{"id":4948,"date":"2025-04-16T16:55:21","date_gmt":"2025-04-16T23:55:21","guid":{"rendered":"https:\/\/www.visla.us\/blog\/?p=4948"},"modified":"2025-04-16T16:55:39","modified_gmt":"2025-04-16T23:55:39","slug":"openai-o3-and-o4-mini-openais-new-models-explained","status":"publish","type":"post","link":"https:\/\/www.visla.us\/blog\/news\/openai-o3-and-o4-mini-openais-new-models-explained\/","title":{"rendered":"OpenAI o3 and o4-mini: OpenAI’s new models, explained"},"content":{"rendered":"\n

If you’ve been keeping an eye on the world of AI, you know things move fast. But today marked one of those major milestones that gets the whole tech world buzzing. OpenAI just launched o3<\/strong> and o4-mini<\/strong>. Two models that are not just smart, but seriously strategic. Think of them as your brainy co-workers who not only know the answer, but can explain it, research it, analyze the data, and brainstorm next steps. <\/p>\n\n\n\n

They\u2019re trained to reason, plan, and take action all in one go. And if you’re wondering how this affects your work, your industry, or your team\u2019s productivity, keep reading. These models weren\u2019t built for fun. They\u2019re built to get things done.<\/p>\n\n\n\n

o3 and o4: designed to think before the speak<\/h2>\n\n\n\n

The biggest shift in o3 and o4-mini<\/a>? They don\u2019t just guess a good answer and throw it at you. These models actually pause and think. OpenAI trained them to use multi-step reasoning<\/a><\/strong>. That means they break down your question, figure out the best way to approach it, and then walk through their logic before they respond. It\u2019s like having a colleague who writes up a mini strategy memo before replying in a Slack thread.<\/p>\n\n\n\n

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This kind of reasoning makes a big difference when you’re solving real-world problems. Whether you\u2019re debugging a tricky piece of code or analyzing market data, you want a model that doesn\u2019t just get close, you want one that gets it right.<\/p>\n\n\n\n

And the o3 model? It keeps getting better the more time you give it to think. Even when it runs at the same speed and cost as its predecessor GPT-4, o3 still wins on accuracy. That\u2019s a big deal.<\/p>\n\n\n\n

Smart tools, smarter uses<\/h2>\n\n\n\n

One of the most exciting upgrades with these models is their autonomous tool use. In older versions, you’d need to nudge the AI to browse the web, write code, or summarize a file. Now, o3 and o4-mini know when and how to use tools without being told.<\/p>\n\n\n\n

For example, you might ask, “What are the top three emerging market trends in renewable energy for 2025?” The model could:<\/p>\n\n\n\n

    \n
  1. Search for recent articles or reports,<\/li>\n\n\n\n
  2. Pull and clean relevant data,<\/li>\n\n\n\n
  3. Run a Python script to identify patterns or compare with past trends,<\/li>\n\n\n\n
  4. Generate a graph or chart to visualize the result,<\/li>\n\n\n\n
  5. Summarize key insights in plain language.<\/li>\n<\/ol>\n\n\n\n

    And it\u2019ll do all of this on its own, connecting steps like a mini project manager with access to an entire research team.<\/p>\n\n\n\n

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    This kind of reasoning and execution combo is what OpenAI calls a move toward an “agentic<\/a>” ChatGPT. Basically, these models don\u2019t just assist, they operate.<\/p>\n\n\n\n

    o3 and o4 see, understand, and incorporate images<\/h2>\n\n\n\n

    o3 and o4-mini are also multimodal<\/strong>, which means they\u2019re just as good with images as they are with words. You can upload a chart, screenshot, product photo\u2014even a whiteboard picture\u2014and they\u2019ll factor that visual content directly into their thinking.<\/p>\n\n\n\n

    Let\u2019s say you\u2019re working on a product launch and you snap a photo of a brainstorm on a whiteboard. The model can analyze the notes, infer the theme, highlight key ideas, and even cross-reference what\u2019s missing based on market data.<\/p>\n\n\n\n

    They don\u2019t just caption images. They reason with them. They can zoom in, rotate, crop, and pick out relevant visual details to support your goals. It\u2019s next-level.<\/p>\n\n\n\n

    More context than ever<\/h2>\n\n\n\n

    Here\u2019s another massive upgrade: context window size<\/strong>. o3 and o4-mini can handle up to 200,000 tokens. That\u2019s hundreds of pages of text. More than five novels worth.<\/p>\n\n\n\n

    This gives them the ability to digest, reference, and build on large volumes of information in a single session. Whether you\u2019re reviewing long legal contracts, analyzing multi-year financial reports, or scanning massive code repositories, these models won\u2019t miss a beat.<\/p>\n\n\n\n

    They also hold onto the flow of a conversation much better, making them ideal for customer service, collaborative brainstorming, or technical support.<\/p>\n\n\n\n

    o3 vs. o4-mini: What\u2019s the difference?<\/h2>\n\n\n\n

    While both models are powerful, OpenAI released two versions to fit different needs. Think of o3<\/strong> as the flagship powerhouse and o4-mini<\/strong> as the lean, fast performer.<\/p>\n\n\n\n

    Feature<\/th>o3<\/th>o4-mini<\/th><\/tr><\/thead>
    Power<\/strong><\/td>Maximum reasoning, ideal for complex tasks<\/td>Efficient and optimized for everyday performance<\/td><\/tr>
    Speed<\/strong><\/td>Slower but more thorough<\/td>Very fast, great for real-time apps<\/td><\/tr>
    Cost<\/strong><\/td>~$10 input \/ $40 output per million tokens<\/td>~$1.10 input \/ $4.40 output per million tokens<\/td><\/tr>
    Use Case<\/strong><\/td>Deep analysis, research, ideation, coding<\/td>Customer support, dev tools, quick analytics<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

    o4-mini might be smaller, but don\u2019t underestimate it. On many benchmarks, it comes within striking distance of o3. For example, on some coding tests, o4-mini scored 68.1%, compared to o3\u2019s 69.1%. That\u2019s a tiny difference, especially considering the big savings in compute cost.<\/p>\n\n\n\n

    What It Means for Your Business<\/h3>\n\n\n\n

    This is where things get practical. Businesses across industries can now tap into high-powered AI without needing a huge budget or tech team. Here\u2019s how we imagine teams could use these models: <\/p>\n\n\n\n