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LLM ComparisonGPT-5 NanoGPT-4 Turbo

GPT-5 Nano vs GPT-4 Turbo

Compare GPT-5 Nano and GPT-4 Turbo. Build AI products powered by either model on Appaca.

Model Comparison

FeatureGPT-5 NanoGPT-4 Turbo
ProviderOpenAIOpenAI
Model Typetexttext
Context Window400,000 tokens128,000 tokens
Input Cost
$0.05/ 1M tokens
$10.00/ 1M tokens
Output Cost
$0.40/ 1M tokens
$30.00/ 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 Turbo

OpenAI

1. Strong reasoning for its generation

  • Next-gen version of GPT-4 designed to be cheaper and faster than the original.
  • Good for analytical tasks, structured writing, coding guidance, and multi-step reasoning.

2. Image input support

  • Accepts images and provides text-only outputs.
  • Useful for OCR, visual Q&A, document extraction, UI analysis, and design interpretation.

3. Stable performance

  • Predictable model behavior suitable for legacy systems still built on GPT-4.
  • Works reliably for established pipelines and enterprise workloads.

4. Large 128K context window

  • Handles long documents, multi-file inputs, or extended conversational sessions.
  • Allows complex prompt chaining and large instruction sets.

5. Broad endpoint compatibility

  • Works with Chat Completions, Responses API, Realtime API, Assistants, Batch, Fine-tuning, Embeddings, and more.
  • Supports streaming and function calling.

6. Good choice for cost-controlled GPT-4-class workloads

  • Although older, still useful for teams who want GPT-4-level reasoning without upgrading immediately.
  • A midpoint between legacy GPT-4 and modern GPT-4o/5.1 models.

7. Text-only output simplifies downstream use

  • Ensures deterministic outputs for applications that need reliable text generation.
  • Good for RAG, data pipelines, automation tools, and enterprise systems.

8. Recommended migration path

  • OpenAI now recommends using GPT-4o or GPT-5.1 for improved speed, cost, reasoning, and multimodal capability.
  • GPT-4 Turbo remains available for backward compatibility and stability.