Files
ponytail/benchmarks/results
DietrichGebertandClaude Opus 4.8 1b4914159e docs: correct cost claim to 42-75% from 30-rep re-verification (#129)
* docs: correct cost claim to 42-75% from 30-rep re-verification

Re-ran the cost benchmark at 30 reps per cell on Claude (Haiku/Sonnet/Opus):
ponytail is 42-75% cheaper than no-skill, not the previously published 47-77%.
The direction holds, both ends came in a few points lower. Updates the README
headline and body, the benchmark chart subtitle, and the benchmarks/README cost
table, and adds a dated results doc with full method.

Also adds the OpenAI (gpt-4.1-mini/gpt-5.4-mini/gpt-5.5) and Gemini configs. On
OpenAI reasoning models ponytail costs more, not less, so the claim stays
Claude-scoped. Gemini run pending a fresh-quota day.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* docs: scope the body claim to Claude models

"on every model" read as cross-provider, but the 30-rep verification shows
the cost win reverses on OpenAI reasoning models. Match the caption and
benchmarks/README, which already say Claude.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* docs: reframe the pitch as the discipline, not token savings

The cost/code/latency numbers vary by model and on some (terse reasoning
models like GPT-5.5) ponytail costs more, so leading with them as a universal
win was misleading. Adds model-variance to the headline caption and a paragraph
making the stated point the mental model: write only what the task needs,
safety kept, maintainable code. Savings are a model-dependent side effect.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* 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) <noreply@anthropic.com>

* docs: state the single-shot limitation honestly

The benchmark is single-shot (one prompt, one completion); it does not measure
a real multi-turn agent session, where the ruleset re-injects and the ladder
deliberates every turn. Adds that caveat to the README, and corrects the
benchmarks/README note that claimed caching widens the gap "in ponytail's
favor" (unverified, and a measured agentic A/B in #121 found the opposite can
happen). Per-session cost can land either way.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* docs: fix run count in caption (cost is 30 runs, not 10)

Cost was re-verified at 30 reps; code and latency are still the original 10.
The headline caption said "10 runs" across the board, which undersold the cost
verification. Now states the split, matching benchmarks/README.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

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Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-06-17 04:23:51 +02:00
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