Ornith-1.5-9B vs Qwen3.8 Max

Updated October 2, 2026. Rank says Qwen3.8 Max is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

Qwen3.8 Max has the higher public point estimate, 72.12 versus 28.63. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. 11 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

Ornith-1.5-9B

Ornith AI

28.63/100

Estimated · Public rank #180

Conditional range 14.3–43.0

Model B

Qwen3.8 Max

Alibaba

72.12/100

Supported · Public rank #16

90% interval 69.1–75.1

Shared results: 11
Ornith-1.5-9B only: 5
Qwen3.8 Max only: 49
Like-for-like categories: 0 / 8

Estimated: Ornith-1.5-9B · Supported: Qwen3.8 Max. Conditional ranges do not establish rank confidence.How the comparison works

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
Ornith-1.5-9B is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Ornith-1.5-9B is scored on Estimated evidence for agentic, so the reading is 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

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

Pick the job you would use them for

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

—Ornith-1.5-9B55.4Qwen3.8 Max

Not comparable · BenchAlign v5.8

The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboard Shortlist models for this job

Same basis rules as the category table below

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.

HLEKnowledge
Normalized gap 23.4
Ornith-1.5-9B:20.2%
Qwen3.8 Max:43.6%
Ornith-1.5-9B source Qwen3.8 Max source
HLE w/o toolsKnowledge
Normalized gap 23.4
Ornith-1.5-9B:20.2%
Qwen3.8 Max:43.6%
Ornith-1.5-9B source Qwen3.8 Max source
SWE-bench ProCoding
Normalized gap 20.2
Ornith-1.5-9B:47.5%
Qwen3.8 Max:67.7%
Ornith-1.5-9B source Qwen3.8 Max source
GPQAKnowledge
Normalized gap 6.2
Ornith-1.5-9B:86.4%
Qwen3.8 Max:92.6%
Ornith-1.5-9B source Qwen3.8 Max source

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.

Agentic

Directional only

Ornith-1.5-9B: 18.0; Estimated · #101/119
Qwen3.8 Max: 65.2; Supported · #14/119
Basis: BenchAlign v5.8 lane · 7 vs 15 public rows
Reading: Directional only

Knowledge

Directional only

Ornith-1.5-9B: 30.6; Estimated · #134/171
Qwen3.8 Max: 66.1; Supported · #23/171
Basis: BenchAlign v5.8 lane · 4 vs 6 public rows
Reading: Directional only

Coding

Not comparable

Ornith-1.5-9B: Not ranked
Qwen3.8 Max: 55.4; Supported · #30/144
Basis: BenchAlign v5.8 lane · 5 vs 12 public rows
Reading: Not comparable

Reasoning

Not comparable

Ornith-1.5-9B: Not ranked
Qwen3.8 Max: 87.7; Unranked · 2 rankable rows
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Ornith-1.5-9B: Not ranked
Qwen3.8 Max: 88.4; #5/49
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Ornith-1.5-9B: Not ranked
Qwen3.8 Max: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Ornith-1.5-9B: Not ranked
Qwen3.8 Max: 89.8; #16/125
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Math

Not comparable

Ornith-1.5-9B: Not ranked
Qwen3.8 Max: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Ornith-1.5-9B and Qwen3.8 Max
Category	Ornith-1.5-9B	Qwen3.8 Max	Basis	Reading
Agentic	18.0Estimated · #101/119	65.2Supported · #14/119	Directional onlyBenchAlign v5.8 lane · 7 vs 15 public rows	Directional only
Knowledge	30.6Estimated · #134/171	66.1Supported · #23/171	Directional onlyBenchAlign v5.8 lane · 4 vs 6 public rows	Directional only
Coding	Not ranked	55.4Supported · #30/144	Not comparableBenchAlign v5.8 lane · 5 vs 12 public rows	Not comparable
Reasoning	Not ranked	87.7Unranked · 2 rankable rows	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable
Multimodal	Not ranked	88.4#5/49	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	Not ranked	89.8#16/125	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Math	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

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-1.5-9B: Self-hosted; infrastructure cost varies; Fits in one request
Qwen3.8 Max: API rate not published; Fits in one request

Ornith-1.5-9B 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-1.5-9B: Self-hosted; infrastructure cost varies; Fits in one request
Qwen3.8 Max: API rate not published; Fits in one request

Ornith-1.5-9B 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-1.5-9B: 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-1.5-9B has no comparable published API token rate. Qwen3.8 Max has no comparable published API token rate.

Cached input falls back to the list input rate only where a cached rate is unpublished

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

SpecificationOrnith-1.5-9BQwen3.8 Max

Context window

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

Ornith-1.5-9B

262K

Ornith-1.5-9B model card

Qwen3.8 Max

Alibaba Cloud Model Studio pricing

API model ID

Ornith-1.5-9B

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-1.5-9B

No comparable hosted API rate

Ornith-1.5-9B model card

Qwen3.8 Max

No comparable hosted API rate

Alibaba Cloud Model Studio pricing

Documented inputs

Ornith-1.5-9B

Not sourced

Qwen3.8 Max

Not sourced

Documented outputs

Ornith-1.5-9B

Not sourced

Qwen3.8 Max

Not sourced

Provider availability

Ornith-1.5-9B

Not sourced

Qwen3.8 Max

Not sourced

Reasoning profile

Ornith-1.5-9B

Reasoning

Qwen3.8 Max

Reasoning

Weight access

Ornith-1.5-9B

Open Weight

Qwen3.8 Max

Open Weight

License

Ornith-1.5-9B

Open Weight

Qwen3.8 Max

Open Weight

Release date

Ornith-1.5-9B

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 point estimate, 72.12 versus 28.63. Their conditional score ranges do not overlap. These ranges do not establish rank confidence.
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.

Questions

Which is better, Ornith-1.5-9B or Qwen3.8 Max?

Qwen3.8 Max has the higher public point estimate, 72.12 versus 28.63. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Ornith-1.5-9B or Qwen3.8 Max?

Ornith-1.5-9B is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Ornith-1.5-9B or Qwen3.8 Max?

Qwen3.8 Max scores higher for agentic tasks on the public lane, 65.2 to 18. Ornith-1.5-9B is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, Ornith-1.5-9B 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-1.5-9B or Qwen3.8 Max?

Qwen3.8 Max has the larger documented context window: 1M, compared with 262K.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence65 rows

Agentic

Terminal-Bench 2.1
Ornith-1.5-9B46.2%
Source
Qwen3.8 Max86.6%
Source
Qwen3.8 Max leads this result
HLE w/ tools
Ornith-1.5-9B30.5%
Source
Qwen3.8 Max56.2%
Source
Qwen3.8 Max leads this result
MCP Atlas
Ornith-1.5-9B54.2%
Source
Qwen3.8 Max—
Not directly comparable
Toolathlon-Verified
Ornith-1.5-9B41.2%
Source
Qwen3.8 Max72.5%
Source
Qwen3.8 Max leads this result
WideResearch
Ornith-1.5-9B59.5%
Source
Qwen3.8 Max81.9%
Source
Qwen3.8 Max leads this result
BrowseComp
Ornith-1.5-9B56.4%
Source
Qwen3.8 Max—
Not directly comparable
Claw-Eval
Ornith-1.5-9B66.5%
Source
Qwen3.8 Max—
Not directly comparable
CoWorkBench
Ornith-1.5-9B—
Qwen3.8 Max74.8%
Source
Not directly comparable
JobBench
Ornith-1.5-9B—
Qwen3.8 Max53.4%
Source
Not directly comparable
skillsBench
Ornith-1.5-9B—
Qwen3.8 Max70.2%
Source
Not directly comparable
Agents' Last Exam
Ornith-1.5-9B—
Qwen3.8 Max52.4%
Source
Not directly comparable
AutomationBench
Ornith-1.5-9B—
Qwen3.8 Max27.3%
Source
Not directly comparable
OSWorld-Verified
Ornith-1.5-9B—
Qwen3.8 Max86.1%
Source
Not directly comparable
OSWorld 2.0
Ornith-1.5-9B—
Qwen3.8 Max19.4%
Source
Not directly comparable
WebArena-Verified
Ornith-1.5-9B—
Qwen3.8 Max66.8%
Source
Not directly comparable
AndroidWorld
Ornith-1.5-9B—
Qwen3.8 Max85.3%
Source
Not directly comparable
MobileWorld
Ornith-1.5-9B—
Qwen3.8 Max77.8%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
Ornith-1.5-9B—
Qwen3.8 Max67.4%
Source
Not directly comparable

Coding

Terminal-Bench 2.1
Ornith-1.5-9B46.2%
Source
Qwen3.8 Max86.6%
Source
Qwen3.8 Max leads this result
SWE-bench Verified
Ornith-1.5-9B70.6%
Source
Qwen3.8 Max—
Not directly comparable
SWE-bench Pro
Ornith-1.5-9B47.5%
Source
Qwen3.8 Max67.7%
Source
Qwen3.8 Max leads this result
SWE Multilingual
Ornith-1.5-9B54.4%
Source
Qwen3.8 Max—
Not directly comparable
NL2Repo
Ornith-1.5-9B32.4%
Source
Qwen3.8 Max55.9%
Source
Qwen3.8 Max leads this result
DeepSWE
Ornith-1.5-9B—
Qwen3.8 Max56.6%
Source
Not directly comparable
FrontierSWE
Ornith-1.5-9B—
Qwen3.8 Max73.5%
Source
Not directly comparable
MLS-Bench Lite
Ornith-1.5-9B—
Qwen3.8 Max41.0%
Source
Not directly comparable
PaperBench
Ornith-1.5-9B—
Qwen3.8 Max93.0%
Source
Not directly comparable
VulcanBench v3
Ornith-1.5-9B—
Qwen3.8 Max81.2%
Source
Not directly comparable
OpenHarmony Bench
Ornith-1.5-9B—
Qwen3.8 Max60.8%
Source
Not directly comparable
FrontierSWE v2
Ornith-1.5-9B—
Qwen3.8 Max15.8%
Source
Not directly comparable
LiveCodeBench (Vals)
Ornith-1.5-9B—
Qwen3.8 Max87.9%
Source
Not directly comparable
SWE-bench (Vals)
Ornith-1.5-9B—
Qwen3.8 Max85.6%
Source
Not directly comparable

Reasoning

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

Multimodal

MMMU-Pro
Ornith-1.5-9B—
Qwen3.8 Max82.3%
Source
Not directly comparable
MathVision
Ornith-1.5-9B—
Qwen3.8 Max95.2%
Source
Not directly comparable
MathVision w/ Python
Ornith-1.5-9B—
Qwen3.8 Max97.7%
Source
Not directly comparable
BabyVision
Ornith-1.5-9B—
Qwen3.8 Max82.0%
Source
Not directly comparable
BabyVision w/ Python
Ornith-1.5-9B—
Qwen3.8 Max91.3%
Source
Not directly comparable
ZeroBench
Ornith-1.5-9B—
Qwen3.8 Max24.0%
Source
Not directly comparable
ZeroBench w/ Python
Ornith-1.5-9B—
Qwen3.8 Max49.0%
Source
Not directly comparable
MedXpertQA (MM)
Ornith-1.5-9B—
Qwen3.8 Max80.4%
Source
Not directly comparable
ScreenSpot Pro
Ornith-1.5-9B—
Qwen3.8 Max84.5%
Source
Not directly comparable
Vision2Web
Ornith-1.5-9B—
Qwen3.8 Max69.0%
Source
Not directly comparable
CharXiv w/o tools
Ornith-1.5-9B—
Qwen3.8 Max88.4%
Source
Not directly comparable
CharXiv
Ornith-1.5-9B—
Qwen3.8 Max93.5%
Source
Not directly comparable
OmniDocBench 1.5
Ornith-1.5-9B—
Qwen3.8 Max92.1%
Source
Not directly comparable
OCRBench V2
Ornith-1.5-9B—
Qwen3.8 Max74.2%
Source
Not directly comparable
CC-OCR
Ornith-1.5-9B—
Qwen3.8 Max79.6%
Source
Not directly comparable
RealWorldQA
Ornith-1.5-9B—
Qwen3.8 Max88.0%
Source
Not directly comparable
ERQA
Ornith-1.5-9B—
Qwen3.8 Max77.8%
Source
Not directly comparable
SimpleVQA
Ornith-1.5-9B—
Qwen3.8 Max75.0%
Source
Not directly comparable
PerceptionBench
Ornith-1.5-9B—
Qwen3.8 Max63.5%
Source
Not directly comparable
Video-MME (with subtitle)
Ornith-1.5-9B—
Qwen3.8 Max90.4%
Source
Not directly comparable
VideoMMMU
Ornith-1.5-9B—
Qwen3.8 Max88.7%
Source
Not directly comparable
MMVU
Ornith-1.5-9B—
Qwen3.8 Max82.4%
Source
Not directly comparable
MLVU (M-Avg)
Ornith-1.5-9B—
Qwen3.8 Max90.8%
Source
Not directly comparable
LVBench
Ornith-1.5-9B—
Qwen3.8 Max81.8%
Source
Not directly comparable

Knowledge

GPQA
Ornith-1.5-9B86.4%
Source
Qwen3.8 Max92.6%
Source
Qwen3.8 Max leads this result
GPQA-D
Ornith-1.5-9B86.4%
Source
Qwen3.8 Max92.6%
Source
Qwen3.8 Max leads this result
HLE
Ornith-1.5-9B20.2%
Source
Qwen3.8 Max43.6%
Source
Qwen3.8 Max leads this result
HLE w/o tools
Ornith-1.5-9B20.2%
Source
Qwen3.8 Max43.6%
Source
Qwen3.8 Max leads this result
GPQA Diamond (Vals)
Ornith-1.5-9B—
Qwen3.8 Max93.7%
Source
Not directly comparable
MMLU-Pro (Vals)
Ornith-1.5-9B—
Qwen3.8 Max88.6%
Source
Not directly comparable

Instruction following

IFBench
Ornith-1.5-9B—
Qwen3.8 Max82.8%
Source
Not directly comparable

65 public results · 11 shared

Related comparisons

Compare API pricing Read the methodology Open the model selector

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Last updated October 2, 2026

Ornith-1.5-9B vs Qwen3.8 Max

Decision reading

Which one for your work

Long documents

Coding work

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Agentic

Knowledge

Coding

Reasoning

Multimodal

Multilingual

Instruction following

Math

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

If you already use one of these models

Questions

Which is better, Ornith-1.5-9B or Qwen3.8 Max?

Which is better for coding, Ornith-1.5-9B or Qwen3.8 Max?

Which is better for agentic tasks, Ornith-1.5-9B or Qwen3.8 Max?

Which costs less, Ornith-1.5-9B or Qwen3.8 Max?

Which has the larger context window, Ornith-1.5-9B or Qwen3.8 Max?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Instruction following

Related comparisons

Watch Ornith-1.5-9B vs Qwen3.8 Max