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Model comparison

Qwen3.7 Max vs SALMONN 7B

Updated August 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Qwen3.7 Max

Alibaba

71.8/100

Supported · Public rank #13

90% interval 65.4–78.2

SALMONN 7B

SALMONN authors

—

Evidence status unavailable

90% interval unavailable

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.

0 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

    No shared weighted benchmark basis supports a winner.

    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

    Not enough matched evidence

    A complete context comparison is not sourced.

    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
0
Qwen3.7 Max only
35
SALMONN 7B only
0
Like-for-like categories
0 / 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

Not comparable
Qwen3.7 Max
69.7
SALMONN 7B
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Coding

Not comparable
Qwen3.7 Max
77.9
SALMONN 7B
Not measured
Weighted basis
4 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
Qwen3.7 Max
90.4
SALMONN 7B
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Qwen3.7 Max
64.2
SALMONN 7B
Not measured
Weighted basis
4 vs 0 rows
Reading
Not comparable

Math

Not comparable
Qwen3.7 Max
97.1
SALMONN 7B
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Qwen3.7 Max
87.0
SALMONN 7B
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Qwen3.7 Max
Not measured
SALMONN 7B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Qwen3.7 Max
84.4
SALMONN 7B
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.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

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
SALMONN 7B
API rate not published
Fit state unavailable

Qwen3.7 Max has no comparable published API token rate. SALMONN 7B 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
SALMONN 7B
API rate not published
Fit state unavailable

Qwen3.7 Max has no comparable published API token rate. SALMONN 7B 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
SALMONN 7B
API rate not published
Fit state unavailable
Cached-input rate unavailable

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

SALMONN 7B

N/A

API model ID

Qwen3.7 Max

Not sourced

SALMONN 7B

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

SALMONN 7B

No comparable hosted API rate

SALMONN authors model documentation

Documented inputs

Qwen3.7 Max

Not sourced

SALMONN 7B

Not sourced

Documented outputs

Qwen3.7 Max

Not sourced

SALMONN 7B

Not sourced

Provider availability

Qwen3.7 Max

Not sourced

SALMONN 7B

Not sourced

Reasoning profile

Qwen3.7 Max

Reasoning

SALMONN 7B

Non-Reasoning

Weight access

Qwen3.7 Max

Proprietary

SALMONN 7B

Open Weight

License

Qwen3.7 Max

Proprietary

SALMONN 7B

Open Weight

Release date

Qwen3.7 Max

2026-05-16

SALMONN 7B

Not sourced

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
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
A complete documented context comparison is not available.

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 evidence35 rows

Agentic

  • Terminal-Bench 2.0

    Qwen3.7 Max69.7%
    Source
    SALMONN 7B—

    Not directly comparable

  • QwenClawBench

    Qwen3.7 Max64.3%
    Source
    SALMONN 7B—

    Not directly comparable

  • Claw-Eval

    Qwen3.7 Max65.2%
    Source
    SALMONN 7B—

    Not directly comparable

  • BFCL v4

    Qwen3.7 Max75.0%
    Source
    SALMONN 7B—

    Not directly comparable

  • MCP Atlas

    Qwen3.7 Max76.4%
    Source
    SALMONN 7B—

    Not directly comparable

  • VITA-Bench

    Qwen3.7 Max47.9%
    Source
    SALMONN 7B—

    Not directly comparable

  • HLE w/ tools

    Qwen3.7 Max53.5%
    Source
    SALMONN 7B—

    Not directly comparable

  • Gert Labs

    Qwen3.7 Max64.27%
    Source
    SALMONN 7B—

    Not directly comparable

  • ResearchClawBench

    Qwen3.7 Max18.7%
    Source
    SALMONN 7B—

    Not directly comparable

Coding

  • SWE-bench Verified

    Qwen3.7 Max80.4%
    Source
    SALMONN 7B—

    Not directly comparable

  • SWE-bench Pro

    Qwen3.7 Max60.6%
    Source
    SALMONN 7B—

    Not directly comparable

  • SWE Multilingual

    Qwen3.7 Max78.3%
    Source
    SALMONN 7B—

    Not directly comparable

  • NL2Repo

    Qwen3.7 Max47.2%
    Source
    SALMONN 7B—

    Not directly comparable

  • SciCode

    Qwen3.7 Max53.5%
    Source
    SALMONN 7B—

    Not directly comparable

  • LiveCodeBench

    Qwen3.7 Max91.6%
    Source
    SALMONN 7B—

    Not directly comparable

  • Terminal-Bench 2.0

    Qwen3.7 Max69.7%
    Source
    SALMONN 7B—

    Not directly comparable

Reasoning

  • MRCRv2

    Qwen3.7 Max90.4%
    Source
    SALMONN 7B—

    Not directly comparable

  • CritPt

    Qwen3.7 Max13.4%
    Source
    SALMONN 7B—

    Not directly comparable

Knowledge

  • GPQA

    Qwen3.7 Max92.4%
    Source
    SALMONN 7B—

    Not directly comparable

  • GPQA-D

    Qwen3.7 Max92.4%
    Source
    SALMONN 7B—

    Not directly comparable

  • HLE

    Qwen3.7 Max41.4%
    Source
    SALMONN 7B—

    Not directly comparable

  • MMLU-Pro

    Qwen3.7 Max89.6%
    Source
    SALMONN 7B—

    Not directly comparable

  • MMLU-Redux

    Qwen3.7 Max95%
    Source
    SALMONN 7B—

    Not directly comparable

  • SuperGPQA

    Qwen3.7 Max73.6%
    Source
    SALMONN 7B—

    Not directly comparable

  • MMMLU

    Qwen3.7 Max90.3%
    Source
    SALMONN 7B—

    Not directly comparable

Math

  • HMMT Feb 2026

    Qwen3.7 Max97.1%
    Source
    SALMONN 7B—

    Not directly comparable

  • IMOAnswerBench

    Qwen3.7 Max90.0%
    Source
    SALMONN 7B—

    Not directly comparable

  • Apex

    Qwen3.7 Max44.5%
    Source
    SALMONN 7B—

    Not directly comparable

Multilingual

  • MMLU-ProX

    Qwen3.7 Max87%
    Source
    SALMONN 7B—

    Not directly comparable

  • NOVA-63

    Qwen3.7 Max59.0%
    Source
    SALMONN 7B—

    Not directly comparable

  • INCLUDE

    Qwen3.7 Max86.2%
    Source
    SALMONN 7B—

    Not directly comparable

  • MAXIFE

    Qwen3.7 Max89.2%
    Source
    SALMONN 7B—

    Not directly comparable

  • PolyMath

    Qwen3.7 Max86.5%
    Source
    SALMONN 7B—

    Not directly comparable

Instruction following

  • IFEval

    Qwen3.7 Max94.3%
    Source
    SALMONN 7B—

    Not directly comparable

  • IFBench

    Qwen3.7 Max79.1%
    Source
    SALMONN 7B—

    Not directly comparable

Frequently asked questions

Which is better, Qwen3.7 Max or SALMONN 7B?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Qwen3.7 Max or SALMONN 7B?

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 SALMONN 7B?

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, Qwen3.7 Max or SALMONN 7B?

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 SALMONN 7B?

A complete documented context-window comparison is not available.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 4, 2026

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