Inspect AI vs DeepEval in 2026: how the two open source eval frameworks differ on who runs the model, scoring, agents, sandboxes, CI and which team each one suits.
How to use MMLU benchmark runs: pick the right variant, run it with lm-evaluation-harness or Inspect, smoke test the format, and read the score honestly.
How to evaluate LLM model performance before you pick one: build a task test set, fix settings, add error bars, test prompt sensitivity, then weigh cost.
OpenAI Evals vs DeepEval in 2026: platform shutdown timeline, DeepEval's pytest CI metrics, multiprovider support, and Promptfoo as the migration path.
The best LLM evaluation frameworks in 2026: Promptfoo, DeepEval, Ragas, Inspect and TruLens for apps, plus lm-evaluation-harness and HELM for model benchmarks.
How to evaluate LLM applications step by step: error analysis, code checks, calibrated LLM judges, CI gates and production sampling, with tools for each stage.