Skip to main content
Radar

Keep up with the models you depend on. Follow price changes, retirements, and API updates.Follow the models you depend on.

Follow model changes
BenchLM
Google logo
Model A
Gemini 3 Pro

Google

66.43/100

Supported · Public rank #33

90% interval 56.3–76.6

Gemini 3 Pro vs Ornith-1.5-35B-A3B

Updated September 10, 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

37.06/100

Estimated · Public rank #212

90% interval 27.2–46.9

Decision reading

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.

0 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Share or export

Share on XLinkedInSocial cardCSVJSON

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

    Gemini 3 Pro

    Gemini 3 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

    Gemini 3 Pro and Ornith-1.5-35B-A3B are scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.

    Confidence: limited

  • Agentic work

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

    Not enough matched evidence

    Gemini 3 Pro and Ornith-1.5-35B-A3B are 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: 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
0
Gemini 3 Pro only
12
Ornith-1.5-35B-A3B only
18
Like-for-like categories
0 / 8

3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign 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
Gemini 3 Pro
58.5
Estimated · #26/152
Ornith-1.5-35B-A3B
46.7
Estimated · #75/152
Basis
BenchAlign lane · 2 vs 7 public rows
Reading
Directional only

Coding

Directional only
Gemini 3 Pro
59.9
Estimated · #22/151
Ornith-1.5-35B-A3B
46.6
Estimated · #79/151
Basis
BenchAlign lane · 1 vs 7 public rows
Reading
Directional only

Knowledge

Directional only
Gemini 3 Pro
64.7
Estimated · #25/183
Ornith-1.5-35B-A3B
41.4
Estimated · #127/183
Basis
BenchAlign lane · 0 vs 4 public rows
Reading
Directional only

Reasoning

Not comparable
Gemini 3 Pro
36.7
Unranked · 3 rankable rows
Ornith-1.5-35B-A3B
Not ranked
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Gemini 3 Pro
55.4
Unranked · 2 rankable rows
Ornith-1.5-35B-A3B
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Gemini 3 Pro
Not ranked
Ornith-1.5-35B-A3B
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Gemini 3 Pro
74.2
#17/48
Ornith-1.5-35B-A3B
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Gemini 3 Pro
86.1
#39/123
Ornith-1.5-35B-A3B
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

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

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.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

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

Gemini 3 Pro
$0.008
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

Gemini 3 Pro
$0.136
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

Gemini 3 Pro
$0.56
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

Gemini 3 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.

API model ID

Gemini 3 Pro

Not sourced

Ornith-1.5-35B-A3B

Not sourced

Cached-input rate

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

Gemini 3 Pro

Not published

Ornith-1.5-35B-A3B

No comparable hosted API rate

Ornith-1.5-35B-A3B model card

Documented inputs

Gemini 3 Pro

Not sourced

Ornith-1.5-35B-A3B

Not sourced

Documented outputs

Gemini 3 Pro

Not sourced

Ornith-1.5-35B-A3B

Not sourced

Provider availability

Gemini 3 Pro

Not sourced

Ornith-1.5-35B-A3B

Not sourced

Reasoning profile

Gemini 3 Pro

Non-Reasoning

Ornith-1.5-35B-A3B

Reasoning

Weight access

Gemini 3 Pro

Proprietary

Ornith-1.5-35B-A3B

Open Weight

License

Gemini 3 Pro

Proprietary

Ornith-1.5-35B-A3B

Open Weight

Release date

Gemini 3 Pro

2025-11-18

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
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Gemini 3 Pro has the larger documented window (2M).

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 evidence30 rows

Agentic

  • Gert Labs

    Gemini 3 Pro63.23%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

  • JobBench

    Gemini 3 Pro11.4%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

  • Terminal-Bench 2.1

    Gemini 3 Pro—
    Ornith-1.5-35B-A3B67.8%
    Source

    Not directly comparable

  • HLE w/ tools

    Gemini 3 Pro—
    Ornith-1.5-35B-A3B33.4%
    Source

    Not directly comparable

  • MCP Atlas

    Gemini 3 Pro—
    Ornith-1.5-35B-A3B70.2%
    Source

    Not directly comparable

  • Toolathlon-Verified

    Gemini 3 Pro—
    Ornith-1.5-35B-A3B48.7%
    Source

    Not directly comparable

  • WideResearch

    Gemini 3 Pro—
    Ornith-1.5-35B-A3B67.8%
    Source

    Not directly comparable

  • BrowseComp

    Gemini 3 Pro—
    Ornith-1.5-35B-A3B67.6%
    Source

    Not directly comparable

  • Claw-Eval

    Gemini 3 Pro—
    Ornith-1.5-35B-A3B72.5%
    Source

    Not directly comparable

Coding

  • Vibe Code Bench

    Gemini 3 Pro14.30%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

  • Terminal-Bench 2.1

    Gemini 3 Pro—
    Ornith-1.5-35B-A3B67.8%
    Source

    Not directly comparable

  • SWE-bench Verified

    Gemini 3 Pro—
    Ornith-1.5-35B-A3B79%
    Source

    Not directly comparable

  • SWE-bench Pro

    Gemini 3 Pro—
    Ornith-1.5-35B-A3B59.6%
    Source

    Not directly comparable

  • SWE Multilingual

    Gemini 3 Pro—
    Ornith-1.5-35B-A3B71.4%
    Source

    Not directly comparable

  • DeepSWE

    Gemini 3 Pro—
    Ornith-1.5-35B-A3B22.0%
    Source

    Not directly comparable

  • frontierBench

    Gemini 3 Pro—
    Ornith-1.5-35B-A3B5.1%
    Source

    Not directly comparable

  • NL2Repo

    Gemini 3 Pro—
    Ornith-1.5-35B-A3B46.2%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    Gemini 3 Pro31.1%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

Knowledge

  • GPQA

    Gemini 3 Pro—
    Ornith-1.5-35B-A3B89.2%
    Source

    Not directly comparable

  • GPQA-D

    Gemini 3 Pro—
    Ornith-1.5-35B-A3B89.2%
    Source

    Not directly comparable

  • HLE

    Gemini 3 Pro—
    Ornith-1.5-35B-A3B25.6%
    Source

    Not directly comparable

  • HLE w/o tools

    Gemini 3 Pro—
    Ornith-1.5-35B-A3B25.6%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Gemini 3 Pro37.600%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Gemini 3 Pro18.750%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

Multimodal

  • MMMU-Pro

    Gemini 3 Pro81%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

  • MathVision

    Gemini 3 Pro86.6%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

  • VideoMMMU

    Gemini 3 Pro87.6%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

  • ScreenSpot Pro

    Gemini 3 Pro72.7%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

  • CharXiv

    Gemini 3 Pro81.4%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

  • V*

    Gemini 3 Pro88.0%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

Frequently asked questions

Which is better, Gemini 3 Pro or Ornith-1.5-35B-A3B?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Gemini 3 Pro or Ornith-1.5-35B-A3B?

Gemini 3 Pro scores higher for coding on the public lane, 59.9 to 46.6. Gemini 3 Pro and Ornith-1.5-35B-A3B are scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, Gemini 3 Pro or Ornith-1.5-35B-A3B?

Gemini 3 Pro scores higher for agentic tasks on the public lane, 58.5 to 46.7. Gemini 3 Pro and Ornith-1.5-35B-A3B are 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, Gemini 3 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, Gemini 3 Pro or Ornith-1.5-35B-A3B?

Gemini 3 Pro has the larger documented context window: 2M, compared with 262K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 10, 2026

Watch Gemini 3 Pro vs Ornith-1.5-35B-A3B

One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.

Read a sample issue

Join 2,000+ readers.