Skip to main content
Join Community

Search AI Workflow Pro

Search tools, categories, stacks, and pages

release

What's new in Claude Sonnet 5

Builders must account for the tokenizer’s 30% token increase when estimating costs, and the removal of sampling parameters limits customization of output randomness in applications.

Simon Willison··3 min readrelease
releaseWhat's new in Claude Sonnet 5
simonwillison.net

What happened

Simon Willison analyzed the developer docs for Claude Sonnet 5, released today by Anthropic. According to Willison, Anthropic claims the model's performance is close to that of Opus 4.8 but at lower prices. However, a new tokenizer produces approximately 30% more tokens for the same input compared to Sonnet 4.6, effectively negating the cost advantage. The model's safety classification is lower than Mythos 5 for cyber tasks, so it faces similar regulatory safeguards as Opus 4.7 and 4.8. Notable API changes include the removal of sampling parameters (temperature, top_p, top_k) and the default enabling of adaptive thinking, which can be disabled. Sonnet 5 supports a 1 million token context window and 128,000 maximum output tokens. Pricing matches Sonnet 4.6 at $3/million input and $15/million output, with an introductory discount to $2/$10 until August 31. For developers integrating AI into workflows, the tokenizer change is a hidden cost increase, and the loss of sampling parameters reduces fine-grained control over output randomness. Willison highlighted these practical implications using his Token Counter tool.

Key takeaways

  • Claude Sonnet 5 performance is near Opus 4.8 but priced lower than Opus 4.8, per Anthropic.
  • New tokenizer increases token count ~30% for same input, raising effective price over Sonnet 4.6.
  • Sampling parameters (temperature, top_p, top_k) are no longer supported; adaptive thinking is default.
  • Context window is 1M tokens; output limit is 128K tokens; pricing same as Sonnet 4.6 with intro discount.
  • Model is less capable at cyber tasks than Mythos 5, so safety restrictions are similar to Opus 4.7/4.8.

Why it matters

Builders must account for the tokenizer’s 30% token increase when estimating costs, and the removal of sampling parameters limits customization of output randomness in applications.

This is an original editorial digest by AI Workflow Pro. Full reporting at the source:

Read the original on Simon Willison
Share this story
Share on X

More AI news

All news →

Join the AI Workflow Pro Community

Join Free