Model comparisons, agent setups, cost, and modalities, measured on real tasks. Read the methodology and results, or rerun a study on your own data.
Models answered 175 MathTutorBench cases covering mathematical problem solving and tutoring. Deterministic scorers checked correctness, and GLM 5.3 Flash judged writing quality.
125 objectively scored caseshigher is better
GLM 5.3 judgehigher is better
Jev was compared with DeepSeek V4 Flash, GPT-5.6 Luna, GPT-4.1 Mini, and Gemma 4B on 616 answer-correctness pairs from JudgeBench and 1,086 groundedness claims from LLM-AggreFact. Each judgment was scored against an objectively verified or human-authored reference label.
1,086 claimshigher is better
616 independent pairshigher is better
Kimi K3 ran through Moonshot and Fireworks in 30 direct latency requests per provider and 60 planned Figma-to-HTML runs per provider. Paired intervals compare matched designs.
55% faster
Each skill packages best practices from leading AI teams and researchers into a plain SKILL.md file your coding agent can read. Use them to get started running evals on your data.
Ask it to do what the skill describes, like validating a scorer against human labels or finding hidden failure modes.