This next-generation 8B parameter medical language model preserves the deployment-friendly footprint of our earlier 7B release while introducing a dedicated reasoning mode that can follow multi-step clinical logic and justify its answers. Trained on an expanded, carefully curated corpus of medical literature and reinforced with chain-of-thought supervision, it excels at differential diagnosis, guideline-aware care planning, and complex patient-note summarization. Its smaller size enables faster inference and reduced computational costs, making it ideal for organizations seeking to balance performance with resource optimization. Perfect for high-throughput environments requiring quick responses, this model maintains high accuracy in core medical tasks while consuming significantly less computing power than larger variants. Like its siblings, it's optimized for Retrieval-Augmented Generation (RAG), seamlessly integrating with healthcare databases and EHR systems. Choose this model when rapid response times and cost-effectiveness are priorities, without compromising on essential medical comprehension capabilities. Accuracy Outperforms Med-PaLM-1 in clinical reasoning (86.81% vs 83.8%) Achieves 75.30% average across OpenMed benchmarks, comparable to larger models Superior performance in PubMedQA (76.6%) vs similar-sized models Matches GPT-4's accuracy in medical QA tasks while being 100x smaller Ideal for cost-efficient clinical deployments with fast inference