Model A

Ornith AI

68.5/100

Estimated · Public rank #21

90% interval 58.6–78.4

Ornith-1.5-397B vs Qwen3.8 Max

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

Model B

Qwen3.8 Max

Alibaba

79.9/100

Supported · Public rank #6

90% interval 77.8–82.1

Decision reading

Qwen3.8 Max has the higher public score estimate, 79.94 versus 68.5, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

12 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.

6 Ornith-1.5-397B only

12 Shared

40 Qwen3.8 Max only

Shared results: 12
Ornith-1.5-397B only: 6
Qwen3.8 Max only: 40
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.

Knowledge

Like-for-like

Ornith-1.5-397B: 51.1
Qwen3.8 Max: 50.2
Weighted basis: 2 vs 2 rows
Reading: Ornith-1.5-397B leads

Coding

Directional only

Ornith-1.5-397B: 78.0
Qwen3.8 Max: 67.7
Weighted basis: 2 vs 1 rows
Reading: Directional only

Agentic

Not comparable

Ornith-1.5-397B: 86.6
Qwen3.8 Max: 86.1
Weighted basis: 1 vs 1 rows
Reading: Not comparable

Reasoning

Not comparable

Ornith-1.5-397B: Not measured
Qwen3.8 Max: 78.3
Weighted basis: 0 vs 2 rows
Reading: Not comparable

Math

Not comparable

Ornith-1.5-397B: Not measured
Qwen3.8 Max: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Multilingual

Not comparable

Ornith-1.5-397B: Not measured
Qwen3.8 Max: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Multimodal

Not comparable

Ornith-1.5-397B: Not measured
Qwen3.8 Max: 86.3
Weighted basis: 0 vs 2 rows
Reading: Not comparable

Instruction following

Not comparable

Ornith-1.5-397B: Not measured
Qwen3.8 Max: 82.8
Weighted basis: 0 vs 1 rows
Reading: Not comparable

Category averages with the server-provided evidence basis for Ornith-1.5-397B and Qwen3.8 Max
Category	Ornith-1.5-397B	Qwen3.8 Max	Weighted basis	Reading
Knowledge	51.1	50.2	Like-for-like2 vs 2 rows	Ornith-1.5-397B leads
Coding	78.0	67.7	Directional only2 vs 1 rows	Directional only
Agentic	86.6	86.1	Not comparable1 vs 1 rows	Not comparable
Reasoning	Not measured	78.3	Not comparable0 vs 2 rows	Not comparable
Math	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable
Multilingual	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable
Multimodal	Not measured	86.3	Not comparable0 vs 2 rows	Not comparable
Instruction following	Not measured	82.8	Not comparable0 vs 1 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.

SWE-bench Pro
Coding
Ornith-1.5-397B: 65.1%Qwen3.8 Max: 67.7%Normalized gap 2.6Ornith-1.5-397B source Qwen3.8 Max source
HLE
Knowledge
Ornith-1.5-397B: 44.6%Qwen3.8 Max: 43.6%Normalized gap 1.0Ornith-1.5-397B source Qwen3.8 Max source
GPQA
Knowledge
Ornith-1.5-397B: 92.8%Qwen3.8 Max: 92.6%Normalized gap 0.2Ornith-1.5-397B source Qwen3.8 Max 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
Qwen3.8 Max: API rate not published; Fits in one request

Ornith-1.5-397B 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-1.5-397B: Self-hosted; infrastructure cost varies; Fits in one request
Qwen3.8 Max: API rate not published; Fits in one request

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

SpecificationOrnith-1.5-397BQwen3.8 Max

Context window

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

Ornith-1.5-397B

262K

Ornith-1.5-397B model card

Qwen3.8 Max

Alibaba Cloud Model Studio pricing

API model ID

Ornith-1.5-397B

Not sourced

Qwen3.8 Max

qwen3.8-max

Alibaba Cloud Model Studio pricing

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

Qwen3.8 Max

No comparable hosted API rate

Alibaba Cloud Model Studio pricing

Documented inputs

Ornith-1.5-397B

Not sourced

Qwen3.8 Max

Not sourced

Documented outputs

Ornith-1.5-397B

Not sourced

Qwen3.8 Max

Not sourced

Provider availability

Ornith-1.5-397B

Not sourced

Qwen3.8 Max

Not sourced

Reasoning profile

Ornith-1.5-397B

Reasoning

Qwen3.8 Max

Reasoning

Weight access

Ornith-1.5-397B

Open Weight

Qwen3.8 Max

Open Weight

License

Ornith-1.5-397B

Open Weight

Qwen3.8 Max

Open Weight

Release date

Ornith-1.5-397B

2026-08-18

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: Qwen3.8 Max has the higher public score estimate, 79.94 versus 68.5, but the 90% score intervals overlap.
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 evidence58 rows

Agentic

Terminal-Bench 2.1
Ornith-1.5-397B86.1%
Source
Qwen3.8 Max86.6%
Source
Qwen3.8 Max leads this result
HLE w/ tools
Ornith-1.5-397B56.1%
Source
Qwen3.8 Max56.2%
Source
Qwen3.8 Max leads this result
MCP Atlas
Ornith-1.5-397B80%
Source
Qwen3.8 Max—
Not directly comparable
Toolathlon-Verified
Ornith-1.5-397B71.2%
Source
Qwen3.8 Max72.5%
Source
Qwen3.8 Max leads this result
WideResearch
Ornith-1.5-397B80.8%
Source
Qwen3.8 Max81.9%
Source
Qwen3.8 Max leads this result
BrowseComp
Ornith-1.5-397B86.6%
Source
Qwen3.8 Max—
Not directly comparable
Claw-Eval
Ornith-1.5-397B81.4%
Source
Qwen3.8 Max—
Not directly comparable
CoWorkBench
Ornith-1.5-397B—
Qwen3.8 Max74.8%
Source
Not directly comparable
JobBench
Ornith-1.5-397B—
Qwen3.8 Max53.4%
Source
Not directly comparable
skillsBench
Ornith-1.5-397B—
Qwen3.8 Max70.2%
Source
Not directly comparable
Agents' Last Exam
Ornith-1.5-397B—
Qwen3.8 Max52.4%
Source
Not directly comparable
AutomationBench
Ornith-1.5-397B—
Qwen3.8 Max27.3%
Source
Not directly comparable
OSWorld-Verified
Ornith-1.5-397B—
Qwen3.8 Max86.1%
Source
Not directly comparable
OSWorld 2.0
Ornith-1.5-397B—
Qwen3.8 Max19.4%
Source
Not directly comparable
WebArena-Verified
Ornith-1.5-397B—
Qwen3.8 Max66.8%
Source
Not directly comparable
AndroidWorld
Ornith-1.5-397B—
Qwen3.8 Max85.3%
Source
Not directly comparable
MobileWorld
Ornith-1.5-397B—
Qwen3.8 Max77.8%
Source
Not directly comparable

Coding

Terminal-Bench 2.1
Ornith-1.5-397B86.1%
Source
Qwen3.8 Max86.6%
Source
Qwen3.8 Max leads this result
SWE-bench Verified
Ornith-1.5-397B86%
Source
Qwen3.8 Max—
Not directly comparable
SWE-bench Pro
Ornith-1.5-397B65.1%
Source
Qwen3.8 Max67.7%
Source
Qwen3.8 Max leads this result
SWE Multilingual
Ornith-1.5-397B79.6%
Source
Qwen3.8 Max—
Not directly comparable
deepSwe
Ornith-1.5-397B56%
Source
Qwen3.8 Max56.6%
Source
Qwen3.8 Max leads this result
frontierBench
Ornith-1.5-397B13.5%
Source
Qwen3.8 Max—
Not directly comparable
NL2Repo
Ornith-1.5-397B59.5%
Source
Qwen3.8 Max55.9%
Source
Ornith-1.5-397B leads this result
FrontierSWE
Ornith-1.5-397B—
Qwen3.8 Max73.5%
Source
Not directly comparable
MLS-Bench Lite
Ornith-1.5-397B—
Qwen3.8 Max41.0%
Source
Not directly comparable
PaperBench
Ornith-1.5-397B—
Qwen3.8 Max93.0%
Source
Not directly comparable

Reasoning

MRCRv2
Ornith-1.5-397B—
Qwen3.8 Max92.9%
Source
Not directly comparable
LongBench v2
Ornith-1.5-397B—
Qwen3.8 Max66.3%
Source
Not directly comparable

Knowledge

GPQA
Ornith-1.5-397B92.8%
Source
Qwen3.8 Max92.6%
Source
Ornith-1.5-397B leads this result
GPQA-D
Ornith-1.5-397B92.8%
Source
Qwen3.8 Max92.6%
Source
Ornith-1.5-397B leads this result
HLE
Ornith-1.5-397B44.6%
Source
Qwen3.8 Max43.6%
Source
Ornith-1.5-397B leads this result
HLE w/o tools
Ornith-1.5-397B44.6%
Source
Qwen3.8 Max43.6%
Source
Ornith-1.5-397B leads this result

Multimodal

MMMU-Pro
Ornith-1.5-397B—
Qwen3.8 Max82.3%
Source
Not directly comparable
MathVision
Ornith-1.5-397B—
Qwen3.8 Max95.2%
Source
Not directly comparable
MathVision w/ Python
Ornith-1.5-397B—
Qwen3.8 Max97.7%
Source
Not directly comparable
BabyVision
Ornith-1.5-397B—
Qwen3.8 Max82.0%
Source
Not directly comparable
BabyVision w/ Python
Ornith-1.5-397B—
Qwen3.8 Max91.3%
Source
Not directly comparable
ZeroBench
Ornith-1.5-397B—
Qwen3.8 Max24.0%
Source
Not directly comparable
ZeroBench w/ Python
Ornith-1.5-397B—
Qwen3.8 Max49.0%
Source
Not directly comparable
MedXpertQA (MM)
Ornith-1.5-397B—
Qwen3.8 Max80.4%
Source
Not directly comparable
ScreenSpot Pro
Ornith-1.5-397B—
Qwen3.8 Max84.5%
Source
Not directly comparable
Vision2Web
Ornith-1.5-397B—
Qwen3.8 Max69.0%
Source
Not directly comparable
CharXiv w/o tools
Ornith-1.5-397B—
Qwen3.8 Max88.4%
Source
Not directly comparable
CharXiv
Ornith-1.5-397B—
Qwen3.8 Max93.5%
Source
Not directly comparable
OmniDocBench 1.5
Ornith-1.5-397B—
Qwen3.8 Max92.1%
Source
Not directly comparable
OCRBench V2
Ornith-1.5-397B—
Qwen3.8 Max74.2%
Source
Not directly comparable
CC-OCR
Ornith-1.5-397B—
Qwen3.8 Max79.6%
Source
Not directly comparable
RealWorldQA
Ornith-1.5-397B—
Qwen3.8 Max88.0%
Source
Not directly comparable
ERQA
Ornith-1.5-397B—
Qwen3.8 Max77.8%
Source
Not directly comparable
SimpleVQA
Ornith-1.5-397B—
Qwen3.8 Max75.0%
Source
Not directly comparable
PerceptionBench
Ornith-1.5-397B—
Qwen3.8 Max63.5%
Source
Not directly comparable
Video-MME (with subtitle)
Ornith-1.5-397B—
Qwen3.8 Max90.4%
Source
Not directly comparable
VideoMMMU
Ornith-1.5-397B—
Qwen3.8 Max88.7%
Source
Not directly comparable
MMVU
Ornith-1.5-397B—
Qwen3.8 Max82.4%
Source
Not directly comparable
MLVU (M-Avg)
Ornith-1.5-397B—
Qwen3.8 Max90.8%
Source
Not directly comparable
LVBench
Ornith-1.5-397B—
Qwen3.8 Max81.8%
Source
Not directly comparable

Instruction following

IFBench
Ornith-1.5-397B—
Qwen3.8 Max82.8%
Source
Not directly comparable

Frequently asked questions

Which is better, Ornith-1.5-397B or Qwen3.8 Max?

Qwen3.8 Max has the higher public score estimate, 79.94 versus 68.5, 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 Qwen3.8 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-1.5-397B 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-1.5-397B 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-1.5-397B or Qwen3.8 Max?

Qwen3.8 Max has the larger documented context window: 1M, compared with 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 Qwen3.8 Max

Which one for your work

Long documents

Coding work

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

What is actually comparable

Category results, on a stated basis

Knowledge

Coding

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

Benchmark evidence

Agentic

Coding

Reasoning

Knowledge

Multimodal

Instruction following

Frequently asked questions

Which is better, Ornith-1.5-397B or Qwen3.8 Max?

Which is better for coding, Ornith-1.5-397B or Qwen3.8 Max?

Which is better for agentic tasks, Ornith-1.5-397B or Qwen3.8 Max?

Which costs less, Ornith-1.5-397B or Qwen3.8 Max?

Which has the larger context window, Ornith-1.5-397B or Qwen3.8 Max?

Related comparisons

Watch Ornith-1.5-397B vs Qwen3.8 Max