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LLM ComparisonGPT-5 NanoClaude 3.5 Haiku

GPT-5 Nano vs Claude 3.5 Haiku

Compare GPT-5 Nano and Claude 3.5 Haiku. Build AI products powered by either model on Appaca.

Model Comparison

FeatureGPT-5 NanoClaude 3.5 Haiku
ProviderOpenAIAnthropic
Model Typetexttext
Context Window400,000 tokens200,000 tokens
Input Cost
$0.05/ 1M tokens
$0.80/ 1M tokens
Output Cost
$0.40/ 1M tokens
$4.00/ 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.)

Claude 3.5 Haiku

Anthropic

1. Intelligence & Benchmark Performance

  • Matches Claude 3 Opus (previous largest model) on many intelligence tasks.
  • Surpasses Claude 3 Opus on multiple evaluations despite being a smaller, faster model.
  • Major improvements across every skill category vs previous Haiku.

2. Coding Strength

  • Scores 40.6% on SWE-bench Verified, outperforming:

    • Claude 3.5 Sonnet (original version)
    • GPT-4o
    • Many agent-driven systems
  • Excellent for engineering assistants, agent coding tasks, and bug fixing.

3. Speed & Latency

  • Same speed class as Claude 3 Haiku (ultra-fast).
  • Ideal for real-time interactions, high request volumes, and UI responsiveness.

4. Tool Use & Instruction Following

  • Better at following instructions than previous Haiku.
  • Stronger at tool use accuracy, making it reliable for agents and workflows.

5. Best Use Cases

  • High-volume, low-latency tasks
  • User-facing products
  • Sub-agent tasks in larger workflows
  • Processing large structured datasets (pricing, inventory, purchase history)
  • Rapid content or code generation where speed matters