Core o4 Features and Capabilities

The o4 model is the first in the 'mini' class to offer native multimodal reasoning and agentic autonomy. While previous versions treated images as separate inputs, o4 processes visual logic within its chain-of-thought. This results in far higher accuracy for complex STEM problems and coding tasks, all while remaining 10x more cost-effective than frontier models like o3. It effectively bridges the gap between chat and deep logic.

o4 uses an integrated multimodal approach. It can analyze UI screenshots, whiteboard sketches, or architectural diagrams directly within its reasoning process. This internal visual logic reduces errors by 35% compared to models like o1-mini. It is specifically optimized for technical visual data, making it a powerhouse for visual debugging and semi-structured data extraction from complex technical manuals or invoices.

Yes. o4 is highly optimized for agentic coding. It supports the Responses API, which allows it to maintain state during long-form engineering tasks. With a score of 85.9% on LiveCodeBench, o4 is capable of multi-step tool use, including executing Python code in sandboxed environments and recursive file analysis. It is ideal for building CLI tools or IDE extensions that require reasoning through entire project directories.

o4 is designed for cost-efficiency at $1.10 per 1M input tokens and $4.40 per 1M output tokens. To further reduce costs, GPTProto.com supports cache discounts of 50% for reused input tokens. Additionally, o4 tasks processed via the Batch API receive a 50% discount. This makes o4 roughly 10x cheaper than the full o3 model, providing reasoning capabilities that were previously too expensive for high-volume production.

Yes, o4 fully supports Structured Outputs via the response_format parameter. This is essential for agentic workflows where o4 must parse visual or text data into reliable JSON objects. By ensuring the model adheres to a specific schema, developers can use o4 for complex data extraction tasks, such as turning handwritten invoices or technical diagrams into structured data for downstream databases without formatting errors.

Migrating to o4 is straightforward. You simply need to update your model parameter to 'o4-mini'. Keep in mind that o4 offers an expanded context window of 200k tokens and adds native multimodal support. If you were using o3-mini for logic, you will likely see improved STEM performance and better instruction following. You can also begin utilizing the reasoning_effort parameter to control latency and depth in your o4 requests.