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BenchLM
Model A
Qwen3.7 Max

Alibaba

71.4/100

Supported · Public rank #16

90% interval 64.0–78.7

Qwen3.7 Max vs SWE-1.7

Updated August 22, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
SWE-1.7

Cognition

—

Evidence status unavailable

90% interval unavailable

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.

3 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.

  • Agentic work

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

    SWE-1.7

    SWE-1.7 leads on the same 1 weighted benchmark row.

    Confidence: limited

  • 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

    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

    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.

Shared results
3
Qwen3.7 Max only
32
SWE-1.7 only
1
Like-for-like categories
1 / 8

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
Qwen3.7 Max
69.7
SWE-1.7
81.5
Weighted basis
1 vs 1 rows
Reading
SWE-1.7 leads

Coding

Not comparable
Qwen3.7 Max
77.9
SWE-1.7
Not measured
Weighted basis
4 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
Qwen3.7 Max
90.4
SWE-1.7
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Qwen3.7 Max
64.2
SWE-1.7
Not measured
Weighted basis
4 vs 0 rows
Reading
Not comparable

Math

Not comparable
Qwen3.7 Max
97.1
SWE-1.7
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Qwen3.7 Max
87.0
SWE-1.7
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Qwen3.7 Max
Not measured
SWE-1.7
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Qwen3.7 Max
84.4
SWE-1.7
Not measured
Weighted basis
2 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

Qwen3.7 Max
API rate not published
Fits in one request
SWE-1.7
API rate not published
Fits in one request

Qwen3.7 Max has no comparable published API token rate. SWE-1.7 has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Qwen3.7 Max
API rate not published
Fits in one request
SWE-1.7
API rate not published
Fits in one request

Qwen3.7 Max has no comparable published API token rate. SWE-1.7 has no comparable published API token rate.

Cache-heavy agent loop

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

Qwen3.7 Max
API rate not published
Fits in one request
Cached-input rate unavailable
SWE-1.7
API rate not published
Fits in one request
Cached-input rate unavailable

Qwen3.7 Max has no comparable published API token rate. SWE-1.7 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.

Qwen3.7 Max

1M

SWE-1.7

256K

API model ID

Qwen3.7 Max

Not sourced

SWE-1.7

Not sourced

Cached-input rate

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

Qwen3.7 Max

No comparable hosted API rate

SWE-1.7

No comparable hosted API rate

Documented inputs

Qwen3.7 Max

Not sourced

SWE-1.7

Not sourced

Documented outputs

Qwen3.7 Max

Not sourced

SWE-1.7

Not sourced

Provider availability

Qwen3.7 Max

Not sourced

SWE-1.7

Not sourced

Reasoning profile

Qwen3.7 Max

Reasoning

SWE-1.7

Reasoning

Weight access

Qwen3.7 Max

Proprietary

SWE-1.7

Proprietary

License

Qwen3.7 Max

Proprietary

SWE-1.7

Proprietary

Release date

Qwen3.7 Max

2026-05-16

SWE-1.7

2026-07-08

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
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.

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

    Qwen3.7 Max69.7%
    Source
    SWE-1.781.5%
    Source

    SWE-1.7 leads this result

  • QwenClawBench

    Qwen3.7 Max64.3%
    Source
    SWE-1.7—

    Not directly comparable

  • Claw-Eval

    Qwen3.7 Max65.2%
    Source
    SWE-1.7—

    Not directly comparable

  • BFCL v4

    Qwen3.7 Max75.0%
    Source
    SWE-1.7—

    Not directly comparable

  • MCP Atlas

    Qwen3.7 Max76.4%
    Source
    SWE-1.7—

    Not directly comparable

  • VITA-Bench

    Qwen3.7 Max47.9%
    Source
    SWE-1.7—

    Not directly comparable

  • HLE w/ tools

    Qwen3.7 Max53.5%
    Source
    SWE-1.7—

    Not directly comparable

  • Gert Labs

    Qwen3.7 Max64.27%
    Source
    SWE-1.7—

    Not directly comparable

  • ResearchClawBench

    Qwen3.7 Max18.7%
    Source
    SWE-1.7—

    Not directly comparable

Coding

  • SWE-bench Verified

    Qwen3.7 Max80.4%
    Source
    SWE-1.7—

    Not directly comparable

  • SWE-bench Pro

    Qwen3.7 Max60.6%
    Source
    SWE-1.7—

    Not directly comparable

  • SWE Multilingual

    Qwen3.7 Max78.3%
    Source
    SWE-1.777.8%
    Source

    Qwen3.7 Max leads this result

  • NL2Repo

    Qwen3.7 Max47.2%
    Source
    SWE-1.7—

    Not directly comparable

  • SciCode

    Qwen3.7 Max53.5%
    Source
    SWE-1.7—

    Not directly comparable

  • LiveCodeBench

    Qwen3.7 Max91.6%
    Source
    SWE-1.7—

    Not directly comparable

  • Terminal-Bench 2.0

    Qwen3.7 Max69.7%
    Source
    SWE-1.781.5%
    Source

    SWE-1.7 leads this result

  • FrontierCode 1.1 Main

    Qwen3.7 Max—
    SWE-1.742.3%
    Source

    Not directly comparable

Reasoning

  • MRCRv2

    Qwen3.7 Max90.4%
    Source
    SWE-1.7—

    Not directly comparable

  • CritPt

    Qwen3.7 Max13.4%
    Source
    SWE-1.7—

    Not directly comparable

Knowledge

  • GPQA

    Qwen3.7 Max92.4%
    Source
    SWE-1.7—

    Not directly comparable

  • GPQA-D

    Qwen3.7 Max92.4%
    Source
    SWE-1.7—

    Not directly comparable

  • HLE

    Qwen3.7 Max41.4%
    Source
    SWE-1.7—

    Not directly comparable

  • MMLU-Pro

    Qwen3.7 Max89.6%
    Source
    SWE-1.7—

    Not directly comparable

  • MMLU-Redux

    Qwen3.7 Max95%
    Source
    SWE-1.7—

    Not directly comparable

  • SuperGPQA

    Qwen3.7 Max73.6%
    Source
    SWE-1.7—

    Not directly comparable

  • MMMLU

    Qwen3.7 Max90.3%
    Source
    SWE-1.7—

    Not directly comparable

Math

  • HMMT Feb 2026

    Qwen3.7 Max97.1%
    Source
    SWE-1.7—

    Not directly comparable

  • IMOAnswerBench

    Qwen3.7 Max90.0%
    Source
    SWE-1.7—

    Not directly comparable

  • Apex

    Qwen3.7 Max44.5%
    Source
    SWE-1.7—

    Not directly comparable

Multilingual

  • MMLU-ProX

    Qwen3.7 Max87%
    Source
    SWE-1.7—

    Not directly comparable

  • NOVA-63

    Qwen3.7 Max59.0%
    Source
    SWE-1.7—

    Not directly comparable

  • INCLUDE

    Qwen3.7 Max86.2%
    Source
    SWE-1.7—

    Not directly comparable

  • MAXIFE

    Qwen3.7 Max89.2%
    Source
    SWE-1.7—

    Not directly comparable

  • PolyMath

    Qwen3.7 Max86.5%
    Source
    SWE-1.7—

    Not directly comparable

Instruction following

  • IFEval

    Qwen3.7 Max94.3%
    Source
    SWE-1.7—

    Not directly comparable

  • IFBench

    Qwen3.7 Max79.1%
    Source
    SWE-1.7—

    Not directly comparable

Frequently asked questions

Which is better, Qwen3.7 Max or SWE-1.7?

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, Qwen3.7 Max or SWE-1.7?

The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.

Which is better for agentic tasks, Qwen3.7 Max or SWE-1.7?

SWE-1.7 leads the like-for-like agentic tasks comparison across 1 shared weighted benchmark row.

Which costs less, Qwen3.7 Max or SWE-1.7?

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, Qwen3.7 Max or SWE-1.7?

Qwen3.7 Max has the larger documented context window: 1M, compared with 256K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 22, 2026

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