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BenchLM
Model A
DeepSeek V3

DeepSeek

44.3/100

Supported · Public rank #168

90% interval 25.4–63.2

DeepSeek V3 vs Qwen3.7 Max

Updated August 18, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
Qwen3.7 Max

Alibaba

71.6/100

Supported · Public rank #16

90% interval 64.6–78.6

Decision reading

Qwen3.7 Max has the higher public score, 71.6 versus 44.28, and the 90% score intervals do not overlap.

5 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.

  • 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

    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

  • 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

What is actually comparable

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

Shared results
5
DeepSeek V3 only
1
Qwen3.7 Max only
30
Like-for-like categories
0 / 8

3 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 V3
38.9
Qwen3.7 Max
77.9
Weighted basis
2 vs 4 rows
Reading
Directional only

Knowledge

Directional only
DeepSeek V3
72.7
Qwen3.7 Max
64.2
Weighted basis
2 vs 4 rows
Reading
Directional only

Instruction following

Directional only
DeepSeek V3
86.1
Qwen3.7 Max
84.4
Weighted basis
1 vs 2 rows
Reading
Directional only

Agentic

Not comparable
DeepSeek V3
Not measured
Qwen3.7 Max
69.7
Weighted basis
0 vs 1 rows
Reading
Not comparable

Reasoning

Not comparable
DeepSeek V3
Not measured
Qwen3.7 Max
90.4
Weighted basis
0 vs 1 rows
Reading
Not comparable

Math

Not comparable
DeepSeek V3
1.7
Qwen3.7 Max
97.1
Weighted basis
1 vs 1 rows
Reading
Not comparable

Multilingual

Not comparable
DeepSeek V3
Not measured
Qwen3.7 Max
87.0
Weighted basis
0 vs 1 rows
Reading
Not comparable

Multimodal

Not comparable
DeepSeek V3
Not measured
Qwen3.7 Max
Not measured
Weighted basis
0 vs 0 rows
Reading
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.

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.

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, 71.6 versus 44.28, 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.

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 evidence36 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

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

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

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

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

Frequently asked questions

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

Qwen3.7 Max has the higher public score, 71.6 versus 44.28, 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?

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 V3 or Qwen3.7 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 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.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 18, 2026

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