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Data

Gemma 4 E2B vs Ornith-1.5-9B

Updated October 2, 2026. Rank says Gemma 4 E2B is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

Gemma 4 E2B has the higher public point estimate, 30.32 versus 28.63. Their conditional score ranges overlap. These ranges do not establish rank confidence. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
Google logo

Google

30.32/100

Estimated · Public rank #165

Conditional range 16.0–44.7

Model B

Ornith AI

28.63/100

Estimated · Public rank #180

Conditional range 14.3–43.0

Shared results
1
Gemma 4 E2B only
1
Ornith-1.5-9B only
15
Like-for-like categories
0 / 8
Estimated: Gemma 4 E2B and Ornith-1.5-9B. Conditional ranges do not establish rank confidence.How the comparison works

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-1.5-9B

    Ornith-1.5-9B 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

    Ornith-1.5-9B is not ranked on the public lane for coding, so no winner is named for coding.

    Confidence: limited
  • Agentic work

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

    Not enough matched evidence

    Gemma 4 E2B is not ranked on the public lane for agentic, so no winner is named for agentic.

    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. Gemma 4 E2B does not fit this workload in one request. Gemma 4 E2B has no comparable published API token rate. Ornith-1.5-9B 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

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

13.5Gemma 4 E2B—Ornith-1.5-9B

Not comparable · BenchAlign v5.8

The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Same basis rules as the category table below

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.8 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.

Knowledge

Directional only
Gemma 4 E2B
24.5
Estimated · #158/171
Ornith-1.5-9B
30.6
Estimated · #134/171
Basis
BenchAlign v5.8 lane · 2 vs 4 public rows
Reading
Directional only

Agentic

Not comparable
Gemma 4 E2B
Not ranked
Ornith-1.5-9B
18.0
Estimated · #101/119
Basis
BenchAlign v5.8 lane · 0 vs 7 public rows
Reading
Not comparable

Coding

Not comparable
Gemma 4 E2B
13.5
Estimated · #135/144
Ornith-1.5-9B
Not ranked
Basis
BenchAlign v5.8 lane · 0 vs 5 public rows
Reading
Not comparable

Reasoning

Not comparable
Gemma 4 E2B
33.3
Unranked · 2 rankable rows
Ornith-1.5-9B
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Gemma 4 E2B
26.9
Unranked · 1 rankable row
Ornith-1.5-9B
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Gemma 4 E2B
Not ranked
Ornith-1.5-9B
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Gemma 4 E2B
42.4
#97/125
Ornith-1.5-9B
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Gemma 4 E2B
Not ranked
Ornith-1.5-9B
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 v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Bars run 0–100Methodology

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

Gemma 4 E2B
Self-hosted; infrastructure cost varies
Fits in one request
Ornith-1.5-9B
Self-hosted; infrastructure cost varies
Fits in one request

Gemma 4 E2B 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

Gemma 4 E2B
Self-hosted; infrastructure cost varies
Fits in one request
Ornith-1.5-9B
Self-hosted; infrastructure cost varies
Fits in one request

Gemma 4 E2B 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

Gemma 4 E2B
Self-hosted; infrastructure cost varies
Does not fit in one request
Cached-input rate unavailable
Ornith-1.5-9B
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable

Gemma 4 E2B does not fit this workload in one request. Gemma 4 E2B has no comparable published API token rate. Ornith-1.5-9B has no comparable published API token rate.

Cached input falls back to the list input rate only where a cached rate is unpublished

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

API model ID

Gemma 4 E2B

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.

Gemma 4 E2B

No comparable hosted API rate

Ornith-1.5-9B

No comparable hosted API rate

Ornith-1.5-9B model card

Reasoning profile

Gemma 4 E2B

Reasoning

Ornith-1.5-9B

Reasoning

Weight access

Gemma 4 E2B

Open Weight

Ornith-1.5-9B

Open Weight

License

Gemma 4 E2B

Open Weight

Ornith-1.5-9B

Open Weight

Release date

Gemma 4 E2B

2026-04-02

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
Gemma 4 E2B has the higher public point estimate, 30.32 versus 28.63. Their conditional score ranges overlap. These ranges do not establish rank confidence.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Ornith-1.5-9B has the larger documented window (262K).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Gemma 4 E2B or Ornith-1.5-9B?

Gemma 4 E2B has the higher public point estimate, 30.32 versus 28.63. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Gemma 4 E2B or Ornith-1.5-9B?

Ornith-1.5-9B is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Gemma 4 E2B or Ornith-1.5-9B?

Gemma 4 E2B is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Gemma 4 E2B 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, Gemma 4 E2B or Ornith-1.5-9B?

Ornith-1.5-9B has the larger documented context window: 262K, compared with 128K.

Benchmark evidence

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

Browse raw public benchmark evidence17 rows

Agentic

  • Terminal-Bench 2.1

    Gemma 4 E2B—
    Ornith-1.5-9B46.2%
    Source

    Not directly comparable

  • HLE w/ tools

    Gemma 4 E2B—
    Ornith-1.5-9B30.5%
    Source

    Not directly comparable

  • MCP Atlas

    Gemma 4 E2B—
    Ornith-1.5-9B54.2%
    Source

    Not directly comparable

  • Toolathlon-Verified

    Gemma 4 E2B—
    Ornith-1.5-9B41.2%
    Source

    Not directly comparable

  • WideResearch

    Gemma 4 E2B—
    Ornith-1.5-9B59.5%
    Source

    Not directly comparable

  • BrowseComp

    Gemma 4 E2B—
    Ornith-1.5-9B56.4%
    Source

    Not directly comparable

  • Claw-Eval

    Gemma 4 E2B—
    Ornith-1.5-9B66.5%
    Source

    Not directly comparable

Coding

  • Terminal-Bench 2.1

    Gemma 4 E2B—
    Ornith-1.5-9B46.2%
    Source

    Not directly comparable

  • SWE-bench Verified

    Gemma 4 E2B—
    Ornith-1.5-9B70.6%
    Source

    Not directly comparable

  • SWE-bench Pro

    Gemma 4 E2B—
    Ornith-1.5-9B47.5%
    Source

    Not directly comparable

  • SWE Multilingual

    Gemma 4 E2B—
    Ornith-1.5-9B54.4%
    Source

    Not directly comparable

  • NL2Repo

    Gemma 4 E2B—
    Ornith-1.5-9B32.4%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Gemma 4 E2B43.4%
    Source
    Ornith-1.5-9B86.4%
    Source

    Ornith-1.5-9B leads this result

  • MMLU-Pro

    Gemma 4 E2B60%
    Source
    Ornith-1.5-9B—

    Not directly comparable

  • GPQA-D

    Gemma 4 E2B—
    Ornith-1.5-9B86.4%
    Source

    Not directly comparable

  • HLE

    Gemma 4 E2B—
    Ornith-1.5-9B20.2%
    Source

    Not directly comparable

  • HLE w/o tools

    Gemma 4 E2B—
    Ornith-1.5-9B20.2%
    Source

    Not directly comparable

17 public results · 1 shared

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Last updated October 2, 2026