GPT-5.1 vs GPT-4o

1. Configurable Reasoning for Agentic Tasks

• Built to excel in autonomous or semi-autonomous coding workflows, with adjustable reasoning effort for planning, refactoring and debugging.

2. Fast Multi-Modal Input with Large Output

• Accepts both text and image inputs while producing text outputs.
• Offers up to 128 k output tokens, allowing long responses and code generation across multiple files.

3. Large Context & Knowledge Cut-Off

• 400 k token context window supports processing large codebases or documents.
• Knowledge cut-off of Sep 30 2024 ensures familiarity with recent tools and frameworks.

4. Reasoning Token Support

• Provides explicit support for reasoning tokens, enabling developers to fine-tune the balance between reasoning depth and speed.

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.