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LLM ComparisonGPT-5 NanoClaude 4.6 Sonnet

GPT-5 Nano vs Claude 4.6 Sonnet

Compare GPT-5 Nano and Claude 4.6 Sonnet. Build AI products powered by either model on Appaca.

Model Comparison

FeatureGPT-5 NanoClaude 4.6 Sonnet
ProviderOpenAIAnthropic
Model Typetexttext
Context Window400,000 tokens1,000,000 tokens
Input Cost
$0.05/ 1M tokens
$3.00/ 1M tokens
Output Cost
$0.40/ 1M tokens
$15.00/ 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 4.6 Sonnet

Anthropic

1. Most capable Sonnet model yet

  • Anthropic describes Sonnet 4.6 as its most capable Sonnet model.
  • It is a full upgrade across coding, computer use, long-context reasoning, agent planning, knowledge work, and design.

2. Stronger coding and professional task performance at Sonnet pricing

  • Pricing remains at $3/M input and $15/M output, matching Sonnet 4.5.
  • Anthropic says early-access developers strongly preferred it to Sonnet 4.5, and often even to Opus 4.5 for practical work.

3. Long-context, agent-friendly reasoning

  • Supports up to a 1M token context window in beta.
  • Anthropic reports better consistency, fewer false claims of success, fewer hallucinations, and more reliable follow-through on multi-step tasks.

4. Modern API controls for adaptive work

  • Supports adaptive thinking and the effort parameter for balancing speed, cost, and depth.
  • Gains dynamic filtering for web search and web fetch, helping agent workflows keep only relevant information in context.