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Ling 3.0 Flash FP8 vs Ornith-1.5-9B

Updated October 2, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

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Decision reading

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. 2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
InclusionAI logo

InclusionAI

—

Evidence status unavailable

90% interval unavailable

Model B

Ornith AI

28.63/100

Estimated · Public rank #180

Conditional range 14.3–43.0

Shared results
2
Ling 3.0 Flash FP8 only
2
Ornith-1.5-9B only
14
Like-for-like categories
0 / 8
Estimated: 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.

No workload recommendation clears the current evidence threshold.

Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.

Show secondary and unsupported calls
  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    Ling 3.0 Flash FP8 and Ornith-1.5-9B are 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

    Ling 3.0 Flash FP8 is not ranked on the public lane for agentic, so no winner is named for agentic.

    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

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.

—Ling 3.0 Flash FP8—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.

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.

Agentic

Not comparable
Ling 3.0 Flash FP8
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
Ling 3.0 Flash FP8
Not ranked
Ornith-1.5-9B
Not ranked
Basis
BenchAlign v5.8 lane · 1 vs 5 public rows
Reading
Not comparable

Reasoning

Not comparable
Ling 3.0 Flash FP8
Not ranked
Ornith-1.5-9B
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Ling 3.0 Flash FP8
Not ranked
Ornith-1.5-9B
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Ling 3.0 Flash FP8
Not ranked
Ornith-1.5-9B
30.6
Estimated · #134/171
Basis
BenchAlign v5.8 lane · 2 vs 4 public rows
Reading
Not comparable

Multilingual

Not comparable
Ling 3.0 Flash FP8
Not ranked
Ornith-1.5-9B
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Ling 3.0 Flash FP8
69.0
#65/125
Ornith-1.5-9B
Not ranked
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Ling 3.0 Flash FP8
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

Ling 3.0 Flash FP8
API rate not published
Fits in one request
Ornith-1.5-9B
Self-hosted; infrastructure cost varies
Fits in one request

Ling 3.0 Flash FP8 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

Ling 3.0 Flash FP8
API rate not published
Fits in one request
Ornith-1.5-9B
Self-hosted; infrastructure cost varies
Fits in one request

Ling 3.0 Flash FP8 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

Ling 3.0 Flash FP8
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

Ling 3.0 Flash FP8 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

Ling 3.0 Flash FP8

Not sourced

Ornith-1.5-9B

Not sourced

Documented inputs

Ling 3.0 Flash FP8

Not sourced

Ornith-1.5-9B

Not sourced

Documented outputs

Ling 3.0 Flash FP8

Not sourced

Ornith-1.5-9B

Not sourced

Provider availability

Ling 3.0 Flash FP8

Not sourced

Ornith-1.5-9B

Not sourced

Reasoning profile

Ling 3.0 Flash FP8

Reasoning

Ornith-1.5-9B

Reasoning

Weight access

Ling 3.0 Flash FP8

Open Weight

Ornith-1.5-9B

Open Weight

License

Ling 3.0 Flash FP8

Open Weight

Ornith-1.5-9B

Open Weight

Release date

Ling 3.0 Flash FP8

2026-08-04

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
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
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.

Questions

Which is better, Ling 3.0 Flash FP8 or Ornith-1.5-9B?

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Ling 3.0 Flash FP8 or Ornith-1.5-9B?

Ling 3.0 Flash FP8 and Ornith-1.5-9B are not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Ling 3.0 Flash FP8 or Ornith-1.5-9B?

Ling 3.0 Flash FP8 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Ling 3.0 Flash FP8 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, Ling 3.0 Flash FP8 or Ornith-1.5-9B?

Both models list the same context window, 262K.

Benchmark evidence

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

Browse raw public benchmark evidence18 rows

Agentic

  • Terminal-Bench 2.1

    Ling 3.0 Flash FP8—
    Ornith-1.5-9B46.2%
    Source

    Not directly comparable

  • HLE w/ tools

    Ling 3.0 Flash FP8—
    Ornith-1.5-9B30.5%
    Source

    Not directly comparable

  • MCP Atlas

    Ling 3.0 Flash FP8—
    Ornith-1.5-9B54.2%
    Source

    Not directly comparable

  • Toolathlon-Verified

    Ling 3.0 Flash FP8—
    Ornith-1.5-9B41.2%
    Source

    Not directly comparable

  • WideResearch

    Ling 3.0 Flash FP8—
    Ornith-1.5-9B59.5%
    Source

    Not directly comparable

  • BrowseComp

    Ling 3.0 Flash FP8—
    Ornith-1.5-9B56.4%
    Source

    Not directly comparable

  • Claw-Eval

    Ling 3.0 Flash FP8—
    Ornith-1.5-9B66.5%
    Source

    Not directly comparable

Coding

  • SciCode

    Ling 3.0 Flash FP840.4%
    Source
    Ornith-1.5-9B—

    Not directly comparable

  • Terminal-Bench 2.1

    Ling 3.0 Flash FP8—
    Ornith-1.5-9B46.2%
    Source

    Not directly comparable

  • SWE-bench Verified

    Ling 3.0 Flash FP8—
    Ornith-1.5-9B70.6%
    Source

    Not directly comparable

  • SWE-bench Pro

    Ling 3.0 Flash FP8—
    Ornith-1.5-9B47.5%
    Source

    Not directly comparable

  • SWE Multilingual

    Ling 3.0 Flash FP8—
    Ornith-1.5-9B54.4%
    Source

    Not directly comparable

  • NL2Repo

    Ling 3.0 Flash FP8—
    Ornith-1.5-9B32.4%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Ling 3.0 Flash FP884%
    Source
    Ornith-1.5-9B86.4%
    Source

    Ornith-1.5-9B leads this result

  • GPQA-D

    Ling 3.0 Flash FP884.0%
    Source
    Ornith-1.5-9B86.4%
    Source

    Ornith-1.5-9B leads this result

  • HLE

    Ling 3.0 Flash FP8—
    Ornith-1.5-9B20.2%
    Source

    Not directly comparable

  • HLE w/o tools

    Ling 3.0 Flash FP8—
    Ornith-1.5-9B20.2%
    Source

    Not directly comparable

Instruction following

  • IFBench

    Ling 3.0 Flash FP873.4%
    Source
    Ornith-1.5-9B—

    Not directly comparable

18 public results · 2 shared

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Last updated October 2, 2026