AI model benchmarks

• Jev vs Jev-class models

  Nobody clearly beats Jev yet

  1. Decision 2.0 Vega 27B75.8%±3.3
  2. Clef74.5%±3.5
  3. FGLiDE74.3%±3.5
  4. AJev-Omni73.3%±3.7
  5. EWinnow73.0%±3.5

• LLM judges

  No single model judges best

  1. Claude Opus 5.589.2%±2.9
  2. GPT-6.1 Sol87.8%±2.8
  3. Claude Sonnet 5.584.6%±3.0
  4. Gemini 3.8 Flash83.1%±2.8
  5. Jev63.7%±4.1

• PII detection

  Qwen3.8 27B leaks the least PII on average

  1. Qwen3.8 27B10.6%±1.5
  2. Ministral 3 14B15.4%±1.4
  3. Qwen3.5 9B20.0%±1.9
  4. Gemma 3 4B IT30.7%±1.9

• Jev vs frontier LLMs

  Claude Opus 5.5, Gemini 3.8 Flash and GPT-6.1 Sol tie on average

  1. Claude Opus 5.584.5%±0.7
  2. Gemini 3.8 Flash84.0%±0.7
  3. GPT-6.1 Sol83.4%±0.7
  4. Claude Sonnet 5.582.8%±0.7
  5. Jev81.0%±0.8

• Jev vs open models up to 28B

  Jev has the best average among models not trained on the tasks

  1. Jev81.9%±0.8
  2. CEikos-27B*81.6%±0.8
  3. CAutoJev-27B*81.2%±0.9
  4. JKev-9B*75.1%±3.1
  5. JKev-4B*69.4%±0.8

• Jev vs tiny open models

  Jev has the best average

  1. Jev79.6%±0.7
  2. JKev-0.8B*55.9%±1.2
  3. JKev-0.6B*48.3%±1.1
  4. CLaya-typed*47.0%±1.1

Each bar is a score with its 95% interval. Orange: ahead alone. Green: tied for the lead. Gray: the rest. Hatched with *: trained on the test data, so not ranked. Average: the unweighted mean of a model's task scores; tasks use different metrics, so read it as a summary, not a ranking.