Model A

Ornith AI

68.5/100

Estimated · Public rank #21

90% interval 58.6–78.4

Ornith-1.5-397B vs Qwen3.6-27B

Updated August 19, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B

Qwen3.6-27B

Alibaba

53.7/100

Estimated · Public rank #106

90% interval 42.2–65.2

Decision reading

Ornith-1.5-397B has the higher public score estimate, 68.5 versus 53.71, but the 90% score intervals overlap. Treat that as a lead, not a settled 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.

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
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
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
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.

11 Ornith-1.5-397B only

7 Shared

31 Qwen3.6-27B only

Shared results: 7
Ornith-1.5-397B only: 11
Qwen3.6-27B only: 31
Like-for-like categories: 0 / 8

2 categories use 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-1.5-397B: 78.0
Qwen3.6-27B: 77.5
Weighted basis: 2 vs 3 rows
Reading: Directional only

Knowledge

Directional only

Ornith-1.5-397B: 51.1
Qwen3.6-27B: 53.3
Weighted basis: 2 vs 4 rows
Reading: Directional only

Agentic

Not comparable

Ornith-1.5-397B: 86.6
Qwen3.6-27B: 59.3
Weighted basis: 1 vs 1 rows
Reading: Not comparable

Reasoning

Not comparable

Ornith-1.5-397B: Not measured
Qwen3.6-27B: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Math

Not comparable

Ornith-1.5-397B: Not measured
Qwen3.6-27B: 89.2
Weighted basis: 0 vs 2 rows
Reading: Not comparable

Multilingual

Not comparable

Ornith-1.5-397B: Not measured
Qwen3.6-27B: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Multimodal

Not comparable

Ornith-1.5-397B: Not measured
Qwen3.6-27B: 76.7
Weighted basis: 0 vs 2 rows
Reading: Not comparable

Instruction following

Not comparable

Ornith-1.5-397B: Not measured
Qwen3.6-27B: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Category averages with the server-provided evidence basis for Ornith-1.5-397B and Qwen3.6-27B
Category	Ornith-1.5-397B	Qwen3.6-27B	Weighted basis	Reading
Coding	78.0	77.5	Directional only2 vs 3 rows	Directional only
Knowledge	51.1	53.3	Directional only2 vs 4 rows	Directional only
Agentic	86.6	59.3	Not comparable1 vs 1 rows	Not comparable
Reasoning	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable
Math	Not measured	89.2	Not comparable0 vs 2 rows	Not comparable
Multilingual	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable
Multimodal	Not measured	76.7	Not comparable0 vs 2 rows	Not comparable
Instruction following	Not measured	Not measured	Not comparable0 vs 0 rows	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.

HLE
Knowledge
Ornith-1.5-397B: 44.6%Qwen3.6-27B: 24%Normalized gap 20.6Ornith-1.5-397B source Qwen3.6-27B source
SWE-bench Pro
Coding
Ornith-1.5-397B: 65.1%Qwen3.6-27B: 53.5%Normalized gap 11.6Ornith-1.5-397B source Qwen3.6-27B source
SWE-bench Verified
Coding
Ornith-1.5-397B: 86%Qwen3.6-27B: 77.2%Normalized gap 8.8Ornith-1.5-397B source Qwen3.6-27B source
GPQA
Knowledge
Ornith-1.5-397B: 92.8%Qwen3.6-27B: 87.8%Normalized gap 5.0Ornith-1.5-397B source Qwen3.6-27B source

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-1.5-397B: Self-hosted; infrastructure cost varies; Fits in one request
Qwen3.6-27B: Self-hosted; infrastructure cost varies; Fits in one request

Ornith-1.5-397B 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-1.5-397B: Self-hosted; infrastructure cost varies; Fits in one request
Qwen3.6-27B: Self-hosted; infrastructure cost varies; Fits in one request

Ornith-1.5-397B 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-1.5-397B: 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-1.5-397B 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.

SpecificationOrnith-1.5-397BQwen3.6-27B

Context window

Maximum documented context; output-token limits may be lower.

Ornith-1.5-397B

262K

Ornith-1.5-397B model card

Qwen3.6-27B

262K

API model ID

Ornith-1.5-397B

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-1.5-397B

No comparable hosted API rate

Ornith-1.5-397B model card

Qwen3.6-27B

No comparable hosted API rate

Documented inputs

Ornith-1.5-397B

Not sourced

Qwen3.6-27B

Not sourced

Documented outputs

Ornith-1.5-397B

Not sourced

Qwen3.6-27B

Not sourced

Provider availability

Ornith-1.5-397B

Not sourced

Qwen3.6-27B

Not sourced

Reasoning profile

Ornith-1.5-397B

Reasoning

Qwen3.6-27B

Reasoning

Weight access

Ornith-1.5-397B

Open Weight

Qwen3.6-27B

Open Weight

License

Ornith-1.5-397B

Open Weight

Qwen3.6-27B

Open Weight

Release date

Ornith-1.5-397B

2026-08-18

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: Ornith-1.5-397B has the higher public score estimate, 68.5 versus 53.71, but the 90% score intervals overlap.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: Both models list 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-1.5-397B

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 evidence49 rows

Agentic

Terminal-Bench 2.1
Ornith-1.5-397B86.1%
Source
Qwen3.6-27B—
Not directly comparable
HLE w/ tools
Ornith-1.5-397B56.1%
Source
Qwen3.6-27B—
Not directly comparable
MCP Atlas
Ornith-1.5-397B80%
Source
Qwen3.6-27B—
Not directly comparable
Toolathlon-Verified
Ornith-1.5-397B71.2%
Source
Qwen3.6-27B—
Not directly comparable
WideResearch
Ornith-1.5-397B80.8%
Source
Qwen3.6-27B—
Not directly comparable
BrowseComp
Ornith-1.5-397B86.6%
Source
Qwen3.6-27B—
Not directly comparable
Claw-Eval
Ornith-1.5-397B81.4%
Source
Qwen3.6-27B72.4%
Source
Ornith-1.5-397B leads this result
Terminal-Bench 2.0
Ornith-1.5-397B—
Qwen3.6-27B59.3%
Source
Not directly comparable
QwenClawBench
Ornith-1.5-397B—
Qwen3.6-27B53.4%
Source
Not directly comparable
QwenWebBench
Ornith-1.5-397B—
Qwen3.6-27B1487
Source
Not directly comparable
AndroidWorld
Ornith-1.5-397B—
Qwen3.6-27B70.3%
Source
Not directly comparable
Gert Labs
Ornith-1.5-397B—
Qwen3.6-27B54.84%
Source
Not directly comparable

Coding

Terminal-Bench 2.1
Ornith-1.5-397B86.1%
Source
Qwen3.6-27B—
Not directly comparable
SWE-bench Verified
Ornith-1.5-397B86%
Source
Qwen3.6-27B77.2%
Source
Ornith-1.5-397B leads this result
SWE-bench Pro
Ornith-1.5-397B65.1%
Source
Qwen3.6-27B53.5%
Source
Ornith-1.5-397B leads this result
SWE Multilingual
Ornith-1.5-397B79.6%
Source
Qwen3.6-27B71.3%
Source
Ornith-1.5-397B leads this result
deepSwe
Ornith-1.5-397B56%
Source
Qwen3.6-27B—
Not directly comparable
frontierBench
Ornith-1.5-397B13.5%
Source
Qwen3.6-27B—
Not directly comparable
NL2Repo
Ornith-1.5-397B59.5%
Source
Qwen3.6-27B36.2%
Source
Ornith-1.5-397B leads this result
Terminal-Bench 2.0
Ornith-1.5-397B—
Qwen3.6-27B59.3%
Source
Not directly comparable
LiveCodeBench
Ornith-1.5-397B—
Qwen3.6-27B83.9%
Source
Not directly comparable

Knowledge

GPQA
Ornith-1.5-397B92.8%
Source
Qwen3.6-27B87.8%
Source
Ornith-1.5-397B leads this result
GPQA-D
Ornith-1.5-397B92.8%
Source
Qwen3.6-27B—
Not directly comparable
HLE
Ornith-1.5-397B44.6%
Source
Qwen3.6-27B24%
Source
Ornith-1.5-397B leads this result
HLE w/o tools
Ornith-1.5-397B44.6%
Source
Qwen3.6-27B—
Not directly comparable
MMLU-Pro
Ornith-1.5-397B—
Qwen3.6-27B86.2%
Source
Not directly comparable
MMLU-Redux
Ornith-1.5-397B—
Qwen3.6-27B93.5%
Source
Not directly comparable
SuperGPQA
Ornith-1.5-397B—
Qwen3.6-27B66%
Source
Not directly comparable
C-Eval
Ornith-1.5-397B—
Qwen3.6-27B91.4%
Source
Not directly comparable

Math

HMMT Feb 2025
Ornith-1.5-397B—
Qwen3.6-27B93.8%
Source
Not directly comparable
HMMT Nov 2025
Ornith-1.5-397B—
Qwen3.6-27B90.7%
Source
Not directly comparable
HMMT Feb 2026
Ornith-1.5-397B—
Qwen3.6-27B84.3%
Source
Not directly comparable
MMAnswerBench
Ornith-1.5-397B—
Qwen3.6-27B80.8%
Source
Not directly comparable
AIME26
Ornith-1.5-397B—
Qwen3.6-27B94.1%
Source
Not directly comparable

Multimodal

MMMU
Ornith-1.5-397B—
Qwen3.6-27B82.9%
Source
Not directly comparable
MMMU-Pro
Ornith-1.5-397B—
Qwen3.6-27B75.8%
Source
Not directly comparable
RealWorldQA
Ornith-1.5-397B—
Qwen3.6-27B84.1%
Source
Not directly comparable
DynaMath
Ornith-1.5-397B—
Qwen3.6-27B85.6%
Source
Not directly comparable
MStar
Ornith-1.5-397B—
Qwen3.6-27B81.4%
Source
Not directly comparable
SimpleVQA
Ornith-1.5-397B—
Qwen3.6-27B56.1%
Source
Not directly comparable
CharXiv
Ornith-1.5-397B—
Qwen3.6-27B78.4%
Source
Not directly comparable
CC-OCR
Ornith-1.5-397B—
Qwen3.6-27B81.2%
Source
Not directly comparable
CountBench
Ornith-1.5-397B—
Qwen3.6-27B97.8%
Source
Not directly comparable
RefCOCO (avg)
Ornith-1.5-397B—
Qwen3.6-27B92.5%
Source
Not directly comparable
ERQA
Ornith-1.5-397B—
Qwen3.6-27B62.5%
Source
Not directly comparable
Video-MME (with subtitle)
Ornith-1.5-397B—
Qwen3.6-27B87.7%
Source
Not directly comparable
VideoMMMU
Ornith-1.5-397B—
Qwen3.6-27B84.4%
Source
Not directly comparable
MLVU (M-Avg)
Ornith-1.5-397B—
Qwen3.6-27B86.6%
Source
Not directly comparable
V*
Ornith-1.5-397B—
Qwen3.6-27B94.7%
Source
Not directly comparable

Frequently asked questions

Which is better, Ornith-1.5-397B or Qwen3.6-27B?

Ornith-1.5-397B has the higher public score estimate, 68.5 versus 53.71, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Ornith-1.5-397B 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-1.5-397B or Qwen3.6-27B?

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-1.5-397B 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-1.5-397B or Qwen3.6-27B?

Both models list the same context window, 262K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 19, 2026

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Ornith-1.5-397B vs Qwen3.6-27B

Which one for your work

Coding work

Agentic work

Long documents

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

What is actually comparable

Category results, on a stated basis

Coding

Knowledge

Agentic

Reasoning

Math

Multilingual

Multimodal

Instruction following

Shape of the matched evidence

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Knowledge

Math

Multimodal

Frequently asked questions

Which is better, Ornith-1.5-397B or Qwen3.6-27B?

Which is better for coding, Ornith-1.5-397B or Qwen3.6-27B?

Which is better for agentic tasks, Ornith-1.5-397B or Qwen3.6-27B?

Which costs less, Ornith-1.5-397B or Qwen3.6-27B?

Which has the larger context window, Ornith-1.5-397B or Qwen3.6-27B?

Related comparisons

Watch Ornith-1.5-397B vs Qwen3.6-27B