1. Strongest Agentic Coding Model

• State-of-the-art on Terminal-Bench 2.0 (82.7%), Expert-SWE (73.1%), and SWE-Bench Pro (58.6%), outperforming GPT-5.4 on complex coding tasks.
• Holds context across large systems, reasons through ambiguous failures, and carries changes through surrounding codebases with fewer tokens.

2. Higher Intelligence at GPT-5.4 Latency

• Co-designed, trained, and served on NVIDIA GB200/GB300 NVL72 systems to match GPT-5.4 per-token latency while performing at a significantly higher level.
• Uses fewer tokens to complete the same tasks, making it more efficient as well as more capable.

3. Powerful for Knowledge Work & Computer Use

• Scores 84.9% on GDPval (44 occupations) and 78.7% on OSWorld-Verified for autonomous computer operation.
• Excels at generating documents, spreadsheets, and reports; naturally moves across finding information, using tools, and checking output.

4. Scientific Research Co-Scientist

• Leading performance on GeneBench, BixBench, and FrontierMath; helped discover a new proof about Ramsey numbers verified in Lean.
• Strong enough to meaningfully accelerate progress at the frontiers of biomedical and mathematical research.

1. Fast, cost-efficient performance

• Designed for low-latency, high-throughput workloads.
• Ideal for production systems where speed and budget matter more than deep reasoning power.

2. Great for focused NLP tasks

• Excels at classification, tagging, entity extraction, rewriting, paraphrasing, and SEO tasks.
• Strong at translation and keyword generation due to efficient language understanding.

3. Multimodal input capable (text + image)

• Accepts images for lightweight visual analysis, categorization, or extraction.
• Outputs text only, ensuring deterministic and easily integrated responses.

4. Supports advanced developer features

• Structured Outputs for predictable schemas.
• Function calling for building tool-augmented agents.
• Fully compatible with Batch API for large-scale processing.

5. Easy to fine-tune

• One of the best OpenAI models for domain-specific fine-tuning.
• Allows organizations to compress larger models' behavior (like GPT-4o) into a smaller footprint.

6. Suitable for distillation workflows

• Can approximate GPT-4o or GPT-5 outputs using distillation, dramatically reducing cost.
• Enables scalable deployment for high-volume applications.

7. Large context window for its size

• 128K context supports multi-step tasks, multi-document inputs, and long-running conversations.
• Useful for agents that need memory across extended sessions.

8. Reliable for commercial production

• Stable, predictable, and low-variance outputs make it ideal for automation and enterprise stacks.
• Works well in synchronous or asynchronous pipelines.