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CloudflareOpen weights model27.4B parameters

Clef benchmarks

All models
  • General rank
    Too few tasks3 non-coding tasks ranked5 needed for a rank
  • Speed#5 / 8
    756 msMedian latency, p50Hosted API only
  • Cost#10 / 10
    $3.22Per 1,000 decisionsHosted price
  • Scenario judge#1 / 31 tied
    75.7%Best task · Accuracyvs Jev-class models

Comparison summary

Strongest
Judging and evals
Leads 1 of 3 (33%)

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
27.4B
Licence
Apache-2.0

Judging and evals

Set

Share of tasks led · #3 of 31

  1. Mercury DecideLeads 2 of 3
  2. OpenAI Decisions APILeads 4 of 6
  3. Clef
    Leads 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. Rene-1 31B FP8Leads 1 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
    57.8, interval 51.6 to 64.1, tier 2
Tested against Jev and 29 Jev-class models

Scenario judge

Tied for the lead#1 / 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. Clef
    75.7, 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. Kev-27B74.3, interval 66.4 to 82.0, tied for the lead
Tested against Jev and 29 Jev-class models

Search

Tier 2 of 6#3 / 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. Clef
    60.2, interval 55.7 to 64.9, tier 2
  7. deck-31B58.9, interval 54.3 to 63.5, tier 2
  8. Decision 2.0 Vega 27B58.3, interval 53.1 to 63.7, tier 2
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 Omni330 ms
  3. Microsoft Decision 1371 ms
  4. Jev414 ms
  5. Clef
    756 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
    $3.22
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.

ClefOther modelsWhisker: 95% interval