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Five or fewer confirmed AI changes, with original sources, on mornings when something changed.A free source-linked morning brief.

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Model A
Qwen3.7 Max

Alibaba

71.6/100

Supported · Public rank #16

90% interval 64.6–78.6

Qwen3.7 Max vs Trinity-Large-Preview

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

Model B
Trinity-Large-Preview

Arcee AI

56.4/100

Estimated · Public rank #91

90% interval 44.9–67.9

Decision reading

Qwen3.7 Max has the higher public score estimate, 71.6 versus 56.39, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

1 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

    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

  • 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: rate-fallback

  • 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
1
Qwen3.7 Max only
34
Trinity-Large-Preview only
3
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
Trinity-Large-Preview
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Coding

Not comparable
Qwen3.7 Max
77.9
Trinity-Large-Preview
Not measured
Weighted basis
4 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
Qwen3.7 Max
90.4
Trinity-Large-Preview
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Qwen3.7 Max
64.2
Trinity-Large-Preview
Not measured
Weighted basis
4 vs 0 rows
Reading
Not comparable

Math

Not comparable
Qwen3.7 Max
97.1
Trinity-Large-Preview
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Qwen3.7 Max
87.0
Trinity-Large-Preview
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Qwen3.7 Max
Not measured
Trinity-Large-Preview
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Qwen3.7 Max
84.4
Trinity-Large-Preview
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
Trinity-Large-Preview
$0.00075
Fits in one request

Qwen3.7 Max 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
Trinity-Large-Preview
$0.0155
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

Qwen3.7 Max
API rate not published
Fits in one request
Cached-input rate unavailable
Trinity-Large-Preview
$0.065
Fits in one request
Cached input priced at the published list-input rate

Trinity-Large-Preview has no published cached-input rate, so cached tokens use its listed input rate. 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.

Qwen3.7 Max

1M

Trinity-Large-Preview

512K

API model ID

Qwen3.7 Max

Not sourced

Trinity-Large-Preview

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

Trinity-Large-Preview

Not published

Documented inputs

Qwen3.7 Max

Not sourced

Trinity-Large-Preview

Not sourced

Documented outputs

Qwen3.7 Max

Not sourced

Trinity-Large-Preview

Not sourced

Provider availability

Qwen3.7 Max

Not sourced

Trinity-Large-Preview

Not sourced

Reasoning profile

Qwen3.7 Max

Reasoning

Trinity-Large-Preview

Non-Reasoning

Weight access

Qwen3.7 Max

Proprietary

Trinity-Large-Preview

Open Weight

License

Qwen3.7 Max

Proprietary

Trinity-Large-Preview

Open Weight

Release date

Qwen3.7 Max

2026-05-16

Trinity-Large-Preview

2026-01-27

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 estimate, 71.6 versus 56.39, but the 90% score intervals 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.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence38 rows

Agentic

  • Terminal-Bench 2.0

    Qwen3.7 Max69.7%
    Source
    Trinity-Large-Preview

    Not directly comparable

  • QwenClawBench

    Qwen3.7 Max64.3%
    Source
    Trinity-Large-Preview

    Not directly comparable

  • Claw-Eval

    Qwen3.7 Max65.2%
    Source
    Trinity-Large-Preview

    Not directly comparable

  • BFCL v4

    Qwen3.7 Max75.0%
    Source
    Trinity-Large-Preview

    Not directly comparable

  • MCP Atlas

    Qwen3.7 Max76.4%
    Source
    Trinity-Large-Preview

    Not directly comparable

  • VITA-Bench

    Qwen3.7 Max47.9%
    Source
    Trinity-Large-Preview

    Not directly comparable

  • HLE w/ tools

    Qwen3.7 Max53.5%
    Source
    Trinity-Large-Preview

    Not directly comparable

  • Gert Labs

    Qwen3.7 Max64.27%
    Source
    Trinity-Large-Preview

    Not directly comparable

  • ResearchClawBench

    Qwen3.7 Max18.7%
    Source
    Trinity-Large-Preview

    Not directly comparable

Coding

  • SWE-bench Verified

    Qwen3.7 Max80.4%
    Source
    Trinity-Large-Preview

    Not directly comparable

  • SWE-bench Pro

    Qwen3.7 Max60.6%
    Source
    Trinity-Large-Preview

    Not directly comparable

  • SWE Multilingual

    Qwen3.7 Max78.3%
    Source
    Trinity-Large-Preview

    Not directly comparable

  • NL2Repo

    Qwen3.7 Max47.2%
    Source
    Trinity-Large-Preview

    Not directly comparable

  • SciCode

    Qwen3.7 Max53.5%
    Source
    Trinity-Large-Preview

    Not directly comparable

  • LiveCodeBench

    Qwen3.7 Max91.6%
    Source
    Trinity-Large-Preview

    Not directly comparable

  • Terminal-Bench 2.0

    Qwen3.7 Max69.7%
    Source
    Trinity-Large-Preview

    Not directly comparable

Reasoning

  • MRCRv2

    Qwen3.7 Max90.4%
    Source
    Trinity-Large-Preview

    Not directly comparable

  • CritPt

    Qwen3.7 Max13.4%
    Source
    Trinity-Large-Preview

    Not directly comparable

Knowledge

  • GPQA

    Qwen3.7 Max92.4%
    Source
    Trinity-Large-Preview

    Not directly comparable

  • GPQA-D

    Qwen3.7 Max92.4%
    Source
    Trinity-Large-Preview63.3%
    Source

    Qwen3.7 Max leads this result

  • HLE

    Qwen3.7 Max41.4%
    Source
    Trinity-Large-Preview

    Not directly comparable

  • MMLU-Pro

    Qwen3.7 Max89.6%
    Source
    Trinity-Large-Preview

    Not directly comparable

  • MMLU-Redux

    Qwen3.7 Max95%
    Source
    Trinity-Large-Preview

    Not directly comparable

  • SuperGPQA

    Qwen3.7 Max73.6%
    Source
    Trinity-Large-Preview

    Not directly comparable

  • MMMLU

    Qwen3.7 Max90.3%
    Source
    Trinity-Large-Preview

    Not directly comparable

  • MMLU

    Qwen3.7 Max
    Trinity-Large-Preview87.2%
    Source

    Not directly comparable

  • MMLU-Pro (Arcee)

    Qwen3.7 Max
    Trinity-Large-Preview75.2%
    Source

    Not directly comparable

Math

  • HMMT Feb 2026

    Qwen3.7 Max97.1%
    Source
    Trinity-Large-Preview

    Not directly comparable

  • IMOAnswerBench

    Qwen3.7 Max90.0%
    Source
    Trinity-Large-Preview

    Not directly comparable

  • Apex

    Qwen3.7 Max44.5%
    Source
    Trinity-Large-Preview

    Not directly comparable

  • AIME25 (Arcee)

    Qwen3.7 Max
    Trinity-Large-Preview24.0%
    Source

    Not directly comparable

Multilingual

  • MMLU-ProX

    Qwen3.7 Max87%
    Source
    Trinity-Large-Preview

    Not directly comparable

  • NOVA-63

    Qwen3.7 Max59.0%
    Source
    Trinity-Large-Preview

    Not directly comparable

  • INCLUDE

    Qwen3.7 Max86.2%
    Source
    Trinity-Large-Preview

    Not directly comparable

  • MAXIFE

    Qwen3.7 Max89.2%
    Source
    Trinity-Large-Preview

    Not directly comparable

  • PolyMath

    Qwen3.7 Max86.5%
    Source
    Trinity-Large-Preview

    Not directly comparable

Instruction following

  • IFEval

    Qwen3.7 Max94.3%
    Source
    Trinity-Large-Preview

    Not directly comparable

  • IFBench

    Qwen3.7 Max79.1%
    Source
    Trinity-Large-Preview

    Not directly comparable

Frequently asked questions

Which is better, Qwen3.7 Max or Trinity-Large-Preview?

Qwen3.7 Max has the higher public score estimate, 71.6 versus 56.39, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Qwen3.7 Max or Trinity-Large-Preview?

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 Trinity-Large-Preview?

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 Trinity-Large-Preview?

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 Trinity-Large-Preview?

Qwen3.7 Max has the larger documented context window: 1M, compared with 512K.

Related comparisons

Last updated August 18, 2026

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