#gpt-54-thinking

#gpt-54-thinking

GPT-Rosalind is designed to support evidence synthesis, hypothesis generation, experimental planning, and multi-step scientific workflows across biochemistry, genomics, and protein engineering.

"This launch, at its core, is about taking our existing agents SDK and making it so it's compatible with all of these sandbox providers," Karan Sharma, who works on OpenAI's product team, told TechCrunch.

"Opus 4.7 is a notable improvement on Opus 4.6 in advanced software engineering, with particular gains on the most difficult tasks," Anthropic said in a blog post.

The new model consolidates some of the capabilities that OpenAI had previously spread across separate models, bringing together the coding strengths of GPT-5.3-Codex-the company's leading programming model-improved reasoning skills, and the agentic ability for the model to navigate desktops, browsers, and software applications autonomously.

A major difference between LLMs and LTMs is the type of data they're able to synthesize and use. LLMs use unstructured data-think text, social media posts, emails, etc. LTMs, on the other hand, can extract information or insights from structured data, which could be contained in tables, for instance. Since many enterprises rely on structured data, often contained in spreadsheets, to run their operations, LTMs could have an immediate use case for many organizations.

OpenAI's GPT-5.2 Pro does better at solving sophisticated math problems than older versions of the company's top large language model, according to a new study by Epoch AI, a non-profit research institute.