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BenchLM
Model A
Ornith-1.5-9B

Ornith AI

37.0/100

Estimated · Public rank #205

90% interval 27.1–46.8

Ornith-1.5-9B vs Qwen3.5-122B-A10B

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

Model B
Qwen3.5-122B-A10B

Alibaba

59.7/100

Supported · Public rank #68

90% interval 48.5–70.8

Decision reading

Qwen3.5-122B-A10B has the higher public score, 59.67 versus 36.95, and the 90% score intervals do not overlap.

3 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

    The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.

    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.

Shared results
3
Ornith-1.5-9B only
13
Qwen3.5-122B-A10B only
12
Like-for-like categories
0 / 8

3 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.

Agentic

Directional only
Ornith-1.5-9B
56.4
Qwen3.5-122B-A10B
56.4
Weighted basis
1 vs 3 rows
Reading
Directional only

Coding

Directional only
Ornith-1.5-9B
61.7
Qwen3.5-122B-A10B
72.0
Weighted basis
2 vs 1 rows
Reading
Directional only

Knowledge

Directional only
Ornith-1.5-9B
29.1
Qwen3.5-122B-A10B
83.6
Weighted basis
2 vs 3 rows
Reading
Directional only

Reasoning

Not comparable
Ornith-1.5-9B
Not measured
Qwen3.5-122B-A10B
60.2
Weighted basis
0 vs 1 rows
Reading
Not comparable

Math

Not comparable
Ornith-1.5-9B
Not measured
Qwen3.5-122B-A10B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Ornith-1.5-9B
Not measured
Qwen3.5-122B-A10B
82.2
Weighted basis
0 vs 1 rows
Reading
Not comparable

Multimodal

Not comparable
Ornith-1.5-9B
Not measured
Qwen3.5-122B-A10B
77.2
Weighted basis
0 vs 1 rows
Reading
Not comparable

Instruction following

Not comparable
Ornith-1.5-9B
Not measured
Qwen3.5-122B-A10B
93.4
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-1.5-9B
Self-hosted; infrastructure cost varies
Fits in one request
Qwen3.5-122B-A10B
Self-hosted; infrastructure cost varies
Fits in one request

Ornith-1.5-9B has no comparable published API token rate. Qwen3.5-122B-A10B 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
Qwen3.5-122B-A10B
Self-hosted; infrastructure cost varies
Fits in one request

Ornith-1.5-9B has no comparable published API token rate. Qwen3.5-122B-A10B 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
Qwen3.5-122B-A10B
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable

Ornith-1.5-9B has no comparable published API token rate. Qwen3.5-122B-A10B 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-1.5-9B

262K

Ornith-1.5-9B model card

Qwen3.5-122B-A10B

262K

API model ID

Ornith-1.5-9B

Not sourced

Qwen3.5-122B-A10B

Not sourced

Cached-input rate

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

Ornith-1.5-9B

No comparable hosted API rate

Ornith-1.5-9B model card

Qwen3.5-122B-A10B

No comparable hosted API rate

Documented inputs

Ornith-1.5-9B

Not sourced

Qwen3.5-122B-A10B

Not sourced

Documented outputs

Ornith-1.5-9B

Not sourced

Qwen3.5-122B-A10B

Not sourced

Provider availability

Ornith-1.5-9B

Not sourced

Qwen3.5-122B-A10B

Not sourced

Reasoning profile

Ornith-1.5-9B

Reasoning

Qwen3.5-122B-A10B

Reasoning

Weight access

Ornith-1.5-9B

Open Weight

Qwen3.5-122B-A10B

Open Weight

License

Ornith-1.5-9B

Open Weight

Qwen3.5-122B-A10B

Open Weight

Release date

Ornith-1.5-9B

2026-08-18

Qwen3.5-122B-A10B

2026-03-04

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
Qwen3.5-122B-A10B has the higher public score, 59.67 versus 36.95, and the 90% score intervals do not 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.

Benchmark evidence

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

Browse raw public benchmark evidence28 rows

Agentic

  • Terminal-Bench 2.1

    Ornith-1.5-9B46.2%
    Source
    Qwen3.5-122B-A10B—

    Not directly comparable

  • HLE w/ tools

    Ornith-1.5-9B30.5%
    Source
    Qwen3.5-122B-A10B—

    Not directly comparable

  • MCP Atlas

    Ornith-1.5-9B54.2%
    Source
    Qwen3.5-122B-A10B—

    Not directly comparable

  • Toolathlon-Verified

    Ornith-1.5-9B41.2%
    Source
    Qwen3.5-122B-A10B—

    Not directly comparable

  • WideResearch

    Ornith-1.5-9B59.5%
    Source
    Qwen3.5-122B-A10B—

    Not directly comparable

  • BrowseComp

    Ornith-1.5-9B56.4%
    Source
    Qwen3.5-122B-A10B63.8%
    Source

    Qwen3.5-122B-A10B leads this result

  • Claw-Eval

    Ornith-1.5-9B66.5%
    Source
    Qwen3.5-122B-A10B—

    Not directly comparable

  • Terminal-Bench 2.0

    Ornith-1.5-9B—
    Qwen3.5-122B-A10B49.4%
    Source

    Not directly comparable

  • OSWorld-Verified

    Ornith-1.5-9B—
    Qwen3.5-122B-A10B58%
    Source

    Not directly comparable

Coding

  • Terminal-Bench 2.1

    Ornith-1.5-9B46.2%
    Source
    Qwen3.5-122B-A10B—

    Not directly comparable

  • SWE-bench Verified

    Ornith-1.5-9B70.6%
    Source
    Qwen3.5-122B-A10B72%
    Source

    Qwen3.5-122B-A10B leads this result

  • SWE-bench Pro

    Ornith-1.5-9B47.5%
    Source
    Qwen3.5-122B-A10B—

    Not directly comparable

  • SWE Multilingual

    Ornith-1.5-9B54.4%
    Source
    Qwen3.5-122B-A10B—

    Not directly comparable

  • NL2Repo

    Ornith-1.5-9B32.4%
    Source
    Qwen3.5-122B-A10B—

    Not directly comparable

Reasoning

  • LongBench v2

    Ornith-1.5-9B—
    Qwen3.5-122B-A10B60.2%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Ornith-1.5-9B86.4%
    Source
    Qwen3.5-122B-A10B86.6%
    Source

    Qwen3.5-122B-A10B leads this result

  • GPQA-D

    Ornith-1.5-9B86.4%
    Source
    Qwen3.5-122B-A10B—

    Not directly comparable

  • HLE

    Ornith-1.5-9B20.2%
    Source
    Qwen3.5-122B-A10B—

    Not directly comparable

  • HLE w/o tools

    Ornith-1.5-9B20.2%
    Source
    Qwen3.5-122B-A10B—

    Not directly comparable

  • MMLU-Pro

    Ornith-1.5-9B—
    Qwen3.5-122B-A10B86.7%
    Source

    Not directly comparable

  • SuperGPQA

    Ornith-1.5-9B—
    Qwen3.5-122B-A10B67.1%
    Source

    Not directly comparable

Multilingual

  • MMLU-ProX

    Ornith-1.5-9B—
    Qwen3.5-122B-A10B82.2%
    Source

    Not directly comparable

Multimodal

  • MMMU

    Ornith-1.5-9B—
    Qwen3.5-122B-A10B83.9%
    Source

    Not directly comparable

  • MMVU

    Ornith-1.5-9B—
    Qwen3.5-122B-A10B74.7%
    Source

    Not directly comparable

  • MathVision

    Ornith-1.5-9B—
    Qwen3.5-122B-A10B86.2%
    Source

    Not directly comparable

  • CharXiv

    Ornith-1.5-9B—
    Qwen3.5-122B-A10B77.2%
    Source

    Not directly comparable

  • V*

    Ornith-1.5-9B—
    Qwen3.5-122B-A10B93.2%
    Source

    Not directly comparable

Instruction following

  • IFEval

    Ornith-1.5-9B—
    Qwen3.5-122B-A10B93.4%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Ornith-1.5-9B or Qwen3.5-122B-A10B?

Qwen3.5-122B-A10B has the higher public score, 59.67 versus 36.95, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.

Which is better for coding, Ornith-1.5-9B or Qwen3.5-122B-A10B?

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-1.5-9B or Qwen3.5-122B-A10B?

The current agentic tasks 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 costs less, Ornith-1.5-9B or Qwen3.5-122B-A10B?

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 Qwen3.5-122B-A10B?

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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