I got tired of picking LLMs based on vibes and leaderboards that don't reflect real workloads, so I built this. You describe a task in plain English. The tool generates a test suite for that specific task, discovers candidate models via OpenRouter, benchmarks them in parallel, and uses a Judge LLM to score every response across 5 dimensions: accuracy, hallucination, grounding, tool-calling, and clarity. Output is a ranked top 3 with average latency per model and a task-specific system prompt optimized for the winner. A few things I learned while building it: - Score and latency rarely correlate. The best model for accuracy on coding tasks was almost never the fastest. This tradeoff is completely task-dependent and impossible to see from benchmarks that don't reflect your workload. - The Judge LLM approach is surprisingly consistent but introduces positional and familiarity bias. Using one model to score others isn't perfect, but it's far more reproducible than manual eval. Open to ideas on how to reduce judge bias without blowing up the cost. - Model discovery matters more than I expected. The top performers on generic benchmarks often weren't the top performers on narrow tasks. Stack: Python, OpenRouter for model access, MIT licensed. https://github.com/gauravvij/llm-evaluator Happy to answer questions on the design decisions. |