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Model comparison

Ornith-1.0-35B vs Qwen3.8 Max

Updated August 3, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Ornith-1.0-35B

DeepReinforce AI

—

Evidence status unavailable

90% interval unavailable

Qwen3.8 Max

Alibaba

65.4/100

Estimated · Public rank #31

90% interval 55.5–75.3

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.

2 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

    Qwen3.8 Max

    Qwen3.8 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

    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
2
Ornith-1.0-35B only
5
Qwen3.8 Max only
50
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.

Coding

Directional only
Ornith-1.0-35B
65.9
Qwen3.8 Max
67.7
Weighted basis
2 vs 1 rows
Reading
Directional only

Agentic

Not comparable
Ornith-1.0-35B
64.2
Qwen3.8 Max
86.1
Weighted basis
1 vs 1 rows
Reading
Not comparable

Reasoning

Not comparable
Ornith-1.0-35B
Not measured
Qwen3.8 Max
78.3
Weighted basis
0 vs 2 rows
Reading
Not comparable

Knowledge

Not comparable
Ornith-1.0-35B
Not measured
Qwen3.8 Max
50.2
Weighted basis
0 vs 2 rows
Reading
Not comparable

Math

Not comparable
Ornith-1.0-35B
Not measured
Qwen3.8 Max
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Ornith-1.0-35B
Not measured
Qwen3.8 Max
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Ornith-1.0-35B
Not measured
Qwen3.8 Max
86.3
Weighted basis
0 vs 2 rows
Reading
Not comparable

Instruction following

Not comparable
Ornith-1.0-35B
Not measured
Qwen3.8 Max
82.8
Weighted basis
0 vs 1 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

Ornith-1.0-35B
Self-hosted; infrastructure cost varies
Fits in one request
Qwen3.8 Max
API rate not published
Fits in one request

Ornith-1.0-35B has no comparable published API token rate. Qwen3.8 Max has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Ornith-1.0-35B
Self-hosted; infrastructure cost varies
Fits in one request
Qwen3.8 Max
API rate not published
Fits in one request

Ornith-1.0-35B has no comparable published API token rate. Qwen3.8 Max has no comparable published API token rate.

Cache-heavy agent loop

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

Ornith-1.0-35B
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable
Qwen3.8 Max
API rate not published
Fits in one request
Cached-input rate unavailable

Ornith-1.0-35B has no comparable published API token rate. Qwen3.8 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.

Cached-input rate

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

Ornith-1.0-35B

No comparable hosted API rate

Qwen3.8 Max

No comparable hosted API rate

Alibaba Cloud Model Studio pricing

Documented inputs

Ornith-1.0-35B

Not sourced

Qwen3.8 Max

Not sourced

Documented outputs

Ornith-1.0-35B

Not sourced

Qwen3.8 Max

Not sourced

Provider availability

Ornith-1.0-35B

Not sourced

Qwen3.8 Max

Not sourced

Reasoning profile

Ornith-1.0-35B

Reasoning

Qwen3.8 Max

Reasoning

Weight access

Ornith-1.0-35B

Open Weight

Qwen3.8 Max

Proprietary

License

Ornith-1.0-35B

Open Weight

Qwen3.8 Max

Proprietary

Release date

Ornith-1.0-35B

2026-06-01

Qwen3.8 Max

2026-08-03

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
Qwen3.8 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 evidence57 rows

Agentic

  • Terminal-Bench 2.0

    Ornith-1.0-35B64.2%
    Source
    Qwen3.8 Max—

    Not directly comparable

  • Claw-Eval

    Ornith-1.0-35B69.8%
    Source
    Qwen3.8 Max—

    Not directly comparable

  • Terminal-Bench 2.1

    Ornith-1.0-35B—
    Qwen3.8 Max86.6%
    Source

    Not directly comparable

  • CoWorkBench

    Ornith-1.0-35B—
    Qwen3.8 Max74.8%
    Source

    Not directly comparable

  • JobBench

    Ornith-1.0-35B—
    Qwen3.8 Max53.4%
    Source

    Not directly comparable

  • skillsBench

    Ornith-1.0-35B—
    Qwen3.8 Max70.2%
    Source

    Not directly comparable

  • Agents' Last Exam

    Ornith-1.0-35B—
    Qwen3.8 Max52.4%
    Source

    Not directly comparable

  • AutomationBench

    Ornith-1.0-35B—
    Qwen3.8 Max27.3%
    Source

    Not directly comparable

  • Toolathlon-Verified

    Ornith-1.0-35B—
    Qwen3.8 Max72.5%
    Source

    Not directly comparable

  • WideResearch

    Ornith-1.0-35B—
    Qwen3.8 Max81.9%
    Source

    Not directly comparable

  • HLE w/ tools

    Ornith-1.0-35B—
    Qwen3.8 Max56.2%
    Source

    Not directly comparable

  • OSWorld-Verified

    Ornith-1.0-35B—
    Qwen3.8 Max86.1%
    Source

    Not directly comparable

  • OSWorld 2.0

    Ornith-1.0-35B—
    Qwen3.8 Max19.4%
    Source

    Not directly comparable

  • WebArena-Verified

    Ornith-1.0-35B—
    Qwen3.8 Max66.8%
    Source

    Not directly comparable

  • AndroidWorld

    Ornith-1.0-35B—
    Qwen3.8 Max85.3%
    Source

    Not directly comparable

  • MobileWorld

    Ornith-1.0-35B—
    Qwen3.8 Max77.8%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Ornith-1.0-35B75.6%
    Source
    Qwen3.8 Max—

    Not directly comparable

  • SWE-bench Pro

    Ornith-1.0-35B50.4%
    Source
    Qwen3.8 Max67.7%
    Source

    Qwen3.8 Max leads this result

  • SWE Multilingual

    Ornith-1.0-35B69.3%
    Source
    Qwen3.8 Max—

    Not directly comparable

  • NL2Repo

    Ornith-1.0-35B34.6%
    Source
    Qwen3.8 Max55.9%
    Source

    Qwen3.8 Max leads this result

  • Terminal-Bench 2.0

    Ornith-1.0-35B64.2%
    Source
    Qwen3.8 Max—

    Not directly comparable

  • Terminal-Bench 2.1

    Ornith-1.0-35B—
    Qwen3.8 Max86.6%
    Source

    Not directly comparable

  • deepSwe

    Ornith-1.0-35B—
    Qwen3.8 Max56.6%
    Source

    Not directly comparable

  • FrontierSWE

    Ornith-1.0-35B—
    Qwen3.8 Max73.5%
    Source

    Not directly comparable

  • MLS-Bench Lite

    Ornith-1.0-35B—
    Qwen3.8 Max41.0%
    Source

    Not directly comparable

  • PaperBench

    Ornith-1.0-35B—
    Qwen3.8 Max93.0%
    Source

    Not directly comparable

Reasoning

  • MRCRv2

    Ornith-1.0-35B—
    Qwen3.8 Max92.9%
    Source

    Not directly comparable

  • LongBench v2

    Ornith-1.0-35B—
    Qwen3.8 Max66.3%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Ornith-1.0-35B—
    Qwen3.8 Max92.6%
    Source

    Not directly comparable

  • GPQA-D

    Ornith-1.0-35B—
    Qwen3.8 Max92.6%
    Source

    Not directly comparable

  • HLE

    Ornith-1.0-35B—
    Qwen3.8 Max43.6%
    Source

    Not directly comparable

  • HLE w/o tools

    Ornith-1.0-35B—
    Qwen3.8 Max43.6%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    Ornith-1.0-35B—
    Qwen3.8 Max82.3%
    Source

    Not directly comparable

  • MathVision

    Ornith-1.0-35B—
    Qwen3.8 Max95.2%
    Source

    Not directly comparable

  • MathVision w/ Python

    Ornith-1.0-35B—
    Qwen3.8 Max97.7%
    Source

    Not directly comparable

  • BabyVision

    Ornith-1.0-35B—
    Qwen3.8 Max82.0%
    Source

    Not directly comparable

  • BabyVision w/ Python

    Ornith-1.0-35B—
    Qwen3.8 Max91.3%
    Source

    Not directly comparable

  • ZeroBench

    Ornith-1.0-35B—
    Qwen3.8 Max24.0%
    Source

    Not directly comparable

  • ZeroBench w/ Python

    Ornith-1.0-35B—
    Qwen3.8 Max49.0%
    Source

    Not directly comparable

  • MedXpertQA (MM)

    Ornith-1.0-35B—
    Qwen3.8 Max80.4%
    Source

    Not directly comparable

  • ScreenSpot Pro

    Ornith-1.0-35B—
    Qwen3.8 Max84.5%
    Source

    Not directly comparable

  • Vision2Web

    Ornith-1.0-35B—
    Qwen3.8 Max69.0%
    Source

    Not directly comparable

  • CharXiv w/o tools

    Ornith-1.0-35B—
    Qwen3.8 Max88.4%
    Source

    Not directly comparable

  • CharXiv

    Ornith-1.0-35B—
    Qwen3.8 Max93.5%
    Source

    Not directly comparable

  • OmniDocBench 1.5

    Ornith-1.0-35B—
    Qwen3.8 Max92.1%
    Source

    Not directly comparable

  • OCRBench V2

    Ornith-1.0-35B—
    Qwen3.8 Max74.2%
    Source

    Not directly comparable

  • CC-OCR

    Ornith-1.0-35B—
    Qwen3.8 Max79.6%
    Source

    Not directly comparable

  • RealWorldQA

    Ornith-1.0-35B—
    Qwen3.8 Max88.0%
    Source

    Not directly comparable

  • ERQA

    Ornith-1.0-35B—
    Qwen3.8 Max77.8%
    Source

    Not directly comparable

  • SimpleVQA

    Ornith-1.0-35B—
    Qwen3.8 Max75.0%
    Source

    Not directly comparable

  • PerceptionBench

    Ornith-1.0-35B—
    Qwen3.8 Max63.5%
    Source

    Not directly comparable

  • Video-MME (with subtitle)

    Ornith-1.0-35B—
    Qwen3.8 Max90.4%
    Source

    Not directly comparable

  • VideoMMMU

    Ornith-1.0-35B—
    Qwen3.8 Max88.7%
    Source

    Not directly comparable

  • MMVU

    Ornith-1.0-35B—
    Qwen3.8 Max82.4%
    Source

    Not directly comparable

  • MLVU (M-Avg)

    Ornith-1.0-35B—
    Qwen3.8 Max90.8%
    Source

    Not directly comparable

  • LVBench

    Ornith-1.0-35B—
    Qwen3.8 Max81.8%
    Source

    Not directly comparable

Instruction following

  • IFBench

    Ornith-1.0-35B—
    Qwen3.8 Max82.8%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Ornith-1.0-35B or Qwen3.8 Max?

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, Ornith-1.0-35B or Qwen3.8 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, Ornith-1.0-35B or Qwen3.8 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, Ornith-1.0-35B or Qwen3.8 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, Ornith-1.0-35B or Qwen3.8 Max?

Qwen3.8 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 3, 2026

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