GPT-5.1 Codex vs GPT-4.1 Nano

Compare GPT-5.1 Codex and GPT-4.1 Nano. Find out which one is better for your use case.

Model Comparison

FeatureGPT-5.1 CodexGPT-4.1 Nano
ProviderOpenAIOpenAI
Model Typetexttext
Context Window400,000 tokens1,047,576 tokens
Input Cost$1.25 / 1M tokens$0.10 / 1M tokens
Output Cost$10.00 / 1M tokens$0.40 / 1M tokens

Strengths & Best Use Cases

GPT-5.1 Codex

1. Purpose-Built for Agentic Coding

  • Designed specifically for environments where the model acts as an autonomous or semi-autonomous coding agent.
  • Optimized for multi-step reasoning in code tasks such as planning, refactoring, debugging, file generation, and tool coordination.

2. Enhanced Coding Intelligence

  • Extends GPT-5.1's advanced reasoning capabilities to handle complex software architecture decisions.
  • Better accuracy in code generation across languages (JavaScript, Python, TypeScript, Go, Rust, etc.).
  • Produces cleaner, more idiomatic code aligned with modern frameworks and best practices.

3. Superior Tool Use & Code Navigation

  • Excels at reading, understanding, and transforming multi-file codebases.
  • Works well with Codex workflows that simulate real developer tooling.
  • Strong at following function signatures, constraints, and code patterns within an existing project.

4. Long-Range Context Awareness

  • 400,000-token context window enables the model to ingest large repositories or multiple files simultaneously.
  • Supports deep analysis of project structures, dependencies, and cross-file logic.

5. Multi-Modal Development Capabilities

  • Accepts text + image input and output — suitable for tasks like:
    • Reading UI mockups or screenshots to generate code
    • Understanding architectural diagrams
    • Reviewing images of whiteboard sessions

6. Agentic Workflow Optimization

  • Built to manage longer chains of thought and execution typically required in:
    • Automated code repair
    • Project bootstrapping
    • Linting and migration tasks
    • Long-running coding agents using planning + execution loops

7. Continually Updated Model Snapshot

  • Codex-specific version receives regular upgrades behind the scenes.
  • Ensures the latest coding improvements without requiring developers to update model names.

8. Reliable Instruction Following

  • Highly consistent in honoring explicit constraints:
    • Code styles
    • Folder structures
    • API contracts
    • Framework conventions

9. Broad API Support

  • Works across Chat Completions, Responses API, Realtime, Assistants, and more.
  • Ideal for apps that need live, reasoning-heavy coding agents or generative dev environments.

GPT-4.1 Nano

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.

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Founder & CEO, Edubuddy