From b4c3659f6950f1b67c96eaf18b905a582e267a60 Mon Sep 17 00:00:00 2001 From: Emeriko Date: Wed, 17 Jun 2026 04:17:44 +0200 Subject: [PATCH] docs: name the ladder's reasoning cost The ladder is a deliberation step: on reasoning models the agent spends thinking tokens working through the rungs before it saves any output, which together with the always-on ruleset can outweigh the shorter code. Makes the GPT-5.5 cost increase legible rather than just stating it. Co-Authored-By: Claude Opus 4.8 (1M context) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index eaed773..ec29388 100644 --- a/README.md +++ b/README.md @@ -52,7 +52,7 @@ Five everyday tasks (email validator, debounce, CSV sum, countdown timer, rate l **80-94% less code, 42-75% less cost, and 3-6× faster than a no-skill agent, on every Claude model.** Every shortcut ponytail takes is marked in the code with a `ponytail:` comment naming its upgrade path. Reproduce it yourself: `npx promptfoo eval -c benchmarks/promptfooconfig.yaml`. Method and raw numbers: [benchmarks/](benchmarks/). Production-grade tasks, where an unconstrained agent bloats far more, are written up in [benchmarks/results/](benchmarks/results/). -**That is the byproduct, not the pitch.** These are Claude numbers, and they vary by model. Capable instruction-following models follow the ladder and write far less, cheaper and faster; terse reasoning models can go the other way, on GPT-5.5 the always-on ruleset costs more than it saves. The rule was never "fewest tokens." It is: write only what the task needs, and never cut validation, error handling, security, or accessibility. The code ends up small because it is necessary, not golfed, and that is the part that stays maintainable. Lower cost and latency are a side effect on the models that follow it. +**That is the byproduct, not the pitch.** These are Claude numbers, and they vary by model. Capable instruction-following models follow the ladder and write far less, cheaper and faster. Terse reasoning models can go the other way: the ladder is a deliberation step, so the model spends thinking tokens working through the rungs before it saves any output, and together with the always-on ruleset that can cost more than the shorter code saves. On GPT-5.5 it does. The rule was never "fewest tokens." It is: write only what the task needs, and never cut validation, error handling, security, or accessibility. The code ends up small because it is necessary, not golfed, and that is the part that stays maintainable. Lower cost and latency are a side effect on the models that follow it. ## How it works