Large Language Models (the ones powering ChatGPT, Claude, Gemini) are trained on text. They predict the next word in a sentence—great for writing, summarizing, and reasoning over language.
Large Quantitative Models are trained on numerical data and scientific equations from real-world lab experiments. They predict things like how strongly a molecule binds to a catalyst surface, or which drug candidates are worth testing in the lab, making them useful for non-text use cases.
LQMs are already integrated with Claude, and now they’re coming to Gemini. The first two models hitting Google Cloud Marketplace are AQCat (Q3 2026, for materials and catalyst discovery: think batteries, semiconductors, sustainable fuels) and AQPotency (later this year, for drug discovery). Researchers can prompt in plain English and have Gemini hand off the actual scientific calculations to a physics-grounded model underneath. The pattern is basically: LLMs as the interface, LQMs as the engine.
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