DietrichGebertandClaude Opus 4.8
1b4914159e
* 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> --------- Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
84 lines
4.0 KiB
Markdown
84 lines
4.0 KiB
Markdown
# Benchmark
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Three arms (no skill, [caveman](https://github.com/JuliusBrussee/caveman), ponytail), three models, five everyday tasks, **10 runs per cell, median reported**. Code LOC is counted from fenced code blocks; tokens, cost, and latency come straight from the API.
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## Reproduce
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### Claude (Haiku / Sonnet / Opus)
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Requires an Anthropic API key and **Node.js ≥ 22.22.0** (promptfoo's engine constraint —
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check with `node --version` and upgrade if needed):
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```bash
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cp ../.env.example ../.env # add your ANTHROPIC_API_KEY
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npx promptfoo@latest eval -c promptfooconfig.yaml --env-file ../.env --repeat 10
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npx promptfoo@latest view
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```
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`--env-file ../.env` is required because promptfoo reads `.env` from the current
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directory (`benchmarks/`), not the repo root where the file lives.
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### Local models via Ollama
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No API key or promptfoo required. Runs against any model served by Ollama:
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```bash
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ollama pull llama3.2 # or any other model
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python benchmarks/benchmark-local.py --model llama3.2 --repeat 3
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```
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See `benchmarks/results/2026-06-15-llama3.2-local.md` for what to expect: the skill works
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well on instruction-following models (Claude-class) but transfers poorly to small local
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models where the multi-step decision ladder isn't reliably followed.
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Tasks: email validator, JS debounce, CSV sum, React countdown, FastAPI rate-limit (see `promptfooconfig.yaml`). Single-shot completions, default temperature.
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## Median results (10 runs, 2026-06-13; cost re-verified at 30 runs, 2026-06-17)
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**Code (lines)**
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| arm | Haiku | Sonnet | Opus |
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|---|--:|--:|--:|
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| baseline (no skill) | 518 | 693 | 256 |
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| caveman | 116 | 120 | 67 |
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| **ponytail** | **39** | **44** | **51** |
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**Cost (USD, 5 tasks; 30 runs, 2026-06-17)**
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| arm | Haiku | Sonnet | Opus |
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|---|--:|--:|--:|
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| baseline (no skill) | 0.030 | 0.137 | 0.137 |
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| caveman | 0.014 | 0.046 | 0.072 |
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| **ponytail** | **0.011** | **0.035** | **0.079** |
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**Latency (seconds, 5 tasks)**
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| arm | Haiku | Sonnet | Opus |
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|---|--:|--:|--:|
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| baseline (no skill) | 37.7 | 124.1 | 58.7 |
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| caveman | 14.9 | 34.7 | 23.1 |
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| **ponytail** | **9.9** | **20.1** | **18.0** |
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Versus baseline, ponytail writes **80-94% less code**, costs **42-75% less**, and runs **3-6x faster**, on every Claude model. Cost re-verified at 30 reps, with OpenAI and Gemini arms, in [results/2026-06-17-cost-verification.md](results/2026-06-17-cost-verification.md).
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## Metrics
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| File | Metric | Behavior |
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|------|--------|----------|
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| `loc.js` | `loc` | Measurement - always passes, records line count |
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| `correctness.js` | `correct` | Gate - fails if generated code doesn't work |
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`correctness.js` extracts fenced code blocks and runs per-task checks (spawns Python/Node for email, debounce, CSV; structural regex for React and FastAPI). A broken one-liner that scores great on LOC will fail on correctness.
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> **Note:** The React countdown and FastAPI rate-limit checks are keyword/structural only (no runtime execution), so they verify plausible structure rather than full correctness. The email, debounce, and CSV checks execute the code.
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### Prerequisites
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Running the benchmark requires **Python 3**, **pandas**, and **Node.js** (18+).
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## Notes
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- Caveman is a prose-compression skill (it leaves code "normal"), so it lands between baseline and ponytail on code size and wins mainly on prose tokens.
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- Cost reflects single-shot calls (one prompt, one completion), not real multi-turn agent sessions. In a session the ruleset re-injects and the ladder deliberates every turn across many turns, so per-session cost can come out higher or lower than these numbers. Prompt caching offsets some of the re-injection, but a measured agentic A/B ([#121](https://github.com/DietrichGebert/ponytail/issues/121)) found ponytail can also raise tool calls and cost on completion-forced tasks. Treat these as generation numbers, not a session-cost promise.
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- These are everyday tasks. For production-grade specs, where an unconstrained agent bloats much harder, see the writeups in `results/`.
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