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CloudflareOpen weights model30B parameters

Clef Omni benchmarks

All models
  • General rank
    Too few tasks3 non-coding tasks ranked5 needed for a rank
  • Speed#2 / 8
    330 msMedian latency, p50Hosted API only
  • Cost#9 / 10
    $2.03Per 1,000 decisionsHosted price
  • LLM-as-a-judge#2 / 31 tied
    57.0%Best task · Accuracyvs Jev-class models

Comparison summary

Strongest
Judging and evals
Leads 0 of 3 (0%)

A typed decision model: it answers a typed question, such as yes or no or one of a few options, with a probability for each answer and no text. Strongest at judging.

Specifications

Lab
Cloudflare
Weights
Open weights
Parameters
30B
Licence
Not recorded

Judging and evals

Set

Share of tasks led · #18 of 31

  1. Mercury DecideLeads 2 of 3
  2. OpenAI Decisions APILeads 4 of 6
  3. ClefLeads 1 of 3
  4. deck-31BLeads 1 of 3
  5. Kev-27BLeads 1 of 3
  6. pplx-decider v1.1 27BLeads 1 of 3
  7. Quyet-1.0-LargeLeads 1 of 3
  8. Clef Omni
    Leads 0 of 3
See the 3 tasks

LLM-as-a-judge

Tier 2 of 2#2 / 31

Accuracy, % · Higher is better

  1. OpenAI Decisions API71.7, interval 66.1 to 77.2, ahead alone
  2. Clef Flash63.0, interval 56.2 to 69.8, tier 2
  3. Mercury Decide61.7, interval 55.6 to 67.9, tier 2
  4. Decision 2.0 Eos 0.8B61.3, interval 54.6 to 68.0, tier 2
  5. Decision 2.0 Nox 4B61.3, interval 55.2 to 67.7, tier 2
  6. Decision 2.0 Sol 2B61.3, interval 54.4 to 67.9, tier 2
  7. Jev61.3, interval 54.8 to 67.9, tier 2
  8. Clef Omni
    57.0, interval 50.6 to 63.9, tier 2
Tested against Jev and 29 Jev-class models

Scenario judge

Tier 2 of 4#11 / 31

Accuracy, % · Higher is better

  1. Mercury Decide83.3, interval 75.5 to 90.3, tied for the lead
  2. OpenAI Decisions API83.3, interval 76.1 to 89.9, tied for the lead
  3. deck-31B81.9, interval 74.8 to 88.1, tied for the lead
  4. Quyet-1.0-Large79.2, interval 72.0 to 85.9, tied for the lead
  5. pplx-decider v1.1 27B77.8, interval 69.8 to 84.9, tied for the lead
  6. Clef75.7, interval 67.4 to 83.1, tied for the lead
  7. Rene-1 31B FP875.0, interval 66.9 to 82.9, tied for the lead
  8. Clef Omni
    63.9, interval 55.2 to 72.2, tier 2
Tested against Jev and 29 Jev-class models

Search

Tier 4 of 6#18 / 31

Accuracy, % · Higher is better

  1. OpenAI Decisions API70.4, interval 66.0 to 74.8, tied for the lead
  2. Mercury Decide66.3, interval 61.5 to 71.0, tied for the lead
  3. GLiDE63.9, interval 59.5 to 68.3, tier 2
  4. Jev63.3, interval 58.2 to 68.1, tier 2
  5. Kev-27B61.1, interval 56.4 to 65.7, tier 2
  6. Clef60.2, interval 55.7 to 64.9, tier 2
  7. deck-31B58.9, interval 54.3 to 63.5, tier 2
  8. Clef Omni
    39.4, interval 33.7 to 45.0, tier 4
Tested against Jev and 29 Jev-class models

Speed

Median latency, p50 · Hosted API only · lower is better

  1. OpenAI Decisions API227 ms
  2. Clef Omni
    330 ms
  3. Microsoft Decision 1371 ms
  4. Jev414 ms
  5. Clef756 ms
  6. Mercury Decide816 ms
  7. GLiDE860 ms
  8. Clef Flash1,007 ms

Cost

Per 1,000 decisions, hosted price · lower is better

  1. pplx-decider v1.1 27B$0.081
  2. AutoJev-27B$0.16
  3. Microsoft Decision 1$0.26
  4. Clef Flash$0.31
  5. Jev$0.43
  6. Mercury Decide$0.52
  7. OpenAI Decisions API$1.23
  8. Clef Omni
    $2.03
How we count

A model leads a task when it is in the task's leading tie tier. Each task counts inside its own benchmark, against that benchmark's models; no score is averaged. An area chart shows the models ranked on at least 2 of the area's tasks in a benchmark this model is in too. The overall standing counts every non-coding tasks and needs 5 for a rank. Latency is compared only on one hardware tier.

Clef OmniOther modelsWhisker: 95% interval