Smarter models need less prompting" - but the data from Claude Code and Claude's published prompts says something else

Everyone heard Anthropic saying they removed 80% of the Claude Code system prompt. Thariq Shihipar's led us to believe as models get smarter, they can follow more instructions, so you'd expect the prompt layer to shrink.

But when you look at the actual traffic, the picture is different:

• Matt Pocock proxied Claude Code requests and found one message carrying 69 tool definitions, roughly 38k tokens of schemas! This prompt gets shipped on every call before you type anything!! His guide shows how to log your own requests and strip unused tools with deny rules and feature flags: https://www.aihero.dev/how-to-kill-the-bloat-in-claude-codes-system-prompt
• Databricks benchmarked Opus 4.8 through Pi at ~$0.74/task vs ~$1.94/task through the native harness, comparable quality. Pi is a lightweight harness with a much smaller prompt.
• I've been tracking Claude's published system prompts over time. Opus 4 shipped at ~1,700 words, grew to ~3,700 by Opus 4.7, and the newest snapshot is back down to ~2,900. What disappeared was mostly small behavior fixes (count letters carefully, don't over-apologize) that moved into training. What stayed: safety policy, tool discovery, memory, product context. Full breakdown with an interactive chart: https://rajivshah.com/blog/system-prompts-dont-get-shorter.html

Prompt complexity doesn't vanish as models improve, it shifts. Some of it moves into training, the rest moves into the harness, where it still costs you tokens and money every turn. If you're building agents, measure your own payloads instead of taking the "less prompting" story at face value.

Video: https://youtube.com/shorts/4qpKlx8cExI?feature=share