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Model A
Ornith-1.0-9B

DeepReinforce AI

Evidence status unavailable

90% interval unavailable

Ornith-1.0-9B vs Qwen3.6-27B

Updated August 13, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
Qwen3.6-27B

Alibaba

52.9/100

Estimated · Public rank #102

90% interval 41.4–64.5

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.

7 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

    Qwen3.6-27B

    Qwen3.6-27B leads on the same 1 weighted benchmark row.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    Qwen3.6-27B

    Qwen3.6-27B 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

  • 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
7
Ornith-1.0-9B only
0
Qwen3.6-27B only
31
Like-for-like categories
1 / 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.

Agentic

Like-for-like
Ornith-1.0-9B
43.1
Qwen3.6-27B
59.3
Weighted basis
1 vs 1 rows
Reading
Qwen3.6-27B leads

Coding

Directional only
Ornith-1.0-9B
59.2
Qwen3.6-27B
77.5
Weighted basis
2 vs 3 rows
Reading
Directional only

Reasoning

Not comparable
Ornith-1.0-9B
Not measured
Qwen3.6-27B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Ornith-1.0-9B
Not measured
Qwen3.6-27B
53.3
Weighted basis
0 vs 4 rows
Reading
Not comparable

Math

Not comparable
Ornith-1.0-9B
Not measured
Qwen3.6-27B
89.2
Weighted basis
0 vs 2 rows
Reading
Not comparable

Multilingual

Not comparable
Ornith-1.0-9B
Not measured
Qwen3.6-27B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Ornith-1.0-9B
Not measured
Qwen3.6-27B
76.7
Weighted basis
0 vs 2 rows
Reading
Not comparable

Instruction following

Not comparable
Ornith-1.0-9B
Not measured
Qwen3.6-27B
Not measured
Weighted basis
0 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

Ornith-1.0-9B
Self-hosted; infrastructure cost varies
Fits in one request
Qwen3.6-27B
Self-hosted; infrastructure cost varies
Fits in one request

Ornith-1.0-9B has no comparable published API token rate. Qwen3.6-27B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Ornith-1.0-9B
Self-hosted; infrastructure cost varies
Fits in one request
Qwen3.6-27B
Self-hosted; infrastructure cost varies
Fits in one request

Ornith-1.0-9B has no comparable published API token rate. Qwen3.6-27B has no comparable published API token rate.

Cache-heavy agent loop

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

Ornith-1.0-9B
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable
Qwen3.6-27B
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable

Ornith-1.0-9B has no comparable published API token rate. Qwen3.6-27B 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.

Ornith-1.0-9B

256K

Qwen3.6-27B

262K

API model ID

Ornith-1.0-9B

Not sourced

Qwen3.6-27B

Not sourced

Cached-input rate

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

Ornith-1.0-9B

No comparable hosted API rate

Qwen3.6-27B

No comparable hosted API rate

Documented inputs

Ornith-1.0-9B

Not sourced

Qwen3.6-27B

Not sourced

Documented outputs

Ornith-1.0-9B

Not sourced

Qwen3.6-27B

Not sourced

Provider availability

Ornith-1.0-9B

Not sourced

Qwen3.6-27B

Not sourced

Reasoning profile

Ornith-1.0-9B

Reasoning

Qwen3.6-27B

Reasoning

Weight access

Ornith-1.0-9B

Open Weight

Qwen3.6-27B

Open Weight

License

Ornith-1.0-9B

Open Weight

Qwen3.6-27B

Open Weight

Release date

Ornith-1.0-9B

2026-06-01

Qwen3.6-27B

2026-04-21

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
Qwen3.6-27B has the larger documented window (262K).

Run the same representative tasks against both endpoints before changing production traffic.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

Ornith-1.0-9B
API / mo$0
Self-host / moNot listed
Break-even
Proprietary model — self-hosting not applicable.
Qwen3.6-27B
API / mo$0
Self-host / mo$429
Break-even
Model the full break-even

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

    Ornith-1.0-9B43.1%
    Source
    Qwen3.6-27B59.3%
    Source

    Qwen3.6-27B leads this result

  • Claw-Eval

    Ornith-1.0-9B63.1%
    Source
    Qwen3.6-27B72.4%
    Source

    Qwen3.6-27B leads this result

  • QwenClawBench

    Ornith-1.0-9B
    Qwen3.6-27B53.4%
    Source

    Not directly comparable

  • QwenWebBench

    Ornith-1.0-9B
    Qwen3.6-27B1487
    Source

    Not directly comparable

  • AndroidWorld

    Ornith-1.0-9B
    Qwen3.6-27B70.3%
    Source

    Not directly comparable

  • Gert Labs

    Ornith-1.0-9B
    Qwen3.6-27B54.84%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Ornith-1.0-9B69.4%
    Source
    Qwen3.6-27B77.2%
    Source

    Qwen3.6-27B leads this result

  • SWE-bench Pro

    Ornith-1.0-9B42.9%
    Source
    Qwen3.6-27B53.5%
    Source

    Qwen3.6-27B leads this result

  • SWE Multilingual

    Ornith-1.0-9B52%
    Source
    Qwen3.6-27B71.3%
    Source

    Qwen3.6-27B leads this result

  • NL2Repo

    Ornith-1.0-9B27.2%
    Source
    Qwen3.6-27B36.2%
    Source

    Qwen3.6-27B leads this result

  • Terminal-Bench 2.0

    Ornith-1.0-9B43.1%
    Source
    Qwen3.6-27B59.3%
    Source

    Qwen3.6-27B leads this result

  • LiveCodeBench

    Ornith-1.0-9B
    Qwen3.6-27B83.9%
    Source

    Not directly comparable

Knowledge

  • MMLU-Pro

    Ornith-1.0-9B
    Qwen3.6-27B86.2%
    Source

    Not directly comparable

  • MMLU-Redux

    Ornith-1.0-9B
    Qwen3.6-27B93.5%
    Source

    Not directly comparable

  • SuperGPQA

    Ornith-1.0-9B
    Qwen3.6-27B66%
    Source

    Not directly comparable

  • C-Eval

    Ornith-1.0-9B
    Qwen3.6-27B91.4%
    Source

    Not directly comparable

  • GPQA

    Ornith-1.0-9B
    Qwen3.6-27B87.8%
    Source

    Not directly comparable

  • HLE

    Ornith-1.0-9B
    Qwen3.6-27B24%
    Source

    Not directly comparable

Math

  • HMMT Feb 2025

    Ornith-1.0-9B
    Qwen3.6-27B93.8%
    Source

    Not directly comparable

  • HMMT Nov 2025

    Ornith-1.0-9B
    Qwen3.6-27B90.7%
    Source

    Not directly comparable

  • HMMT Feb 2026

    Ornith-1.0-9B
    Qwen3.6-27B84.3%
    Source

    Not directly comparable

  • MMAnswerBench

    Ornith-1.0-9B
    Qwen3.6-27B80.8%
    Source

    Not directly comparable

  • AIME26

    Ornith-1.0-9B
    Qwen3.6-27B94.1%
    Source

    Not directly comparable

Multimodal

  • MMMU

    Ornith-1.0-9B
    Qwen3.6-27B82.9%
    Source

    Not directly comparable

  • MMMU-Pro

    Ornith-1.0-9B
    Qwen3.6-27B75.8%
    Source

    Not directly comparable

  • RealWorldQA

    Ornith-1.0-9B
    Qwen3.6-27B84.1%
    Source

    Not directly comparable

  • DynaMath

    Ornith-1.0-9B
    Qwen3.6-27B85.6%
    Source

    Not directly comparable

  • MStar

    Ornith-1.0-9B
    Qwen3.6-27B81.4%
    Source

    Not directly comparable

  • SimpleVQA

    Ornith-1.0-9B
    Qwen3.6-27B56.1%
    Source

    Not directly comparable

  • CharXiv

    Ornith-1.0-9B
    Qwen3.6-27B78.4%
    Source

    Not directly comparable

  • CC-OCR

    Ornith-1.0-9B
    Qwen3.6-27B81.2%
    Source

    Not directly comparable

  • CountBench

    Ornith-1.0-9B
    Qwen3.6-27B97.8%
    Source

    Not directly comparable

  • RefCOCO (avg)

    Ornith-1.0-9B
    Qwen3.6-27B92.5%
    Source

    Not directly comparable

  • ERQA

    Ornith-1.0-9B
    Qwen3.6-27B62.5%
    Source

    Not directly comparable

  • Video-MME (with subtitle)

    Ornith-1.0-9B
    Qwen3.6-27B87.7%
    Source

    Not directly comparable

  • VideoMMMU

    Ornith-1.0-9B
    Qwen3.6-27B84.4%
    Source

    Not directly comparable

  • MLVU (M-Avg)

    Ornith-1.0-9B
    Qwen3.6-27B86.6%
    Source

    Not directly comparable

  • V*

    Ornith-1.0-9B
    Qwen3.6-27B94.7%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Ornith-1.0-9B or Qwen3.6-27B?

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-1.0-9B or Qwen3.6-27B?

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-1.0-9B or Qwen3.6-27B?

Qwen3.6-27B leads the like-for-like agentic tasks comparison across 1 shared weighted benchmark row.

Which costs less, Ornith-1.0-9B or Qwen3.6-27B?

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-1.0-9B or Qwen3.6-27B?

Qwen3.6-27B has the larger documented context window: 262K, compared with 256K.

Related comparisons

Last updated August 13, 2026

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