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When OpenAI released GPT-5.4 at the start of March, the company said the new model was designed primarily for professional work like programming and data analysis. Now OpenAI is launching GPT-5.4 mini and nano, and while it is once again highlighting the usefulness of these new systems for tasks like coding, one of the new models is available to Free and Go users. What's more, that model, GPT-5.4 mini, even offers performance that approaches GPT-5.4 in a handful of areas.
As a Free or Go user, you can access 5.4 mini by selecting "Thinking" from ChatGPT's plus menu. For paid users, the model is the new fallback for when you've hit your rate limit with 5.4 proper. OpenAI says 5.4 mini offers better performance than GPT-5.0 mini in a few different key areas, including reasoning, multimodal understanding and tool use. That means 5.4 mini is better at parsing non-text inputs such as images and audio, and has a more nuanced understanding of how to do things like search the web. It does all of this while running more than twice as fast as its predecessor.
As for GPT-5.4 nano, OpenAI says it's ideal for tasks such as data classification and extraction where speed and cost-efficiency are top of mind. If you're a ChatGPT user, you won't find the new model in the chatbot. Instead, OpenAI is making it only available through its API service. The company envisions developers using more advanced models to delegate tasks to AI agents running GPT-5.4 nano, and that's reflected in the cost of the new model, which OpenAI has priced starting at $0.20 per million input tokens.
This article originally appeared on Engadget at https://www.engadget.com/ai/gpt-54-mi
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Flash floods are notoriously difficult to predict, but Google might have a novel solution. The company just revealed Groundsource, a prediction tool for flash floods that uses Gemini to source data from old news reports. This is the first time it has used a language model for this type of work.
This provides a massive,…
— Google Research (@GoogleResearch) March 12, 2026
Google tasked Gemini with sorting through 5 million news articles from around the world and isolating flood reports. It transformed this data into a geo-tagged series of chronological events. Next, researchers trained a model to ingest current weather forecasts and leverage the Groundsource data to determine the likelihood of a flash flood in a given area.
We don't have any concrete information as to how accurate Google's forecast model is, though that should come over time. One trial user did say it helped his organization respond quicker to localized weather events. For now, the company is highlighting risks for urban areas in 150 countries via its
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