GPT-5.4 vs GPT-4o

1. Best Intelligence at Scale

• OpenAI positions GPT-5.4 as its frontier model for agentic, coding, and professional workflows.
• Built for complex professional work where stronger reasoning and higher answer quality matter.

2. Configurable Reasoning + Multimodal Input

• Supports configurable reasoning effort from none to xhigh, letting teams balance speed and depth.
• Accepts both text and image inputs while producing text output.

3. Massive Context for Long-Running Work

• 1.05M token context window supports very large codebases, documents, and multi-step workflows.
• Allows up to 128 k output tokens for long-form answers and larger generations.

4. Updated Knowledge & Broad Tool Support

• Knowledge cut-off of Aug 31 2025 keeps it current for newer frameworks and business context.
• Supports tools like web search, file search, code interpreter, hosted shell, computer use, and MCP in the Responses API.

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.