GPT-5 Pro vs GPT-4o

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. High-intelligence, general-purpose model

• Strong reasoning, creativity, summarization, and problem-solving.
• Great balance of speed, accuracy, and cost.

2. Multimodal input support

• Accepts text + image inputs for visual reasoning, extraction, or description.
• Output is text only, making it predictable for production.

3. Excellent for structured and unstructured tasks

• Performs well on Q&A, writing, analysis, classification, chat, and planning.
• Supports Structured Outputs, making it suitable for deterministic workflows.

4. Strong tool-use capabilities

• Supports function calling, API orchestration, and tool-augmented workflows.
• Integrates well with assistants, batch operations, and automation pipelines.

5. Large context for complex tasks

• 128K context allows multi-document reasoning, multi-step conversations, and large input payloads.

6. Production-ready reliability

• Stable outputs, predictable behaviors, and broad modality coverage.
• Supported across all major API endpoints.

7. Lower latency than o-series reasoning models

• Faster responses due to no dedicated reasoning step.
• Ideal for interactive or near-real-time applications.

8. Fine-tuning and distillation supported

• Enables specialization for domain-specific tasks.
• Distillation helps create smaller, efficient custom models.