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BenchLM
Model A
o1-pro

OpenAI

46.1/100

Estimated · Public rank #158

90% interval 34.6–57.6

o1-pro 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 46.07, and the 90% score intervals do not overlap.

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

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. o1-pro does not fit this workload in one request. o1-pro has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.7 Max has no comparable published API token rate.

    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
o1-pro only
0
Qwen3.7 Max only
34
Like-for-like categories
0 / 8

1 category uses 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.

Knowledge

Directional only
o1-pro
79.0
Qwen3.7 Max
64.2
Weighted basis
1 vs 4 rows
Reading
Directional only

Agentic

Not comparable
o1-pro
Not measured
Qwen3.7 Max
69.7
Weighted basis
0 vs 1 rows
Reading
Not comparable

Coding

Not comparable
o1-pro
Not measured
Qwen3.7 Max
77.9
Weighted basis
0 vs 4 rows
Reading
Not comparable

Reasoning

Not comparable
o1-pro
Not measured
Qwen3.7 Max
90.4
Weighted basis
0 vs 1 rows
Reading
Not comparable

Math

Not comparable
o1-pro
Not measured
Qwen3.7 Max
97.1
Weighted basis
0 vs 1 rows
Reading
Not comparable

Multilingual

Not comparable
o1-pro
Not measured
Qwen3.7 Max
87.0
Weighted basis
0 vs 1 rows
Reading
Not comparable

Multimodal

Not comparable
o1-pro
Not measured
Qwen3.7 Max
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
o1-pro
Not measured
Qwen3.7 Max
84.4
Weighted basis
0 vs 2 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

o1-pro
$0.45
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

o1-pro
$9.30
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

o1-pro
$39.00
Does not fit in one request
Cached input priced at the published list-input rate
Qwen3.7 Max
API rate not published
Fits in one request
Cached-input rate unavailable

o1-pro does not fit this workload in one request. o1-pro 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.

o1-pro

200K

Qwen3.7 Max

1M

API model ID

o1-pro

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.

o1-pro

Not published

Qwen3.7 Max

No comparable hosted API rate

Documented inputs

o1-pro

Not sourced

Qwen3.7 Max

Not sourced

Documented outputs

o1-pro

Not sourced

Qwen3.7 Max

Not sourced

Provider availability

o1-pro

Not sourced

Qwen3.7 Max

Not sourced

Reasoning profile

o1-pro

Reasoning

Qwen3.7 Max

Reasoning

Weight access

o1-pro

Proprietary

Qwen3.7 Max

Proprietary

License

o1-pro

Proprietary

Qwen3.7 Max

Proprietary

Release date

o1-pro

2024-12-01

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 46.07, 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.

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

    o1-pro—
    Qwen3.7 Max69.7%
    Source

    Not directly comparable

  • QwenClawBench

    o1-pro—
    Qwen3.7 Max64.3%
    Source

    Not directly comparable

  • Claw-Eval

    o1-pro—
    Qwen3.7 Max65.2%
    Source

    Not directly comparable

  • BFCL v4

    o1-pro—
    Qwen3.7 Max75.0%
    Source

    Not directly comparable

  • MCP Atlas

    o1-pro—
    Qwen3.7 Max76.4%
    Source

    Not directly comparable

  • VITA-Bench

    o1-pro—
    Qwen3.7 Max47.9%
    Source

    Not directly comparable

  • HLE w/ tools

    o1-pro—
    Qwen3.7 Max53.5%
    Source

    Not directly comparable

  • Gert Labs

    o1-pro—
    Qwen3.7 Max64.27%
    Source

    Not directly comparable

  • ResearchClawBench

    o1-pro—
    Qwen3.7 Max18.7%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    o1-pro—
    Qwen3.7 Max80.4%
    Source

    Not directly comparable

  • SWE-bench Pro

    o1-pro—
    Qwen3.7 Max60.6%
    Source

    Not directly comparable

  • SWE Multilingual

    o1-pro—
    Qwen3.7 Max78.3%
    Source

    Not directly comparable

  • NL2Repo

    o1-pro—
    Qwen3.7 Max47.2%
    Source

    Not directly comparable

  • SciCode

    o1-pro—
    Qwen3.7 Max53.5%
    Source

    Not directly comparable

  • LiveCodeBench

    o1-pro—
    Qwen3.7 Max91.6%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    o1-pro—
    Qwen3.7 Max69.7%
    Source

    Not directly comparable

Reasoning

  • MRCRv2

    o1-pro—
    Qwen3.7 Max90.4%
    Source

    Not directly comparable

  • CritPt

    o1-pro—
    Qwen3.7 Max13.4%
    Source

    Not directly comparable

Knowledge

  • GPQA

    o1-pro79%
    Source
    Qwen3.7 Max92.4%
    Source

    Qwen3.7 Max leads this result

  • GPQA-D

    o1-pro—
    Qwen3.7 Max92.4%
    Source

    Not directly comparable

  • HLE

    o1-pro—
    Qwen3.7 Max41.4%
    Source

    Not directly comparable

  • MMLU-Pro

    o1-pro—
    Qwen3.7 Max89.6%
    Source

    Not directly comparable

  • MMLU-Redux

    o1-pro—
    Qwen3.7 Max95%
    Source

    Not directly comparable

  • SuperGPQA

    o1-pro—
    Qwen3.7 Max73.6%
    Source

    Not directly comparable

  • MMMLU

    o1-pro—
    Qwen3.7 Max90.3%
    Source

    Not directly comparable

Math

  • HMMT Feb 2026

    o1-pro—
    Qwen3.7 Max97.1%
    Source

    Not directly comparable

  • IMOAnswerBench

    o1-pro—
    Qwen3.7 Max90.0%
    Source

    Not directly comparable

  • Apex

    o1-pro—
    Qwen3.7 Max44.5%
    Source

    Not directly comparable

Multilingual

  • MMLU-ProX

    o1-pro—
    Qwen3.7 Max87%
    Source

    Not directly comparable

  • NOVA-63

    o1-pro—
    Qwen3.7 Max59.0%
    Source

    Not directly comparable

  • INCLUDE

    o1-pro—
    Qwen3.7 Max86.2%
    Source

    Not directly comparable

  • MAXIFE

    o1-pro—
    Qwen3.7 Max89.2%
    Source

    Not directly comparable

  • PolyMath

    o1-pro—
    Qwen3.7 Max86.5%
    Source

    Not directly comparable

Instruction following

  • IFEval

    o1-pro—
    Qwen3.7 Max94.3%
    Source

    Not directly comparable

  • IFBench

    o1-pro—
    Qwen3.7 Max79.1%
    Source

    Not directly comparable

Frequently asked questions

Which is better, o1-pro or Qwen3.7 Max?

Qwen3.7 Max has the higher public score, 71.6 versus 46.07, 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, o1-pro or Qwen3.7 Max?

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, o1-pro 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, o1-pro 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, o1-pro or Qwen3.7 Max?

Qwen3.7 Max has the larger documented context window: 1M, compared with 200K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 18, 2026

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