Inspect-eval is an open-source AI evaluation framework developed by the UK AI Safety Institute, built specifically for testing and comparing large language models (LLMs). It allows users to assess multiple models side-by-side using a unified structure, evaluating key abilities such as reasoning, factual knowledge, and instruction following.

Three core features define the platform: multi-model comparison (e.g., GPT-4, Claude, Gemini), flexible scoring options including accuracy, pattern matching, and model-based grading, and a modular architecture that supports a range of sources from APIs (OpenAI, Anthropic) to local models and custom datasets. It also provides a user-friendly interface and a VS Code extension to facilitate development and visualization.

Beyond standard tasks like coding or logic puzzles, Inspect supports behavioral testing, multi-turn dialogue evaluation, and even multimodal input analysis. Researchers can easily extend the system by defining custom scorers or loading new datasets in formats like CSV, JSON, or Hugging Face’s data.

In academic research, Inspect is particularly useful for studying model fairness and bias. Scholars can create task-specific datasets to probe how models respond across demographic, cultural, or ethical dimensions. The platform currently integrates multiple benchmark datasets—such as ARC, MMLU, and GSM8K—and offers clear templates in JSONL or CSV format to help researchers build custom evaluations.Its evaluation logic is structured into three components: Dataset → Solver → Scorer, allowing flexible configuration of multi-step prompting, intermediate reasoning, and automated scoring. This architecture supports reproducible, fine-grained analysis across different model types.

As a community-driven, open-source toolkit, Inspect-eval offers a robust and extensible foundation for conducting in-depth model comparisons and advancing trustworthy AI research.