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LLM ComparisonGPT-5 NanoGPT-4.1

GPT-5 Nano vs GPT-4.1

Compare GPT-5 Nano and GPT-4.1. Build AI products powered by either model on Appaca.

Model Comparison

FeatureGPT-5 NanoGPT-4.1
ProviderOpenAIOpenAI
Model Typetexttext
Context Window400,000 tokens1,047,576 tokens
Input Cost
$0.05/ 1M tokens
$2.00/ 1M tokens
Output Cost
$0.40/ 1M tokens
$8.00/ 1M tokens

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Strengths & Best Use Cases

GPT-5 Nano

OpenAI

1. Extremely fast performance

  • Fastest model in the GPT-5 family.
  • Great for real-time workflows, rapid responses, and high-throughput systems.

2. Most cost-efficient GPT-5 model

  • Lowest input and output token costs.
  • Suitable for large-scale or budget-sensitive applications.

3. Ideal for lightweight, well-scoped tasks

  • Excels at summarization, classification, text extraction, and simple logic tasks.
  • Best used when tasks are narrow and well-defined.

4. Multimodal input

  • Accepts text + image as input.
  • Outputs text only.

5. Broad tool support

  • Supports Web Search, File Search, Image Generation (as a tool), Code Interpreter, and MCP.
  • (Does not support Computer Use.)

GPT-4.1

OpenAI

1. Smartest non-reasoning model

  • Highest intelligence among models without a reasoning step.
  • Great for tasks where speed + accuracy matter without deep chain-of-thought.

2. Excellent instruction following

  • Very strong at structured tasks, formatting, and precise execution.
  • Ideal for productized workflows and deterministic outputs.

3. Reliable tool calling

  • Works smoothly with Web Search, File Search, Image Generation, and Code Interpreter.
  • Supports MCP and advanced tool-enabled API flows.

4. Large 1M-token context window

  • Allows extremely long conversations, large documents, and multi-file use cases.
  • Handles context-heavy tasks without requiring chunking.

5. Low latency (no reasoning step)

  • Faster responses than GPT-5 family when reasoning mode isn't required.
  • More predictable timing for production use.

6. Multimodal input

  • Accepts text + image.
  • Output is text only.

7. Supports fine-tuning

  • Can be fine-tuned for specialized tasks.
  • Also supports distillation for smaller custom models.

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