Sonnet 5.5
Anthropic released Claude Sonnet 5.5, a faster, cheaper mid-tier model featuring new cyber safeguards and anti-distillation protections.
summary is enough The announcement covers expected benchmark and pricing updates, but the comment thread offers sharp critiques on real-world cost-to-performance relative to Opus and open-weight models.
summary
Anthropic has introduced Claude Sonnet 5.5, touting 30 percent faster execution and lower task costs compared to its predecessor. The model achieves notable jumps in agentic coding and knowledge work, performing comparably to Opus 5.5 at lower effort levels. It also marks the first time a Sonnet model includes advanced cybersecurity fallbacks and anti-distillation classifiers. Commenters debate whether its pricing justifies its use over Opus or ultra-cheap open alternatives.
key points
- Sonnet 5.5 runs 30 percent faster and costs up to 30 percent less per task than Sonnet 5, scoring 70.6 percent on Terminal-Bench 4.0.
- It is the first Sonnet model to feature mandatory cyber safeguards and anti-distillation reasoning-protection classifiers.
- Community testers note that its cost-to-performance curve at higher effort levels heavily overlaps with Opus 5.5.
who should read it
AI developers and engineers evaluating LLM pricing and agentic coding performance.
discussion mixed
consensus
Commenters question the practical positioning of Sonnet 5.5 since Opus 5.5 at lower effort settings often provides better or comparable value, while budget-conscious users emphasize competition from much cheaper Chinese models.
pushback
Technical commenters pointed out that Sonnet's higher Terminal-Bench score over Opus was largely an artifact of Opus triggering safety fallbacks more frequently.
Pelicans. Sonnet 5.5 has the same problem as Opus 5.5: on "max" thinking effort it burned through 128,000 thinking tokens (taking 15 minutes to do that) and ran out before it had produced the final SVG.
https://tools.simonwillison.net/markdown-svg-renderer?url=ht...
Here's how the thinking effort levels compare:
low 27 input, 1,623 output, thinking_tokens: 0 1.6284 Duration: 10138ms (10s)
medium 27 input, 1,796 output, thinking_tokens: 0 1.7914 cents Duration: 11266ms (11s)
high 27 input, 2,334 output, thinking_tokens: 745 2.3394 cents Duration: 17376ms (17s)
xhigh 27 input, 5,730 …
Simon Willison provides empirical test data showing that max thinking effort causes the model to burn 128000 tokens and fail on a standard SVG generation benchmark.
Generated by an LLM from the article and thread. Check the sources before relying on it.