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BenchLM
Model A
Agents-A1

InternScience

60.9/100

Estimated · Public rank #55

90% interval 51.0–70.8

Agents-A1 vs Ornith-1.5-9B

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

Model B
Ornith-1.5-9B

Ornith AI

37.0/100

Estimated · Public rank #205

90% interval 27.1–46.8

Decision reading

Agents-A1 has the higher public score, 60.91 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.

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Agents-A1

    Agents-A1 leads on the same 1 weighted benchmark row.

    Confidence: limited

Show secondary and unsupported calls

  • Coding work

    Code generation, repair, and software-engineering tasks

    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.

Shared results
3
Agents-A1 only
3
Ornith-1.5-9B only
13
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
Agents-A1
75.5
Ornith-1.5-9B
56.4
Weighted basis
1 vs 1 rows
Reading
Agents-A1 leads

Knowledge

Directional only
Agents-A1
47.6
Ornith-1.5-9B
29.1
Weighted basis
1 vs 2 rows
Reading
Directional only

Coding

Not comparable
Agents-A1
Not measured
Ornith-1.5-9B
61.7
Weighted basis
0 vs 2 rows
Reading
Not comparable

Reasoning

Not comparable
Agents-A1
60.2
Ornith-1.5-9B
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Math

Not comparable
Agents-A1
Not measured
Ornith-1.5-9B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Agents-A1
Not measured
Ornith-1.5-9B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Agents-A1
Not measured
Ornith-1.5-9B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Agents-A1
94.8
Ornith-1.5-9B
Not measured
Weighted basis
1 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

Agents-A1
API rate not published
Fits in one request
Ornith-1.5-9B
Self-hosted; infrastructure cost varies
Fits in one request

Agents-A1 has no comparable published API token rate. Ornith-1.5-9B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Agents-A1
API rate not published
Fits in one request
Ornith-1.5-9B
Self-hosted; infrastructure cost varies
Fits in one request

Agents-A1 has no comparable published API token rate. Ornith-1.5-9B has no comparable published API token rate.

Cache-heavy agent loop

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

Agents-A1
API rate not published
Fits in one request
Cached-input rate unavailable
Ornith-1.5-9B
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable

Agents-A1 has no comparable published API token rate. Ornith-1.5-9B 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.

Agents-A1

262K

Ornith-1.5-9B

262K

Ornith-1.5-9B model card

API model ID

Agents-A1

Not sourced

Ornith-1.5-9B

Not sourced

Cached-input rate

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

Agents-A1

No comparable hosted API rate

Ornith-1.5-9B

No comparable hosted API rate

Ornith-1.5-9B model card

Documented inputs

Agents-A1

Not sourced

Ornith-1.5-9B

Not sourced

Documented outputs

Agents-A1

Not sourced

Ornith-1.5-9B

Not sourced

Provider availability

Agents-A1

Not sourced

Ornith-1.5-9B

Not sourced

Reasoning profile

Agents-A1

Reasoning

Ornith-1.5-9B

Reasoning

Weight access

Agents-A1

Open Weight

Ornith-1.5-9B

Open Weight

License

Agents-A1

Open Weight

Ornith-1.5-9B

Open Weight

Release date

Agents-A1

2026-06-26

Ornith-1.5-9B

2026-08-18

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
Agents-A1 has the higher public score, 60.91 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 evidence19 rows

Agentic

  • BrowseComp

    Agents-A175.5%
    Source
    Ornith-1.5-9B56.4%
    Source

    Agents-A1 leads this result

  • HLE w/ tools

    Agents-A147.6%
    Source
    Ornith-1.5-9B30.5%
    Source

    Agents-A1 leads this result

  • VITA-Bench

    Agents-A138.8%
    Source
    Ornith-1.5-9B—

    Not directly comparable

  • Terminal-Bench 2.1

    Agents-A1—
    Ornith-1.5-9B46.2%
    Source

    Not directly comparable

  • MCP Atlas

    Agents-A1—
    Ornith-1.5-9B54.2%
    Source

    Not directly comparable

  • Toolathlon-Verified

    Agents-A1—
    Ornith-1.5-9B41.2%
    Source

    Not directly comparable

  • WideResearch

    Agents-A1—
    Ornith-1.5-9B59.5%
    Source

    Not directly comparable

  • Claw-Eval

    Agents-A1—
    Ornith-1.5-9B66.5%
    Source

    Not directly comparable

Coding

  • Terminal-Bench 2.1

    Agents-A1—
    Ornith-1.5-9B46.2%
    Source

    Not directly comparable

  • SWE-bench Verified

    Agents-A1—
    Ornith-1.5-9B70.6%
    Source

    Not directly comparable

  • SWE-bench Pro

    Agents-A1—
    Ornith-1.5-9B47.5%
    Source

    Not directly comparable

  • SWE Multilingual

    Agents-A1—
    Ornith-1.5-9B54.4%
    Source

    Not directly comparable

  • NL2Repo

    Agents-A1—
    Ornith-1.5-9B32.4%
    Source

    Not directly comparable

Reasoning

  • LongBench v2

    Agents-A160.2%
    Source
    Ornith-1.5-9B—

    Not directly comparable

Knowledge

  • HLE

    Agents-A147.6%
    Source
    Ornith-1.5-9B20.2%
    Source

    Agents-A1 leads this result

  • GPQA

    Agents-A1—
    Ornith-1.5-9B86.4%
    Source

    Not directly comparable

  • GPQA-D

    Agents-A1—
    Ornith-1.5-9B86.4%
    Source

    Not directly comparable

  • HLE w/o tools

    Agents-A1—
    Ornith-1.5-9B20.2%
    Source

    Not directly comparable

Instruction following

  • IFEval

    Agents-A194.8%
    Source
    Ornith-1.5-9B—

    Not directly comparable

Frequently asked questions

Which is better, Agents-A1 or Ornith-1.5-9B?

Agents-A1 has the higher public score, 60.91 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, Agents-A1 or Ornith-1.5-9B?

The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.

Which is better for agentic tasks, Agents-A1 or Ornith-1.5-9B?

Agents-A1 leads the like-for-like agentic tasks comparison across 1 shared weighted benchmark row.

Which costs less, Agents-A1 or Ornith-1.5-9B?

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, Agents-A1 or Ornith-1.5-9B?

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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