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BenchLM
Model A
Ornith-1.5-397B

Ornith AI

68.5/100

Estimated · Public rank #21

90% interval 58.6–78.4

Ornith-1.5-397B vs ZAYA1-8B

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

Model B
ZAYA1-8B

Zyphra

30.9/100

Estimated · Public rank #210

90% interval 21.0–40.7

Decision reading

Ornith-1.5-397B has the higher public score, 68.5 versus 30.86, and the 90% score intervals do not overlap.

2 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

    Ornith-1.5-397B

    Ornith-1.5-397B 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

    No shared weighted benchmark basis supports a winner.

    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

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. ZAYA1-8B does not fit this workload in one request. Ornith-1.5-397B has no comparable published API token rate. ZAYA1-8B has no comparable published API token rate.

    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
2
Ornith-1.5-397B only
16
ZAYA1-8B only
9
Like-for-like categories
0 / 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

Directional only
Ornith-1.5-397B
51.1
ZAYA1-8B
73.6
Weighted basis
2 vs 2 rows
Reading
Directional only

Agentic

Not comparable
Ornith-1.5-397B
86.6
ZAYA1-8B
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Coding

Not comparable
Ornith-1.5-397B
78.0
ZAYA1-8B
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
Ornith-1.5-397B
Not measured
ZAYA1-8B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
Ornith-1.5-397B
Not measured
ZAYA1-8B
80.4
Weighted basis
0 vs 2 rows
Reading
Not comparable

Multilingual

Not comparable
Ornith-1.5-397B
Not measured
ZAYA1-8B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Ornith-1.5-397B
Not measured
ZAYA1-8B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Ornith-1.5-397B
Not measured
ZAYA1-8B
64.1
Weighted basis
0 vs 2 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-1.5-397B
Self-hosted; infrastructure cost varies
Fits in one request
ZAYA1-8B
Self-hosted; infrastructure cost varies
Fits in one request

Ornith-1.5-397B has no comparable published API token rate. ZAYA1-8B 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
ZAYA1-8B
Self-hosted; infrastructure cost varies
Fits in one request

Ornith-1.5-397B has no comparable published API token rate. ZAYA1-8B 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
ZAYA1-8B
Self-hosted; infrastructure cost varies
Does not fit in one request
Cached-input rate unavailable

ZAYA1-8B does not fit this workload in one request. Ornith-1.5-397B has no comparable published API token rate. ZAYA1-8B 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-1.5-397B

262K

Ornith-1.5-397B model card

ZAYA1-8B

131K

API model ID

Ornith-1.5-397B

Not sourced

ZAYA1-8B

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

ZAYA1-8B

No comparable hosted API rate

Documented inputs

Ornith-1.5-397B

Not sourced

ZAYA1-8B

Not sourced

Documented outputs

Ornith-1.5-397B

Not sourced

ZAYA1-8B

Not sourced

Provider availability

Ornith-1.5-397B

Not sourced

ZAYA1-8B

Not sourced

Reasoning profile

Ornith-1.5-397B

Reasoning

ZAYA1-8B

Reasoning

Weight access

Ornith-1.5-397B

Open Weight

ZAYA1-8B

Open Weight

License

Ornith-1.5-397B

Open Weight

ZAYA1-8B

Open Weight

Release date

Ornith-1.5-397B

2026-08-18

ZAYA1-8B

2026-05-05

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, 68.5 versus 30.86, and the 90% score intervals do not overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Ornith-1.5-397B has the larger documented window (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 evidence27 rows

Agentic

  • Terminal-Bench 2.1

    Ornith-1.5-397B86.1%
    Source
    ZAYA1-8B—

    Not directly comparable

  • HLE w/ tools

    Ornith-1.5-397B56.1%
    Source
    ZAYA1-8B—

    Not directly comparable

  • MCP Atlas

    Ornith-1.5-397B80%
    Source
    ZAYA1-8B—

    Not directly comparable

  • Toolathlon-Verified

    Ornith-1.5-397B71.2%
    Source
    ZAYA1-8B—

    Not directly comparable

  • WideResearch

    Ornith-1.5-397B80.8%
    Source
    ZAYA1-8B—

    Not directly comparable

  • BrowseComp

    Ornith-1.5-397B86.6%
    Source
    ZAYA1-8B—

    Not directly comparable

  • Claw-Eval

    Ornith-1.5-397B81.4%
    Source
    ZAYA1-8B—

    Not directly comparable

  • BFCL v4

    Ornith-1.5-397B—
    ZAYA1-8B39.2%
    Source

    Not directly comparable

Coding

  • Terminal-Bench 2.1

    Ornith-1.5-397B86.1%
    Source
    ZAYA1-8B—

    Not directly comparable

  • SWE-bench Verified

    Ornith-1.5-397B86%
    Source
    ZAYA1-8B—

    Not directly comparable

  • SWE-bench Pro

    Ornith-1.5-397B65.1%
    Source
    ZAYA1-8B—

    Not directly comparable

  • SWE Multilingual

    Ornith-1.5-397B79.6%
    Source
    ZAYA1-8B—

    Not directly comparable

  • deepSwe

    Ornith-1.5-397B56%
    Source
    ZAYA1-8B—

    Not directly comparable

  • frontierBench

    Ornith-1.5-397B13.5%
    Source
    ZAYA1-8B—

    Not directly comparable

  • NL2Repo

    Ornith-1.5-397B59.5%
    Source
    ZAYA1-8B—

    Not directly comparable

  • LiveCodeBench v6

    Ornith-1.5-397B—
    ZAYA1-8B65.8%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Ornith-1.5-397B92.8%
    Source
    ZAYA1-8B71%
    Source

    Ornith-1.5-397B leads this result

  • GPQA-D

    Ornith-1.5-397B92.8%
    Source
    ZAYA1-8B71.0%
    Source

    Ornith-1.5-397B leads this result

  • HLE

    Ornith-1.5-397B44.6%
    Source
    ZAYA1-8B—

    Not directly comparable

  • HLE w/o tools

    Ornith-1.5-397B44.6%
    Source
    ZAYA1-8B—

    Not directly comparable

  • MMLU-Pro

    Ornith-1.5-397B—
    ZAYA1-8B74.2%
    Source

    Not directly comparable

Math

  • AIME26

    Ornith-1.5-397B—
    ZAYA1-8B89.1%
    Source

    Not directly comparable

  • HMMT Feb 2026

    Ornith-1.5-397B—
    ZAYA1-8B71.6%
    Source

    Not directly comparable

  • IMOAnswerBench

    Ornith-1.5-397B—
    ZAYA1-8B59.3%
    Source

    Not directly comparable

  • Apex

    Ornith-1.5-397B—
    ZAYA1-8B32.2%
    Source

    Not directly comparable

Instruction following

  • IFEval

    Ornith-1.5-397B—
    ZAYA1-8B85.6%
    Source

    Not directly comparable

  • IFBench

    Ornith-1.5-397B—
    ZAYA1-8B52.6%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Ornith-1.5-397B or ZAYA1-8B?

Ornith-1.5-397B has the higher public score, 68.5 versus 30.86, 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-1.5-397B or ZAYA1-8B?

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, Ornith-1.5-397B or ZAYA1-8B?

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 ZAYA1-8B?

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 ZAYA1-8B?

Ornith-1.5-397B has the larger documented context window: 262K, compared with 131K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 19, 2026

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