标签 · Claude — Glean

Recent picks

4picks · chronological

09-29

Claude Sonnet 5.5 launches with 70.6% on Terminal-Bench 4.0

Anthropic announced Claude Sonnet 5.5, the second model in the Claude 5.5 family, positioned as a faster and cheaper complement to Opus 5.5. It generates output 30%+ faster and costs up to 30% less per task than Sonnet 5. On Terminal-Bench 4.0 it scores 70.6% versus Sonnet 5's 10.3%; gains also show on FrontierCode, CursorBench, GDPval-AA and OSWorld 2.1, with some results near Opus 5.5. Token pricing is unchanged at $2 input / $10 output per million, with $0.20 cache reads, but fewer tokens are needed per task. It is the first Sonnet released with cybersecurity safeguards that fall back to Sonnet 5 for high-risk requests, plus anti-distillation classifiers and preserved thinking. Relevant to engineers choosing models and budgeting agentic coding.

www.anthropic.com · 10 min · Agentic Coding · Ai Safety · Anthropic

09-23

Claude Opus 5.5: 40% cheaper, best alignment scores

Anthropic released Claude Opus 5.5, the first model in its new Claude 5.5 family, claiming Fable 5.1-level performance at 40% lower serving cost than Opus 5. Pricing drops to $4 per million input tokens and $20 per million output tokens, with cache reads at $0.20 (60% lower), and output generation is more than 30% faster. Anthropic reports its best automated behavioral audit scores to date across nearly 2,000 simulated scenarios, fewer attempts to cross containment boundaries, and stronger prompt-injection resistance. Because biology and cybersecurity capabilities approach Fable 5.1, the model ships with comparable safeguards: most cybersecurity tasks route to Opus 4.8, vetted labs can apply to a Life Sciences Verification Program, and preserved thinking blocks API context edits used for distillation. Benchmark tables carry standard errors and third-party caveats, and Anthropic concedes that score gaps no longer reliably predict real-world differences.

www.anthropic.com · 23 min · Ai Safety · Anthropic · Claude

09-20

Balyasny: evaluating and governing frontier models at $38B scale

An interview with Balyasny Asset Management's chief AI officer on how a $38B multi-strategy firm puts frontier models into production. BAM evaluates new models on thousands of real financial tasks with verifiable outcomes — equities, macro, commodities — both standalone and inside its own agentic environment using the same tools and files its users have, watching for numerical errors, missed coverage and retrieval failures. On the relevant subset Claude Fable 5 scored 89.4% versus 86.1% for the prior production model; a set of economics problems no model had ever solved finally passed, and BAM re-ran and independently re-checked the eval before accepting the result. Merger-arbitrage packages dropped from three-to-five days to under one, with a roughly 30-minute agent run and mandatory human review. Governance is framed as controls around the model — data boundaries, least privilege, tool-level permissions, logging, human approval — not as model selection. Note this is vendor-published customer material.

claude.com · 8 min · Agent Evaluation · Agent Harness · Agent Infrastructure

07-08

12-step guide to persistent memory for Claude agents

A practical 12-step walkthrough for giving Claude agents persistent memory across sessions. Covers four layers: built-in Chat Memory, Project instructions, a lean memory file (CLAUDE.md), and Dreaming – a scheduled background process that consolidates and reorganized memory. Includes setup steps, API calls, and advice on filtering what to remember. Harvey reported ~6x task-completion rate improvement with Dreaming. Ideal for engineers building long-running agents.

x.com · 12 min · Agent Engineering · Ai-Memory · Claude