1. Configurable Reasoning for Agentic Tasks

• Built to excel in autonomous or semi-autonomous coding workflows, with adjustable reasoning effort for planning, refactoring and debugging.

2. Fast Multi-Modal Input with Large Output

• Accepts both text and image inputs while producing text outputs.
• Offers up to 128 k output tokens, allowing long responses and code generation across multiple files.

3. Large Context & Knowledge Cut-Off

• 400 k token context window supports processing large codebases or documents.
• Knowledge cut-off of Sep 30 2024 ensures familiarity with recent tools and frameworks.

4. Reasoning Token Support

• Provides explicit support for reasoning tokens, enabling developers to fine-tune the balance between reasoning depth and speed.

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.