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BenchLM
Model A
GPT-5.4 Pro

OpenAI

61.3/100

Estimated · Public rank #54

90% interval 44.4–78.1

GPT-5.4 Pro vs Ornith-1.5-35B-A3B

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

Model B
Ornith-1.5-35B-A3B

Ornith AI

49.3/100

Estimated · Public rank #136

90% interval 39.4–59.1

Decision reading

GPT-5.4 Pro has the higher public score estimate, 61.25 versus 49.27, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

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

    GPT-5.4 Pro

    GPT-5.4 Pro leads on the same 1 weighted benchmark row.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    GPT-5.4 Pro

    GPT-5.4 Pro 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

  • 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: rate-fallback

  • 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
GPT-5.4 Pro only
8
Ornith-1.5-35B-A3B only
15
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
GPT-5.4 Pro
89.3
Ornith-1.5-35B-A3B
67.6
Weighted basis
1 vs 1 rows
Reading
GPT-5.4 Pro leads

Knowledge

Directional only
GPT-5.4 Pro
58.7
Ornith-1.5-35B-A3B
34.2
Weighted basis
1 vs 2 rows
Reading
Directional only

Coding

Not comparable
GPT-5.4 Pro
Not measured
Ornith-1.5-35B-A3B
71.5
Weighted basis
0 vs 2 rows
Reading
Not comparable

Reasoning

Not comparable
GPT-5.4 Pro
83.3
Ornith-1.5-35B-A3B
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Math

Not comparable
GPT-5.4 Pro
46.9
Ornith-1.5-35B-A3B
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
GPT-5.4 Pro
Not measured
Ornith-1.5-35B-A3B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
GPT-5.4 Pro
94.0
Ornith-1.5-35B-A3B
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
GPT-5.4 Pro
Not measured
Ornith-1.5-35B-A3B
Not measured
Weighted basis
0 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

GPT-5.4 Pro
$0.12
Fits in one request
Ornith-1.5-35B-A3B
Self-hosted; infrastructure cost varies
Fits in one request

Ornith-1.5-35B-A3B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GPT-5.4 Pro
$2.04
Fits in one request
Ornith-1.5-35B-A3B
Self-hosted; infrastructure cost varies
Fits in one request

Ornith-1.5-35B-A3B has no comparable published API token rate.

Cache-heavy agent loop

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

GPT-5.4 Pro
$8.40
Fits in one request
Cached input priced at the published list-input rate
Ornith-1.5-35B-A3B
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable

GPT-5.4 Pro has no published cached-input rate, so cached tokens use its listed input rate. Ornith-1.5-35B-A3B 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.

Cached-input rate

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

GPT-5.4 Pro

Not published

OpenAI pricing

Ornith-1.5-35B-A3B

No comparable hosted API rate

Ornith-1.5-35B-A3B model card

Provider availability

GPT-5.4 Pro

Generally Available · OpenAI Responses API

OpenAI model catalog

Ornith-1.5-35B-A3B

Not sourced

Reasoning profile

GPT-5.4 Pro

Reasoning

Ornith-1.5-35B-A3B

Reasoning

Weight access

GPT-5.4 Pro

Proprietary

Ornith-1.5-35B-A3B

Open Weight

License

GPT-5.4 Pro

Proprietary

Ornith-1.5-35B-A3B

Open Weight

Release date

GPT-5.4 Pro

2026-03-05

Ornith-1.5-35B-A3B

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
GPT-5.4 Pro has the higher public score estimate, 61.25 versus 49.27, but the 90% score intervals overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
GPT-5.4 Pro has the larger documented window (1.05M).

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 evidence26 rows

Agentic

  • BrowseComp

    GPT-5.4 Pro89.3%
    Source
    Ornith-1.5-35B-A3B67.6%
    Source

    GPT-5.4 Pro leads this result

  • Terminal-Bench 2.1

    GPT-5.4 Pro—
    Ornith-1.5-35B-A3B67.8%
    Source

    Not directly comparable

  • HLE w/ tools

    GPT-5.4 Pro—
    Ornith-1.5-35B-A3B33.4%
    Source

    Not directly comparable

  • MCP Atlas

    GPT-5.4 Pro—
    Ornith-1.5-35B-A3B70.2%
    Source

    Not directly comparable

  • Toolathlon-Verified

    GPT-5.4 Pro—
    Ornith-1.5-35B-A3B48.7%
    Source

    Not directly comparable

  • WideResearch

    GPT-5.4 Pro—
    Ornith-1.5-35B-A3B67.8%
    Source

    Not directly comparable

  • Claw-Eval

    GPT-5.4 Pro—
    Ornith-1.5-35B-A3B72.5%
    Source

    Not directly comparable

Coding

  • Terminal-Bench 2.1

    GPT-5.4 Pro—
    Ornith-1.5-35B-A3B67.8%
    Source

    Not directly comparable

  • SWE-bench Verified

    GPT-5.4 Pro—
    Ornith-1.5-35B-A3B79%
    Source

    Not directly comparable

  • SWE-bench Pro

    GPT-5.4 Pro—
    Ornith-1.5-35B-A3B59.6%
    Source

    Not directly comparable

  • SWE Multilingual

    GPT-5.4 Pro—
    Ornith-1.5-35B-A3B71.4%
    Source

    Not directly comparable

  • deepSwe

    GPT-5.4 Pro—
    Ornith-1.5-35B-A3B22%
    Source

    Not directly comparable

  • frontierBench

    GPT-5.4 Pro—
    Ornith-1.5-35B-A3B5.1%
    Source

    Not directly comparable

  • NL2Repo

    GPT-5.4 Pro—
    Ornith-1.5-35B-A3B46.2%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    GPT-5.4 Pro83.3%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

Knowledge

  • HLE

    GPT-5.4 Pro58.7%
    Source
    Ornith-1.5-35B-A3B25.6%
    Source

    GPT-5.4 Pro leads this result

  • FrontierScience

    GPT-5.4 Pro36.7%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

  • FrontierScience Research

    GPT-5.4 Pro36.7%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

  • HLE w/o tools

    GPT-5.4 Pro42.7%
    Source
    Ornith-1.5-35B-A3B25.6%
    Source

    GPT-5.4 Pro leads this result

  • GPQA

    GPT-5.4 Pro—
    Ornith-1.5-35B-A3B89.2%
    Source

    Not directly comparable

  • GPQA-D

    GPT-5.4 Pro—
    Ornith-1.5-35B-A3B89.2%
    Source

    Not directly comparable

Math

  • IPhO 2025 (Theory)

    GPT-5.4 Pro93.5%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

  • FrontierMath (legacy)

    GPT-5.4 Pro50%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    GPT-5.4 Pro50.000%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    GPT-5.4 Pro37.500%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

Multimodal

  • MMMU-Pro

    GPT-5.4 Pro94%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

Frequently asked questions

Which is better, GPT-5.4 Pro or Ornith-1.5-35B-A3B?

GPT-5.4 Pro has the higher public score estimate, 61.25 versus 49.27, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, GPT-5.4 Pro or Ornith-1.5-35B-A3B?

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, GPT-5.4 Pro or Ornith-1.5-35B-A3B?

GPT-5.4 Pro leads the like-for-like agentic tasks comparison across 1 shared weighted benchmark row.

Which costs less, GPT-5.4 Pro or Ornith-1.5-35B-A3B?

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, GPT-5.4 Pro or Ornith-1.5-35B-A3B?

GPT-5.4 Pro has the larger documented context window: 1.05M, compared with 262K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 19, 2026

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