GPT-5 Pro vs o1

1. Highest reasoning quality in the GPT-5 family

• Uses significantly more compute to "think harder" before responding.
• Designed for the toughest reasoning tasks where answer quality matters more than speed.
• Produces more precise, reliable, and detailed outputs than standard GPT-5.

2. Advanced multi-turn reasoning via Responses API

• Available only in the Responses API to support:
  • Multi-turn internal model interactions before returning a reply.
  • Advanced control patterns (e.g., background mode for long-running jobs).
• Ideal for complex workflows, deep planning, and multi-step analysis.

3. Configured for maximum effort by default

• Always runs with reasoning.effort: 'high' (no lower-effort mode).
• Prioritizes depth and correctness over latency and cost.

4. Multimodal input

• Accepts text + image as input.
• Outputs text, with strong instruction-following and analysis capabilities.

5. Tooling and ecosystem integration

• Supports Web Search, File Search, and Image Generation (as tools).
• Supports MCP and other Responses API tooling patterns.
• Does not support Code Interpreter and does not support Computer Use, keeping focus on pure reasoning + tools.

1. Full-scale reasoning model

• Uses reinforcement learning to generate long internal chains of thought.
• Suitable for tasks requiring deep logic, multi-step planning, and rich analytical reasoning.

2. Strong performance across domains

• Excellent at math, science, coding, and structured analytical work.
• Handles multi-step workflows and complex problem-solving with high consistency.

3. High output capacity (100K tokens)

• Enables long, detailed explanations, large documents, and multi-part analyses.

4. Image-understanding capable

• Accepts text + image inputs for visual reasoning and mixed-modality tasks.
• Output is text only, optimized for clear explanations.

5. Advanced API compatibility

• Works with Chat Completions, Responses, Realtime, Assistants, and more.
• Supports streaming, function calling, and structured outputs.

6. Stable long-context performance

• 200K-token context window supports large files, multi-document analysis, and extended conversations.

7. Designed for correctness-oriented workloads

• Prioritizes rigorous reasoning over speed.
• Useful in auditing, verification, scientific thinking, policy analysis, and legal-style reasoning.

8. Powerful but expensive

• High token costs make it suitable for selective, mission-critical reasoning rather than high-volume usage.