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OpenAI releases new simulated reasoning models with full tool access

By Advanced AI EditorApril 16, 2025No Comments2 Mins Read
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On Wednesday, OpenAI announced the release of two new models—o3 and o4-mini—that combine simulated reasoning capabilities with access to functions like web browsing and coding. These models mark the first time OpenAI’s reasoning-focused models can use every ChatGPT tool simultaneously, including visual analysis and image generation.

OpenAI announced o3 in December, and until now, only less capable derivative models named “o3-mini” and “03-mini-high” have been available. However, the new models replace their predecessors—o1 and o3-mini.

OpenAI is rolling out access today for ChatGPT Plus, Pro, and Team users, with Enterprise and Edu customers gaining access next week. Free users can try o4-mini by selecting the “Think” option before submitting queries. OpenAI CEO Sam Altman tweeted that “we expect to release o3-pro to the pro tier in a few weeks.”

For developers, both models are available starting today through the Chat Completions API and Responses API, though some organizations will need verification for access.

“These are the smartest models we’ve released to date, representing a step change in ChatGPT’s capabilities for everyone from curious users to advanced researchers,” OpenAI claimed on its website. OpenAI says the models offer better cost efficiency than their predecessors, and each comes with a different intended use case: o3 targets complex analysis, while o4-mini, being a smaller version of its next-gen SR model “o4” (not yet released), optimizes for speed and cost-efficiency.

OpenAI says o3 and o4-mini are multimodal, featuring the ability to
OpenAI says o3 and o4-mini are multimodal, featuring the ability to “think with images.”


Credit:

OpenAI

What sets these new models apart from OpenAI’s other models (like GPT-4o and GPT-4.5) is their simulated reasoning capability, which uses a simulated step-by-step “thinking” process to solve problems. Additionally, the new models dynamically determine when and how to deploy aids to solve multistep problems. For example, when asked about future energy usage in California, the models can autonomously search for utility data, write Python code to build forecasts, generate visualizing graphs, and explain key factors behind predictions—all within a single query.



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