-host vs API cost

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

DeepSeek V3
API / mo$1,028
Self-host / mo$18,221
Break-even1.2B/day
Qwen3.7 Max
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 evidence42 rows

Agentic

  • Terminal-Bench 2.0

    DeepSeek V3—
    Qwen3.7 Max69.7%
    Source

    Not directly comparable

  • QwenClawBench

    DeepSeek V3—
    Qwen3.7 Max64.3%
    Source

    Not directly comparable

  • Claw-Eval

    DeepSeek V3—
    Qwen3.7 Max65.2%
    Source

    Not directly comparable

  • BFCL v4

    DeepSeek V3—
    Qwen3.7 Max75.0%
    Source

    Not directly comparable

  • MCP Atlas

    DeepSeek V3—
    Qwen3.7 Max76.4%
    Source

    Not directly comparable

  • VITA-Bench

    DeepSeek V3—
    Qwen3.7 Max47.9%
    Source

    Not directly comparable

  • HLE w/ tools

    DeepSeek V3—
    Qwen3.7 Max53.5%
    Source

    Not directly comparable

  • Gert Labs

    DeepSeek V3—
    Qwen3.7 Max64.27%
    Source

    Not directly comparable

  • ResearchClawBench

    DeepSeek V3—
    Qwen3.7 Max18.7%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    DeepSeek V3—
    Qwen3.7 Max61.0%
    Source

    Not directly comparable

Coding

  • LiveCodeBench

    DeepSeek V337.6%
    Source
    Qwen3.7 Max91.6%
    Source

    Qwen3.7 Max leads this result

  • SWE-bench Verified

    DeepSeek V342%
    Source
    Qwen3.7 Max80.4%
    Source

    Qwen3.7 Max leads this result

  • SWE-bench Pro

    DeepSeek V3—
    Qwen3.7 Max60.6%
    Source

    Not directly comparable

  • SWE Multilingual

    DeepSeek V3—
    Qwen3.7 Max78.3%
    Source

    Not directly comparable

  • NL2Repo

    DeepSeek V3—
    Qwen3.7 Max47.2%
    Source

    Not directly comparable

  • SciCode

    DeepSeek V3—
    Qwen3.7 Max53.5%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    DeepSeek V3—
    Qwen3.7 Max69.7%
    Source

    Not directly comparable

  • OpenHarmony Bench

    DeepSeek V3—
    Qwen3.7 Max53.4%
    Source

    Not directly comparable

  • LiveCodeBench (Vals)

    DeepSeek V3—
    Qwen3.7 Max87.1%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    DeepSeek V3—
    Qwen3.7 Max68.8%
    Source

    Not directly comparable

Reasoning

  • MRCRv2

    DeepSeek V3—
    Qwen3.7 Max90.4%
    Source

    Not directly comparable

  • CritPt

    DeepSeek V3—
    Qwen3.7 Max13.4%
    Source

    Not directly comparable

Knowledge

  • GPQA

    DeepSeek V359.1%
    Source
    Qwen3.7 Max92.4%
    Source

    Qwen3.7 Max leads this result

  • MMLU-Pro

    DeepSeek V375.9%
    Source
    Qwen3.7 Max89.6%
    Source

    Qwen3.7 Max leads this result

  • GPQA-D

    DeepSeek V3—
    Qwen3.7 Max92.4%
    Source

    Not directly comparable

  • HLE

    DeepSeek V3—
    Qwen3.7 Max41.4%
    Source

    Not directly comparable

  • MMLU-Redux

    DeepSeek V3—
    Qwen3.7 Max95%
    Source

    Not directly comparable

  • SuperGPQA

    DeepSeek V3—
    Qwen3.7 Max73.6%
    Source

    Not directly comparable

  • MMMLU

    DeepSeek V3—
    Qwen3.7 Max90.3%
    Source

    Not directly comparable

  • GPQA Diamond (Vals)

    DeepSeek V3—
    Qwen3.7 Max90.2%
    Source

    Not directly comparable

  • MMLU-Pro (Vals)

    DeepSeek V3—
    Qwen3.7 Max89.3%
    Source

    Not directly comparable

Multilingual

  • MMLU-ProX

    DeepSeek V3—
    Qwen3.7 Max87%
    Source

    Not directly comparable

  • NOVA-63

    DeepSeek V3—
    Qwen3.7 Max59.0%
    Source

    Not directly comparable

  • INCLUDE

    DeepSeek V3—
    Qwen3.7 Max86.2%
    Source

    Not directly comparable

  • MAXIFE

    DeepSeek V3—
    Qwen3.7 Max89.2%
    Source

    Not directly comparable

  • PolyMath

    DeepSeek V3—
    Qwen3.7 Max86.5%
    Source

    Not directly comparable

Instruction following

  • IFEval

    DeepSeek V386.1%
    Source
    Qwen3.7 Max94.3%
    Source

    Qwen3.7 Max leads this result

  • IFBench

    DeepSeek V3—
    Qwen3.7 Max79.1%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    DeepSeek V31.724%
    Source
    Qwen3.7 Max—

    Not directly comparable

  • HMMT Feb 2026

    DeepSeek V3—
    Qwen3.7 Max97.1%
    Source

    Not directly comparable

  • IMOAnswerBench

    DeepSeek V3—
    Qwen3.7 Max90.0%
    Source

    Not directly comparable

  • Apex

    DeepSeek V3—
    Qwen3.7 Max44.5%
    Source

    Not directly comparable

42 public results · 5 shared

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Last updated October 2, 2026