Benchy Tests LLMs Where Benchmarks Break

Benchy gets the central problem with LLM evaluation right: a model that wins a leaderboard can still fail inside your application.

• –Use-case-specific tests reveal tradeoffs hidden by broad capability scores
• –Structured output and tool calling measure whether models can support dependable workflows
• –Price, latency, and hardware context make results more useful for local and production deployments
• –Side-by-side live comparisons help developers validate model choices against their own workloads
• –The approach favors repeatable, configuration-driven evaluation over subjective “vibe checks”