Anthropic has unveiled Claude Science, a flagship AI product engineered to transform scientific research, particularly in computational biology and drug development. Positioned alongside Claude Code, this standalone offering empowers researchers to autonomously carry out complex tasks, marking a significant strategic leap for Anthropic into the life sciences domain and intensifying competition in AI for scientific discovery.

Anthropic's new AI Workbench has demonstrated its capability to map complex scientific fields for an astonishingly low cost of $26, signaling a paradigm shift in research. This groundbreaking efficiency promises to democratize scientific discovery, making advanced research accessible and affordable to a wider array of institutions and individuals. The development underscores the transformative potential of AI to accelerate innovation across every scientific discipline.

NVIDIA's BioNeMo Agent Toolkit now powers Anthropic's Claude Science, enabling researchers to accelerate drug discovery and genomic analysis with natural language workflows. This collaboration marries NVIDIA's GPU computing with Claude Science's AI agents for faster scientific innovation.

Anthropic unveils Claude Sonnet 5, a groundbreaking AI model offering enhanced agentic capabilities at lower costs. Targeting developers and businesses, the model aims to outperform competitors like GPT-5.5 and Gemini Pro while reducing operational expenses.

A new arXiv study shows OpenEvidence's specialized clinical tool beats top general‑purpose models (Claude Opus 4.8, Gemini 3.1 Pro, GPT‑5.5) on 620 real‑world point‑of‑care questions. Physicians across 30 specialties rated the specialized tool higher on accuracy, utility, source quality, verifiability and completeness.

OpenAI introduces IMCBench, a benchmark for multimodal large language models in image-grounded medical conversations, evaluating safety, accuracy, and uncertainty in diagnosis. The benchmark tests eight models, with Claude Opus 4.6 achieving the highest overall score. The results highlight the need for multi-dimensional evaluation frameworks in medical AI.