Skip to main content
BenchLM
Data

Claude Opus 5.5 vs Clef-Omni

Updated October 20, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

Share or export
Share on XLinkedInAPI/MCP

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.

Model A
Anthropic logo

Anthropic

86.69/100

Supported · Public rank #1

90% interval 81.8–91.6

Model B

Cloudflare

—

Evidence status unavailable

90% interval unavailable

Shared results
0
Claude Opus 5.5 only
63
Clef-Omni only
0
Like-for-like categories
0 / 8
Supported: Claude Opus 5.5How 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 5.5

    Claude Opus 5.5 has the larger documented context window.

    Confidence: documented
  • Chat turn cost

    1K fresh input + 500 output tokens

    Clef-Omni

    Clef-Omni has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates
Show secondary and unsupported calls
  • Repository review cost

    50K fresh input + 3K output tokens

    Clef-Omni

    Clef-Omni has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates
  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    Clef-Omni 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

    Clef-Omni is not ranked on the public lane for agentic, so no winner is named for agentic.

    Confidence: limited
  • 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. Clef-Omni does not fit this workload in one request. Clef-Omni has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

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.

82.7Claude Opus 5.5—Clef-Omni

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.

A shared-evidence shape is not available.

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

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
Claude Opus 5.5
90.4
Supported · #1/131
Clef-Omni
Not ranked
Basis
BenchAlign v5.8 lane · 15 vs 0 public rows
Reading
Not comparable

Coding

Not comparable
Claude Opus 5.5
82.7
Supported · #2/152
Clef-Omni
Not ranked
Basis
BenchAlign v5.8 lane · 11 vs 0 public rows
Reading
Not comparable

Reasoning

Not comparable
Claude Opus 5.5
86.1
#4/27
Clef-Omni
Not ranked
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Claude Opus 5.5
92.2
#3/55
Clef-Omni
Not ranked
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Claude Opus 5.5
90.3
Supported · #1/187
Clef-Omni
Not ranked
Basis
BenchAlign v5.8 lane · 16 vs 0 public rows
Reading
Not comparable

Multilingual

Not comparable
Claude Opus 5.5
Not ranked
Clef-Omni
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Claude Opus 5.5
Not ranked
Clef-Omni
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Claude Opus 5.5
97.3
Unranked · 4 rankable rows
Clef-Omni
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 5.5
$0.014
Fits in one request
Clef-Omni
$0.00015
Fits in one request

Clef-Omni has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Claude Opus 5.5
$0.26
Fits in one request
Clef-Omni
$0.0075
Fits in one request

Clef-Omni has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

Claude Opus 5.5
$0.32
Fits in one request
Clef-Omni
$0.033
Does not fit in one request
Cached input priced at the published list-input rate

Clef-Omni does not fit this workload in one request. Clef-Omni has no published cached-input rate, so cached tokens use its listed input 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.

Reasoning profile

Claude Opus 5.5

Reasoning

Clef-Omni

Unspecified

Weight access

Claude Opus 5.5

Proprietary

Clef-Omni

Open Weight

License

Claude Opus 5.5

Proprietary

Clef-Omni

Open Weight

Release date

Claude Opus 5.5

2026-09-22

Clef-Omni

2026-10-09

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
Repository review: $0.26 vs $0.0075. Cache-heavy agent loop: $0.32 vs $0.033.
Context tradeoff
Claude Opus 5.5 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Claude Opus 5.5 or Clef-Omni?

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, Claude Opus 5.5 or Clef-Omni?

Clef-Omni is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Claude Opus 5.5 or Clef-Omni?

Clef-Omni is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Claude Opus 5.5 or Clef-Omni?

For the stated presets, chat costs $0.014 on Claude Opus 5.5 and $0.00015 on Clef-Omni; repository review costs $0.26 and $0.0075; the cache-heavy agent loop costs $0.32 and $0.033. Clef-Omni does not fit this workload in one request. Clef-Omni has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Claude Opus 5.5 or Clef-Omni?

Claude Opus 5.5 has the larger documented context window: 1M, compared with 64K.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

Claude Opus 5.5
API / mo$18,000
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Clef-Omni
API / mo$113
Self-host / mo$298
Break-even95M/day
Model the full break-even

Benchmark evidence

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

Browse raw public benchmark evidence63 rows

Agentic

  • Terminal-Bench 4.0

    Claude Opus 5.566.40%
    Source
    Clef-Omni—

    Not directly comparable

  • Terminal-Bench-Science 0.1

    Claude Opus 5.558.7%
    Source
    Clef-Omni—

    Not directly comparable

  • AutomationBench

    Claude Opus 5.540.0%
    Source
    Clef-Omni—

    Not directly comparable

  • HLE w/ tools

    Claude Opus 5.567.7%
    Source
    Clef-Omni—

    Not directly comparable

  • OSWorld 2.0

    Claude Opus 5.548.7%
    Source
    Clef-Omni—

    Not directly comparable

  • LAB all-pass (Harvey held-out)

    Claude Opus 5.58.3%
    Source
    Clef-Omni—

    Not directly comparable

  • LAB criterion-pass (Harvey held-out)

    Claude Opus 5.591.2%
    Source
    Clef-Omni—

    Not directly comparable

  • Toolathlon-Verified

    Claude Opus 5.577.8%
    Source
    Clef-Omni—

    Not directly comparable

  • Toolathlon Verified Pass@3

    Claude Opus 5.582.4%
    Source
    Clef-Omni—

    Not directly comparable

  • Toolathlon Verified Pass³

    Claude Opus 5.572.2%
    Source
    Clef-Omni—

    Not directly comparable

  • Toolathlon Verified avg. turns

    Claude Opus 5.526.9 turns
    Source
    Clef-Omni—

    Not directly comparable

  • CWE-bench v1

    Claude Opus 5.567.0%
    Source
    Clef-Omni—

    Not directly comparable

  • Hermes Index

    Claude Opus 5.563.3%
    Source
    Clef-Omni—

    Not directly comparable

  • Hermes Bench

    Claude Opus 5.576.7%
    Source
    Clef-Omni—

    Not directly comparable

  • MazeBench

    Claude Opus 5.513%
    Source
    Clef-Omni—

    Not directly comparable

Coding

  • FrontierCode 1.1 Main

    Claude Opus 5.554.4%
    Source
    Clef-Omni—

    Not directly comparable

  • CursorBench 4.0

    Claude Opus 5.557.8%
    Source
    Clef-Omni—

    Not directly comparable

  • SWE-bench Pro

    Claude Opus 5.589.9%
    Source
    Clef-Omni—

    Not directly comparable

  • SWE Multilingual

    Claude Opus 5.593.9%
    Source
    Clef-Omni—

    Not directly comparable

  • SWE Multimodal

    Claude Opus 5.561.4%
    Source
    Clef-Omni—

    Not directly comparable

  • DeepSWE

    Claude Opus 5.574.2%
    Source
    Clef-Omni—

    Not directly comparable

  • FrontierCode 1.1 Extended

    Claude Opus 5.563.6%
    Source
    Clef-Omni—

    Not directly comparable

  • FrontierSWE v2

    Claude Opus 5.562.3%
    Source
    Clef-Omni—

    Not directly comparable

  • ProgramBench

    Claude Opus 5.591.2%
    Source
    Clef-Omni—

    Not directly comparable

  • PostTrainBench v1.1

    Claude Opus 5.549.3%
    Source
    Clef-Omni—

    Not directly comparable

  • Bug Hunt Bench

    Claude Opus 5.541.7 fixes
    Source
    Clef-Omni—

    Not directly comparable

Reasoning

  • STEMBench

    Claude Opus 5.52.62
    Source
    Clef-Omni—

    Not directly comparable

  • ARC-AGI-1

    Claude Opus 5.597.50%
    Source
    Clef-Omni—

    Not directly comparable

  • ARC-AGI-2

    Claude Opus 5.591.7%
    Source
    Clef-Omni—

    Not directly comparable

  • GraphWalks BFS 256K–1M

    Claude Opus 5.566.8%
    Source
    Clef-Omni—

    Not directly comparable

Multimodal

  • ExtractBench Pro

    Claude Opus 5.551%
    Source
    Clef-Omni—

    Not directly comparable

  • ExtractBench (LlamaIndex)

    Claude Opus 5.586.06%
    Source
    Clef-Omni—

    Not directly comparable

  • ParseBench

    Claude Opus 5.579.85%
    Source
    Clef-Omni—

    Not directly comparable

  • Chartography (tools)

    Claude Opus 5.589.0%
    Source
    Clef-Omni—

    Not directly comparable

  • Chartography (no tools)

    Claude Opus 5.564.4%
    Source
    Clef-Omni—

    Not directly comparable

  • BenchCAD Vision2Code (no tools)

    Claude Opus 5.50.730
    Source
    Clef-Omni—

    Not directly comparable

  • BenchCAD Vision2Code (tools)

    Claude Opus 5.50.962
    Source
    Clef-Omni—

    Not directly comparable

  • Biomedical image analysis

    Claude Opus 5.571.4%
    Source
    Clef-Omni—

    Not directly comparable

  • OfficeQA

    Claude Opus 5.578.9%
    Source
    Clef-Omni—

    Not directly comparable

  • OfficeQA Pro

    Claude Opus 5.567.7%
    Source
    Clef-Omni—

    Not directly comparable

Knowledge

  • HLE w/o tools

    Claude Opus 5.564.4%
    Source
    Clef-Omni—

    Not directly comparable

  • HealthBench (raw)

    Claude Opus 5.568.1%
    Source
    Clef-Omni—

    Not directly comparable

  • HealthBench (length-adjusted)

    Claude Opus 5.560.6%
    Source
    Clef-Omni—

    Not directly comparable

  • HealthBench Professional

    Claude Opus 5.565.6%
    Source
    Clef-Omni—

    Not directly comparable

  • HealthBench Professional (raw)

    Claude Opus 5.577.1%
    Source
    Clef-Omni—

    Not directly comparable

  • BioMysteryBench (human-solvable)

    Claude Opus 5.589.3%
    Source
    Clef-Omni—

    Not directly comparable

  • BioMysteryBench (human-difficult)

    Claude Opus 5.550.0%
    Source
    Clef-Omni—

    Not directly comparable

  • SpatialBench Verified

    Claude Opus 5.572.0%
    Source
    Clef-Omni—

    Not directly comparable

  • SingleCellBench

    Claude Opus 5.561.2%
    Source
    Clef-Omni—

    Not directly comparable

  • Morphology-to-molecule matching

    Claude Opus 5.534.0%
    Source
    Clef-Omni—

    Not directly comparable

  • Medicinal chemistry

    Claude Opus 5.563.5%
    Source
    Clef-Omni—

    Not directly comparable

  • Protein Design

    Claude Opus 5.560.2%
    Source
    Clef-Omni—

    Not directly comparable

  • Protein Design library ranking

    Claude Opus 5.556.0%
    Source
    Clef-Omni—

    Not directly comparable

  • De novo protein-binder design

    Claude Opus 5.582.6%
    Source
    Clef-Omni—

    Not directly comparable

  • Protocols (troubleshooting)

    Claude Opus 5.573.7%
    Source
    Clef-Omni—

    Not directly comparable

  • Protocols (understanding)

    Claude Opus 5.569.0%
    Source
    Clef-Omni—

    Not directly comparable

Multilingual

  • GMMLU

    Claude Opus 5.594.3%
    Source
    Clef-Omni—

    Not directly comparable

  • MILU

    Claude Opus 5.593.1%
    Source
    Clef-Omni—

    Not directly comparable

Instruction following

  • Gray Swan IPI (15 attempts)

    Claude Opus 5.51.0%
    Source
    Clef-Omni—

    Not directly comparable

Math

  • ArXivMath Aug. 2026 (no tools)

    Claude Opus 5.591.2%
    Source
    Clef-Omni—

    Not directly comparable

  • ArXivMath Aug. 2026 (tools)

    Claude Opus 5.596.9%
    Source
    Clef-Omni—

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    Claude Opus 5.591.228%
    Source
    Clef-Omni—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Claude Opus 5.595.000%
    Source
    Clef-Omni—

    Not directly comparable

63 public results · 0 shared

Watch Claude Opus 5.5 vs Clef-Omni

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

Read a sample issue

Join 5,600+ readers.

Last updated October 20, 2026