Self-Driving Chemical Laboratories (SDCLs) are shifting chemical experimentation toward embodied automation, requiring machines to perceive lab scenes, reason about experimental states, and act under physical, procedural, and safety constraints. Existing laboratory benchmarks mainly address safety, anomaly detection, or general scientific understanding, leaving execution‑oriented embodied capabilities under‑explored. To fill this gap, we introduce BEAKER, a benchmark for evaluating Embodied Actionability, Knowledge, Experimentation, and Reasoning in SDCLs. BEAKER comprises 1,000 expert‑curated QA samples across 11 image and video task types, covering affordance grounding, trajectory planning, equipment, state, and safety recognition, apparatus setup, operation and step reasoning, and temporal procedure reasoning. All questions and annotations are manually created and reviewed by chemistry and embodied AI experts, without AI‑assisted generation or annotation. We evaluate 28 general‑purpose, science‑oriented, chemistry‑specialized, and embodied multimodal large language models (MLLMs) in an offline single‑turn setting. Results show that current models can follow coarse experimental procedures in videos, but struggle to ground actions into operable regions and feasible trajectories, to recognize fine‑grained laboratory apparatus, states, and safety norms, and to order steps in mid‑length procedures. BEAKER thus reveals a clear gap between coarse procedural understanding and precise, action‑grounded perception required for laboratory execution, providing a diagnostic benchmark for future MLLMs in SDCLs.\n\nReview: BEAKER raises the bar for embodied evaluation in self‑driving labs, highlighting the technical chasm between high‑level protocol comprehension and low‑level operational precision, and pointing to concrete research avenues for the next generation of laboratory AI.