GPT-5.2 Codex vs GPT-4.1 Nano

1. Optimized for Long-Horizon Coding Tasks

• OpenAI describes GPT-5.2 Codex as a highly intelligent coding model built for long-horizon, agentic coding work.
• Well suited to planning, refactoring, debugging, and multi-step implementation flows inside real codebases.

2. Adjustable Reasoning for Coding Work

• Supports configurable reasoning effort from low to xhigh depending on speed and quality needs.
• Accepts both text and image inputs while producing text output.

3. Large Context + Long Output

• 400 k token context window supports broad repository understanding and larger working sets.
• Allows up to 128 k output tokens for longer patches, code generation, and technical explanations.

4. Up-to-Date Model Snapshot

• Knowledge cut-off of Aug 31 2025 keeps it current with newer tools and frameworks.
• Supports streaming, function calling, and structured outputs for tool-driven coding workflows.

1. Ultra-Fast, Low-Latency Performance

• The fastest model in the GPT-4.1 family, ideal for real-time interactions and high-throughput applications.
• Designed for scenarios where speed matters more than complex reasoning.

2. Most Cost-Efficient GPT-4.1 Variant

• Lowest price point among GPT-4.1 models.
• Enables large-scale deployments such as support bots, routing systems, and lightweight assistants without high compute costs.

3. Solid Instruction Following

• Consistent and reliable at following clear instructions.
• Well-suited for:
  • Classification
  • Simple reasoning
  • Data extraction
  • Content rewriting
  • Chat-style responses

4. Strong Tool Calling Capabilities

• Built with robust support for:
  • Function calling
  • Structured outputs (e.g., JSON)
  • Lightweight automation tasks
• Works well within multi-step agent workflows that rely on simple tools.

5. Basic Multimodal Input

• Supports text and image input.
• Useful for:
  • Simple visual recognition
  • Alt-text generation
  • Reading graphics or screenshots

6. Text-Only Output

• Produces text only, ensuring:
  • Clean structured outputs
  • High reliability for downstream processing
  • Ease of integration into backend systems

7. 1M-Token Context Window

• Supports up to 1,047,576 tokens, allowing:
  • Long documents
  • Multiple files
  • Large prompt memory
• Reduces or eliminates the need for chunking and retrieval in many simple workflows.

8. Ideal Use Cases

• Customer support bots
• Routing and intent detection
• Simple agents and workflow automation
• Content cleanup and rewriting
• Basic Q&A, summaries, and extraction

9. Broad API Integration

• Available across major API endpoints:
  • Chat Completions
  • Responses
  • Realtime
  • Assistants
  • Fine-tuning
• Supports predicted outputs for reliability and determinism.