Colin Eberhardt
70df716a02
The reproduction steps involve running promptfoo from the benchmarks folder. In order for the environment var9ables in `.env` to be discoverable they need to be in this folder, not the project root.
93 lines
4.7 KiB
Markdown
93 lines
4.7 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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> **Read this number honestly (updated 2026-06-18).** The gap above is single-shot, against a bare
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> model that answers with several options plus commentary, so it counts prose, not just code, and
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> overstates the win. [#126](https://github.com/DietrichGebert/ponytail/issues/126) was right about
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> that. The [agentic benchmark](agentic/) re-runs the comparison as a *real Claude Code session on a
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> real public repo*: ponytail cuts **60-94%** on features with an over-build trap (custom component
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> vs native input), is a wash on already-minimal code, never writes more, and stays **100% safe**
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> while the bare "one-liner" prompt drops a guard. That is the honest, defensible number. See
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> [results/2026-06-18-agentic.md](results/2026-06-18-agentic.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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