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BenchLM
Model A
Claude Fable 5

Anthropic

82.6/100

Supported · Public rank #3

90% interval 79.8–85.5

Claude Fable 5 vs Ornith-1.0-35B

Updated August 13, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
Ornith-1.0-35B

DeepReinforce AI

—

Evidence status unavailable

90% interval unavailable

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.

4 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.

  • Coding work

    Code generation, repair, and software-engineering tasks

    Claude Fable 5

    Claude Fable 5 leads on the same 2 weighted benchmark rows.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    Claude Fable 5

    Claude Fable 5 has the larger documented context window.

    Confidence: documented

Show secondary and unsupported calls

  • Agentic work

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

    Not enough matched evidence

    The category averages use different weighted benchmark sets, so they are 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: 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
4
Claude Fable 5 only
12
Ornith-1.0-35B only
3
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.

Coding

Like-for-like
Claude Fable 5
89.2
Ornith-1.0-35B
65.9
Weighted basis
2 vs 2 rows
Reading
Claude Fable 5 leads

Agentic

Directional only
Claude Fable 5
84.6
Ornith-1.0-35B
64.2
Weighted basis
2 vs 1 rows
Reading
Directional only

Reasoning

Not comparable
Claude Fable 5
Not measured
Ornith-1.0-35B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Claude Fable 5
Not measured
Ornith-1.0-35B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
Claude Fable 5
Not measured
Ornith-1.0-35B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Claude Fable 5
Not measured
Ornith-1.0-35B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Claude Fable 5
57.9
Ornith-1.0-35B
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Claude Fable 5
Not measured
Ornith-1.0-35B
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

Claude Fable 5
$0.035
Fits in one request
Ornith-1.0-35B
Self-hosted; infrastructure cost varies
Fits in one request

Ornith-1.0-35B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Claude Fable 5
$0.65
Fits in one request
Ornith-1.0-35B
Self-hosted; infrastructure cost varies
Fits in one request

Ornith-1.0-35B has no comparable published API token rate.

Cache-heavy agent loop

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

Claude Fable 5
$0.9
Fits in one request
Ornith-1.0-35B
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable

Ornith-1.0-35B 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.

Claude Fable 5

1M

Anthropic model overview

Ornith-1.0-35B

256K

Cached-input rate

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

Claude Fable 5

$1 per 1M cached input tokens

Claude API pricing

Ornith-1.0-35B

No comparable hosted API rate

Reasoning profile

Claude Fable 5

Reasoning

Ornith-1.0-35B

Reasoning

Weight access

Claude Fable 5

Proprietary

Ornith-1.0-35B

Open Weight

License

Claude Fable 5

Proprietary

Ornith-1.0-35B

Open Weight

Release date

Claude Fable 5

2026-06-09

Ornith-1.0-35B

2026-06-01

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
Claude Fable 5 has the larger documented window (1M).

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 evidence19 rows

Agentic

  • Terminal-Bench 3.0

    Claude Fable 534.0%
    Source
    Ornith-1.0-35B—

    Not directly comparable

  • Terminal-Bench 2.0

    Claude Fable 584.3%
    Source
    Ornith-1.0-35B64.2%
    Source

    Claude Fable 5 leads this result

  • OSWorld-Verified

    Claude Fable 585%
    Source
    Ornith-1.0-35B—

    Not directly comparable

  • Claw-Eval

    Claude Fable 5—
    Ornith-1.0-35B69.8%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Claude Fable 595%
    Source
    Ornith-1.0-35B75.6%
    Source

    Claude Fable 5 leads this result

  • SWE-bench Pro

    Claude Fable 580%
    Source
    Ornith-1.0-35B50.4%
    Source

    Claude Fable 5 leads this result

  • FrontierCode 1.1 Main

    Claude Fable 553.5%
    Source
    Ornith-1.0-35B—

    Not directly comparable

  • Terminal-Bench 2.0

    Claude Fable 584.3%
    Source
    Ornith-1.0-35B64.2%
    Source

    Claude Fable 5 leads this result

  • cursorBench31

    Claude Fable 570.6%
    Source
    Ornith-1.0-35B—

    Not directly comparable

  • cursorBench32

    Claude Fable 570.5%
    Source
    Ornith-1.0-35B—

    Not directly comparable

  • VulcanBench v3

    Claude Fable 587.0%
    Source
    Ornith-1.0-35B—

    Not directly comparable

  • APEX-SWE

    Claude Fable 558.8%
    Source
    Ornith-1.0-35B—

    Not directly comparable

  • EEBench

    Claude Fable 554.2%
    Source
    Ornith-1.0-35B—

    Not directly comparable

  • 3DCodeBench

    Claude Fable 543.7%
    Source
    Ornith-1.0-35B—

    Not directly comparable

  • CADGenBench Generation

    Claude Fable 537.3%
    Source
    Ornith-1.0-35B—

    Not directly comparable

  • SWE Multilingual

    Claude Fable 5—
    Ornith-1.0-35B69.3%
    Source

    Not directly comparable

  • NL2Repo

    Claude Fable 5—
    Ornith-1.0-35B34.6%
    Source

    Not directly comparable

Multimodal

  • Blueprint-Bench 2

    Claude Fable 538.6%
    Source
    Ornith-1.0-35B—

    Not directly comparable

  • OfficeQA Pro

    Claude Fable 557.9%
    Source
    Ornith-1.0-35B—

    Not directly comparable

Frequently asked questions

Which is better, Claude Fable 5 or Ornith-1.0-35B?

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, Claude Fable 5 or Ornith-1.0-35B?

Claude Fable 5 leads the like-for-like coding comparison across 2 shared weighted benchmark rows.

Which is better for agentic tasks, Claude Fable 5 or Ornith-1.0-35B?

The current agentic tasks averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.

Which costs less, Claude Fable 5 or Ornith-1.0-35B?

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, Claude Fable 5 or Ornith-1.0-35B?

Claude Fable 5 has the larger documented context window: 1M, compared with 256K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 13, 2026

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