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MODEL · ENTITY #1070

Claude Science

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EVENT TIMELINE

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MODELS · 4 SOURCES · Anthropic · TechCrunch AI · The Verge AI · The Decoder

Anthropic releases Claude Opus 5 — lower-cost model claiming near‑frontier performance

Anthropic announced Claude Opus 5, a new model positioned as a cost‑efficient successor to Opus 4.8 and the new default on Claude Max (and the strongest on Claude Pro). Anthropic says Opus 5 matches or exceeds prior models on many coding, knowledge‑work and scientific benchmarks (Frontier‑Bench, GDPval‑AA, CursorBench, ARC‑AGI, Zapier AutomationBench, OSWorld) at lower cost per task while remaining behind Mythos 5 on security and biology frontier tasks; the company also reports improved alignment and safety in pre‑deployment audits and links a System Card for more details.

8.0

RESEARCH · 1 SOURCE · Anthropic

Anthropic opens 10,000 Claude subscriptions for scientists and expands AI for Science credits

Anthropic is opening 10,000 seats to let verified academic and nonprofit labs access Claude subscriptions free or at discounted premium rates ($15/month with 5× usage) for one year, and plans to extend the program. The company is also expanding its AI for Science credits (up to $50,000 per project), limiting biology/chemistry to Opus-class models while blocking professional bio queries on Fable models, working with the US government to pilot Mythos-class access, reporting three July 30 incidents of unauthorized system access under investigation with METR, and previewing the Model Hardware Standard (MHS) to select labs and manufacturers.

8.0

MODELS · 1 SOURCE · NVIDIA Developer

Run NVIDIA BioNeMo NIM Microservices for Protein Structure Prediction in Claude Science

NVIDIA Developer published guidance on running BioNeMo NIM microservices for protein structure prediction inside Claude Science, illustrating how NVIDIA’s model microservices can be invoked within an agentic research workflow. The article frames this integration in the context of agentic AI that can read papers, propose hypotheses, call models, and prioritize experiments.

6.0