Seismic Impact (30%)
9.0/10
How newsworthy is this in AI?
Ecosystem Relevance (70%)
9.0/10
How useful for your apps?
Anthropic say of Sonnet 5 that "its performance is close to that of Opus 4.8, but at lower prices". The system card helps explain how they were able to release the model without being blocked by the US government:
Sonnet 5 is significantly less capable at cyber tasks than Mythos 5: its safeguards are thus similar to those we apply to Opus 4.7 and Opus 4.8 (models that are more capable than Sonnet 5 but much less capable than Mythos 5).
Of note from the "what's new" API changes:
temperature, top_p, top_k are no longer supported."thinking": {type: "disabled"}.I used my Claude Token Counter tool to try out the new tokenizer. Here are my results for several larger documents:
Document
Sonnet 4.6
Opus 4.7
Sonnet 5
Universal Declaration of Human Rights (English)
2,356
3,347
1.42x
3,341
1.42x
Universal Declaration of Human Rights (Spanish)
3,572
4,753
1.33x
4,747
1.33x
Universal Declaration of Human Rights (Chinese, Mandarin Simplified)
3,334
3,366
1.01x
3,360
1.01x
sqlite_utils/db.py (4,279 lines of Python)
44,014
56,118
1.28x
56,113
1.27x
So the new token is roughly 1.4x times more expensive for English, 1.33x for Spanish, 1.28x for Python code and effectively the same cost for Simplified Mandarin.
Here's the pelican. It's nothing to write home about. Sonnet 5 thinks it looks like a goose.

Via Hacker News
Tags: ai, generative-ai, llms, anthropic, claude, llm-pricing, pelican-riding-a-bicycle, llm-release
Claude Sonnet 5's near-Opus-4.8 performance at lower prices is directly relevant to the orchestrator and specialized agents (rails-expert, test-engineer, investigator) — this could be a strong upgrade path, especially for cost-sensitive agent loops running many tasks. However, the 30% tokenizer inflation and removal of sampling parameters (temperature, top_p, top_k) are breaking changes that require testing before upgrading ai_tracker, task_tracker, or any app that tunes generation behavior. The 1M token context window and 128K output tokens could unlock new patterns like ingesting entire Rails app codebases for refactoring tasks or generating large test suites in a single pass.