GPT-5 vs GPT-4o

1. High reasoning capability

• Designed for intelligent reasoning across complex domains.
• Supports reasoning tokens and adjustable reasoning effort.

2. Strong coding and agentic performance

• Optimized for multi-step coding tasks, tool-use chains, and agent workflows.
• Handles complex logic, planning, and structured problem solving reliably.

3. Multimodal input

• Accepts text + image as input.
• Produces text outputs with strong instruction following.

4. Extensive tool support

• Works with Web Search, File Search, Image Generation (as a tool), Code Interpreter, MCP, and more.
• Integrated across Chat Completions, Responses API, Realtime, Assistants, Batch, Embeddings, etc.

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.