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Claude Opus 4.6 vs Ornith-1.5-9B

Updated October 2, 2026. Rank says Claude Opus 4.6 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

Claude Opus 4.6 has the higher public point estimate, 64.26 versus 28.63. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. 8 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
Anthropic logo

Anthropic

64.26/100

Supported · Public rank #40

90% interval 51.9–76.6

Model B

Ornith AI

28.63/100

Estimated · Public rank #180

Conditional range 14.3–43.0

Shared results
8
Claude Opus 4.6 only
26
Ornith-1.5-9B only
8
Like-for-like categories
0 / 8
Supported: Claude Opus 4.6 · 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.

  • Long documents

    Prompts that approach the documented context limit

    Claude Opus 4.6

    Claude Opus 4.6 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

    Ornith-1.5-9B is 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

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.

49.5Claude Opus 4.6—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.

2 categories rest 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.

Agentic

Directional only
Claude Opus 4.6
44.5
Supported · #47/119
Ornith-1.5-9B
18.0
Estimated · #101/119
Basis
BenchAlign v5.8 lane · 10 vs 7 public rows
Reading
Directional only

Knowledge

Directional only
Claude Opus 4.6
58.6
Estimated · #44/171
Ornith-1.5-9B
30.6
Estimated · #134/171
Basis
BenchAlign v5.8 lane · 9 vs 4 public rows
Reading
Directional only

Coding

Not comparable
Claude Opus 4.6
49.5
Supported · #45/144
Ornith-1.5-9B
Not ranked
Basis
BenchAlign v5.8 lane · 8 vs 5 public rows
Reading
Not comparable

Reasoning

Not comparable
Claude Opus 4.6
68.3
Unranked · 2 rankable rows
Ornith-1.5-9B
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Claude Opus 4.6
60.6
#29/49
Ornith-1.5-9B
Not ranked
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Claude Opus 4.6
Not ranked
Ornith-1.5-9B
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Claude Opus 4.6
51.0
#79/125
Ornith-1.5-9B
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Claude Opus 4.6
58.5
Unranked · 3 rankable rows
Ornith-1.5-9B
Not ranked
Basis
Provisional lane · 2 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.

Supported evidence per lane · 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

Claude Opus 4.6
$0.0175
Fits in one request
Ornith-1.5-9B
Self-hosted; infrastructure cost varies
Fits in one request

Ornith-1.5-9B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Claude Opus 4.6
$0.325
Fits in one request
Ornith-1.5-9B
Self-hosted; infrastructure cost varies
Fits in one request

Ornith-1.5-9B has no comparable published API token rate.

Cache-heavy agent loop

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

Claude Opus 4.6
$1.35
Fits in one request
Cached input priced at the published list-input rate
Ornith-1.5-9B
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable

Claude Opus 4.6 has no published cached-input rate, so cached tokens use its listed input 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.

Context window

Maximum documented context; output-token limits may be lower.

Claude Opus 4.6

1M

Ornith-1.5-9B

API model ID

Claude Opus 4.6

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.

Claude Opus 4.6

Not published

Ornith-1.5-9B

No comparable hosted API rate

Ornith-1.5-9B model card

Documented inputs

Claude Opus 4.6

Not sourced

Ornith-1.5-9B

Not sourced

Documented outputs

Claude Opus 4.6

Not sourced

Ornith-1.5-9B

Not sourced

Provider availability

Claude Opus 4.6

Not sourced

Ornith-1.5-9B

Not sourced

Reasoning profile

Claude Opus 4.6

Non-Reasoning

Ornith-1.5-9B

Reasoning

Weight access

Claude Opus 4.6

Proprietary

Ornith-1.5-9B

Open Weight

License

Claude Opus 4.6

Proprietary

Ornith-1.5-9B

Open Weight

Release date

Claude Opus 4.6

2026-02-01

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
Claude Opus 4.6 has the higher public point estimate, 64.26 versus 28.63. Their conditional score ranges do not overlap. These ranges do not establish rank confidence.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Claude Opus 4.6 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Claude Opus 4.6 or Ornith-1.5-9B?

Claude Opus 4.6 has the higher public point estimate, 64.26 versus 28.63. Their conditional score ranges do not 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, Claude Opus 4.6 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, Claude Opus 4.6 or Ornith-1.5-9B?

Claude Opus 4.6 scores higher for agentic tasks on the public lane, 44.5 to 18. Ornith-1.5-9B is 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, Claude Opus 4.6 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, Claude Opus 4.6 or Ornith-1.5-9B?

Claude Opus 4.6 has the larger documented context window: 1M, compared with 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 evidence42 rows

Agentic

  • Terminal-Bench 2.0

    Claude Opus 4.665.4%
    Source
    Ornith-1.5-9B—

    Not directly comparable

  • BrowseComp

    Claude Opus 4.683.7%
    Source
    Ornith-1.5-9B56.4%
    Source

    Claude Opus 4.6 leads this result

  • OSWorld-Verified

    Claude Opus 4.672.7%
    Source
    Ornith-1.5-9B—

    Not directly comparable

  • Claw-Eval

    Claude Opus 4.670.4%
    Source
    Ornith-1.5-9B66.5%
    Source

    Claude Opus 4.6 leads this result

  • DeepSearchQA

    Claude Opus 4.673.7%
    Source
    Ornith-1.5-9B—

    Not directly comparable

  • CyberGym

    Claude Opus 4.666.6%
    Source
    Ornith-1.5-9B—

    Not directly comparable

  • Gert Labs

    Claude Opus 4.661.85%
    Source
    Ornith-1.5-9B—

    Not directly comparable

  • ResearchClawBench

    Claude Opus 4.619.9%
    Source
    Ornith-1.5-9B—

    Not directly comparable

  • JobBench

    Claude Opus 4.636.7%
    Source
    Ornith-1.5-9B—

    Not directly comparable

  • ApprenticeBench

    Claude Opus 4.65%
    Source
    Ornith-1.5-9B—

    Not directly comparable

  • Terminal-Bench 2.1

    Claude Opus 4.6—
    Ornith-1.5-9B46.2%
    Source

    Not directly comparable

  • HLE w/ tools

    Claude Opus 4.6—
    Ornith-1.5-9B30.5%
    Source

    Not directly comparable

  • MCP Atlas

    Claude Opus 4.6—
    Ornith-1.5-9B54.2%
    Source

    Not directly comparable

  • Toolathlon-Verified

    Claude Opus 4.6—
    Ornith-1.5-9B41.2%
    Source

    Not directly comparable

  • WideResearch

    Claude Opus 4.6—
    Ornith-1.5-9B59.5%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Claude Opus 4.680.8%
    Source
    Ornith-1.5-9B70.6%
    Source

    Claude Opus 4.6 leads this result

  • SWE-bench Verified*

    Claude Opus 4.675.6%
    Source
    Ornith-1.5-9B—

    Not directly comparable

  • LiveCodeBench Pro

    Claude Opus 4.670.7%
    Source
    Ornith-1.5-9B—

    Not directly comparable

  • SWE-bench Pro

    Claude Opus 4.653.4%
    Source
    Ornith-1.5-9B47.5%
    Source

    Claude Opus 4.6 leads this result

  • SWE-Rebench

    Claude Opus 4.665.3%
    Source
    Ornith-1.5-9B—

    Not directly comparable

  • React Native Evals

    Claude Opus 4.684.1%
    Source
    Ornith-1.5-9B—

    Not directly comparable

  • Vibe Code Bench

    Claude Opus 4.657.57%
    Source
    Ornith-1.5-9B—

    Not directly comparable

  • FrontierCode 1.1 Main

    Claude Opus 4.626.9%
    Source
    Ornith-1.5-9B—

    Not directly comparable

  • Terminal-Bench 2.1

    Claude Opus 4.6—
    Ornith-1.5-9B46.2%
    Source

    Not directly comparable

  • SWE Multilingual

    Claude Opus 4.6—
    Ornith-1.5-9B54.4%
    Source

    Not directly comparable

  • NL2Repo

    Claude Opus 4.6—
    Ornith-1.5-9B32.4%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    Claude Opus 4.677.3%
    Source
    Ornith-1.5-9B—

    Not directly comparable

  • ERQA

    Claude Opus 4.651.6%
    Source
    Ornith-1.5-9B—

    Not directly comparable

  • ScreenSpot Pro

    Claude Opus 4.683.1%
    Source
    Ornith-1.5-9B—

    Not directly comparable

  • MedXpertQA (MM)

    Claude Opus 4.664.8%
    Source
    Ornith-1.5-9B—

    Not directly comparable

Knowledge

  • GPQA

    Claude Opus 4.691.3%
    Source
    Ornith-1.5-9B86.4%
    Source

    Claude Opus 4.6 leads this result

  • GPQA-D

    Claude Opus 4.689.2%
    Source
    Ornith-1.5-9B86.4%
    Source

    Claude Opus 4.6 leads this result

  • SuperGPQA

    Claude Opus 4.695%
    Source
    Ornith-1.5-9B—

    Not directly comparable

  • MMLU-Pro

    Claude Opus 4.682%
    Source
    Ornith-1.5-9B—

    Not directly comparable

  • MMLU-Pro (Arcee)

    Claude Opus 4.689.1%
    Source
    Ornith-1.5-9B—

    Not directly comparable

  • HLE

    Claude Opus 4.653%
    Source
    Ornith-1.5-9B20.2%
    Source

    Claude Opus 4.6 leads this result

  • HLE w/o tools

    Claude Opus 4.640%
    Source
    Ornith-1.5-9B20.2%
    Source

    Claude Opus 4.6 leads this result

  • HealthBench Hard

    Claude Opus 4.614.8%
    Source
    Ornith-1.5-9B—

    Not directly comparable

  • MedXpertQA (Text)

    Claude Opus 4.652.1%
    Source
    Ornith-1.5-9B—

    Not directly comparable

Math

  • AIME25 (Arcee)

    Claude Opus 4.699.8%
    Source
    Ornith-1.5-9B—

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    Claude Opus 4.640.700%
    Source
    Ornith-1.5-9B—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Claude Opus 4.622.900%
    Source
    Ornith-1.5-9B—

    Not directly comparable

42 public results · 8 shared

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Last updated October 2, 2026