1. High reasoning performance

• Retains strong reasoning capabilities despite being a smaller, faster model.
• Suitable for tasks requiring accurate logic and structured thinking.

2. Fast and cost-efficient

• Optimized for speed, making it ideal for real-time or high-volume workloads.
• Far cheaper than GPT-5 while maintaining solid capability.

3. Great for well-defined tasks

• Excels when prompts are precise and objectives are clearly specified.
• More predictable and stable for deterministic workflows.

4. Multimodal input

• Accepts text + image as input.
• Outputs text only.

5. Tool support

• Works with Web Search, File Search, Code Interpreter, MCP.
• (Does not support Image Generation as a tool and does not support Computer Use.)

1. Maximum-compute o-series model

• Uses significantly more compute per query compared to o1.
• Produces deeper, more reliable reasoning chains.
• Best suited for high-stakes tasks that need correctness over speed.

2. Trained with reinforcement learning for deliberate thinking

• Explicit "think-before-answer" architecture.
• Excels at complex reasoning requiring multi-step analysis.

3. Very strong at math, science, coding, and technical proofs

• Handles long derivations, algorithm design, and difficult logic problems.
• Produces structured and explainable reasoning trails.

4. Great for multi-turn reasoning workflows

• Responses API optimized: can think over multiple internal turns before responding.
• Ideal for agentic reasoning pipelines.

5. Large context window

• 200,000-token context for large documents, multi-file review, and long reasoning traces.

6. Multimodal input (text + image)

• Can analyze images for mathematical diagrams, charts, handwritten content, UI layouts, etc.
• Output is text only.

7. Consistency, reliability, and depth

• Designed for situations where accuracy matters more than latency or cost.
• Strong error-checking and self-correction abilities.