Anthropic released Claude Haiku 5.5 today, so we ran the 5.5 family through our coding agent token usage benchmark. Haiku, Sonnet, and Opus each did 5 tasks, 3 times each, in Claude Code at its default medium effort. Haiku 5.5 spends a significant amount of time on reasoning and planning, which is typical for how smaller models increase intelligence.

30 agents minutes or 2-2.5 engineering weeks

early results on Luna vs Jev vs Decisions API on a few internal classification tasks tldr; they are very comparable on performance and speed

We ran the new models through our coding agent token usage benchmark. @OpenAI's GPT-6.1 Sol vs @AnthropicAI's Claude Sonnet 5.5 and Claude Opus 5.5. For every one of them, the biggest line item was context. Claude still does more formal planning than GPT. GPT-6.1 Sol put 58% of its cost into context and only 3% into planning. It finished all 5 tasks for $0.45. Sonnet 5.5 and Opus 5.5 spent 40% and 38% on context and about a quarter on planning. Opus cost $1.58 (~3.5x Sol). Sonnet cost $0.72 (~1.6x Sol).

new @conductor_build loadout

@linear please tell me labelling and assignments are going to get better with Jev

me every time a new frontier model drops

never tell an agent to "be thorough" we found a "be thorough" in an internal agent system prompt, removing it decreased spend by 67% with no significant change to quality 🤯