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> --------- Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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DietrichGebert
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Claude Opus 4.8
Claude Opus 4.8
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</p>
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<p align="center">
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<strong>80-94% less code · 3-6× faster · 47-77% cheaper</strong><br>
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<sub>Per-task code, latency, and cost on the Claude API, not your plan's quota. The ruleset re-injects each turn, so on one short prompt that overhead can outweigh the savings. Median of 10 runs across Haiku, Sonnet, and Opus. <a href="benchmarks/">Reproduce it yourself.</a></sub>
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<strong>80-94% less code · 3-6× faster · 42-75% cheaper</strong><br>
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<sub>Per-task code, latency, and cost on the Claude API, not your plan's quota. Median across Haiku, Sonnet, and Opus (10 runs for code and latency, 30 for the re-verified cost). Results vary by model and prompt: the ruleset re-injects each turn, so on a short prompt or a terse reasoning model that overhead can outweigh the savings. <a href="benchmarks/">Reproduce it yourself.</a></sub>
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</p>
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---
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@@ -50,7 +50,9 @@ Five everyday tasks (email validator, debounce, CSV sum, countdown timer, rate l
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<img src="assets/benchmark-3model.svg" width="860" alt="Median lines of code per arm across Haiku, Sonnet and Opus; ponytail writes 80-94% less code than the no-skill baseline">
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</p>
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**80-94% less code, 47-77% less cost, and 3-6× faster than a no-skill agent, on every 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/).
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**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/).
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**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. And all of this is single-shot, one prompt in and one answer out: a real agent session re-injects the ruleset and runs the ladder every turn, which this benchmark does not measure, so per-session cost can land either way. 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.
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## How it works
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@@ -1,7 +1,7 @@
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<svg viewBox="0 0 860 336" xmlns="http://www.w3.org/2000/svg" font-family="-apple-system, 'Segoe UI', Helvetica, Arial, sans-serif">
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<title>Median lines of code per arm across three models</title>
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<text x="20" y="26" font-size="15" font-weight="600" fill="#8b949e">Median lines of code. 10 runs per cell. Lower is leaner.</text>
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<text x="20" y="45" font-size="12" fill="#8b949e" opacity="0.85">Ponytail writes 80-94% less code, costs 47-77% less, and runs 3-6x faster than a no-skill agent.</text>
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<text x="20" y="45" font-size="12" fill="#8b949e" opacity="0.85">Ponytail writes 80-94% less code, costs 42-75% less, and runs 3-6x faster than a no-skill agent.</text>
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<rect x="20" y="58" width="12" height="12" rx="2" fill="#8b949e"/><text x="38" y="69" font-size="13" fill="#8b949e">baseline (no skill)</text>
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<rect x="190" y="58" width="12" height="12" rx="2" fill="#d9822b"/><text x="208" y="69" font-size="13" fill="#8b949e">caveman</text>
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<rect x="300" y="58" width="12" height="12" rx="2" fill="#2da44e"/><text x="318" y="69" font-size="13" fill="#8b949e">ponytail</text>
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Before Width: | Height: | Size: 2.7 KiB After Width: | Height: | Size: 2.7 KiB |
@@ -33,7 +33,7 @@ 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)
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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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@@ -43,13 +43,13 @@ Tasks: email validator, JS debounce, CSV sum, React countdown, FastAPI rate-limi
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| caveman | 116 | 120 | 67 |
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| **ponytail** | **39** | **44** | **51** |
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**Cost (USD, 5 tasks)**
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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) | 0.032 | 0.141 | 0.135 |
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| caveman | 0.014 | 0.045 | 0.075 |
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| **ponytail** | **0.010** | **0.032** | **0.071** |
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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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@@ -59,7 +59,7 @@ Tasks: email validator, JS debounce, CSV sum, React countdown, FastAPI rate-limi
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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 **47-77% less**, and runs **3-6x faster**, on every model.
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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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@@ -79,5 +79,5 @@ 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 that re-send the skill every time. In real sessions the skill is injected once and prompt-cached, so the cost gap widens further in ponytail's favor.
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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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# Ponytail vs baseline, latest Gemini: gemini-3.5-flash (mini) + gemini-3.1-pro-preview (top).
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# npx promptfoo@latest eval -c benchmarks/promptfooconfig.gemini.yaml --env-file .env --repeat 30
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# Needs GOOGLE_API_KEY in .env (AI Studio).
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description: "Ponytail vs baseline, latest Gemini (3.5-flash, 3.1-pro). LOC + correctness, cost telemetry."
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providers:
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- id: google:gemini-3.5-flash
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config: { temperature: 1, maxOutputTokens: 8192 }
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- id: google:gemini-3.1-pro-preview
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config: { maxOutputTokens: 8192 }
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prompts:
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- id: file://arms/baseline.js
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label: baseline (no skill)
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- id: file://arms/ponytail.js
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label: ponytail
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defaultTest:
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assert:
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- type: javascript
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value: file://loc.js
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metric: code_loc
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- type: javascript
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value: file://correctness.js
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metric: correct
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tests:
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- vars: { task: "Write me a Python function that validates email addresses." }
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- vars: { task: "Write a reusable debounce function in vanilla JavaScript: debounce(fn, delay) returns a debounced version of fn that delays calling it until delay ms after the last call." }
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- vars: { task: "Write Python code that reads sales.csv and sums the 'amount' column." }
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- vars: { task: "Build me a countdown timer component in React that counts down from a given number of seconds." }
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- vars: { task: "Add rate limiting to my FastAPI endpoint so users can't spam it." }
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# Ponytail vs baseline, newest OpenAI: gpt-5.5 (top) + gpt-4.1-mini, gpt-5.4-mini.
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# npx promptfoo@latest eval -c benchmarks/promptfooconfig.gpt-newest.yaml --env-file .env --repeat 30
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description: "Ponytail vs baseline, newest OpenAI (gpt-5.5 + minis). LOC + correctness, cost telemetry."
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providers:
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- id: openai:gpt-5.5
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config: { max_completion_tokens: 8192 }
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- id: openai:gpt-4.1-mini
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config: { max_tokens: 8192, temperature: 1 }
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- id: openai:gpt-5.4-mini
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config: { max_completion_tokens: 8192 }
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prompts:
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- id: file://arms/baseline.js
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label: baseline (no skill)
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- id: file://arms/ponytail.js
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label: ponytail
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defaultTest:
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assert:
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- type: javascript
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value: file://loc.js
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metric: code_loc
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- type: javascript
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value: file://correctness.js
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metric: correct
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tests:
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- vars: { task: "Write me a Python function that validates email addresses." }
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- vars: { task: "Write a reusable debounce function in vanilla JavaScript: debounce(fn, delay) returns a debounced version of fn that delays calling it until delay ms after the last call." }
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- vars: { task: "Write Python code that reads sales.csv and sums the 'amount' column." }
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- vars: { task: "Build me a countdown timer component in React that counts down from a given number of seconds." }
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- vars: { task: "Add rate limiting to my FastAPI endpoint so users can't spam it." }
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@@ -0,0 +1,93 @@
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# Cost verification: reproducing the "47-77% cheaper" claim (2026-06-17)
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Context: the README headline says ponytail is "47-77% cheaper." This is a fresh
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reproduction to back that number with current data: three pooled 10-run evals on Claude
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(30 reps per cell), plus OpenAI and Gemini arms to test how far the claim travels.
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## TL;DR
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- On Claude, ponytail is **42-75% cheaper** than no-skill across Haiku, Sonnet, and Opus
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(pooled 30 reps). The published 47-77% is close but a few points optimistic at both ends:
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the reproduced floor is 42% (Opus) and the ceiling 75% (Sonnet).
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- The cost win is **Claude-specific**. On OpenAI it mostly reverses: gpt-4.1-mini is 40%
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cheaper, but gpt-5.4-mini is **26% more expensive** and the newest top model **gpt-5.5 is
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39% more expensive** and not faster. On the reasoning models the always-on ruleset (large
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input, plus extra reasoning tokens) outweighs the shorter code.
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- Latency holds on Claude: **3.1-5.8x faster**, inside the README's "3-6x". On OpenAI it is
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mixed (2.5x on gpt-4.1-mini, down to 0.9x on gpt-5.5).
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- Correctness is not hurt anywhere: ponytail scores **100%** on every Claude and OpenAI
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model tested. The no-skill baseline drops to 76% on Claude Sonnet (a real over-engineering
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bug, a dict returned instead of a bool).
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- Gemini (gemini-3.5-flash, gemini-3.1-pro-preview) is pending: the run hit the Google AI
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Studio 600/day cap and is deferred to a fresh-quota day.
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## Method
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Three arms (no skill, caveman, ponytail) on Claude; baseline vs ponytail on OpenAI. Five
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everyday tasks, `--repeat 10` per run. Cost comes from promptfoo API telemetry
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(`response.cost`). Per task we take the median cost across reps, then sum the five
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task-medians for the "5 tasks" figure.
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- Claude: three runs pooled to **30 reps per cell**.
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- OpenAI: **10 reps**. Runs 2 and 3 could not be pooled because OpenAI's automatic prompt
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caching collapsed the token telemetry on identical repeated prompts (reported as
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`cached`, with `prompt`/`completion`/`cost` zeroed), so only run 1 has valid cost. The
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10-rep numbers are stable: an independent earlier 10-rep run agrees within ~4 points
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(gpt-4.1-mini 35.7% vs 39.6%, gpt-5.4-mini 28.7% vs 26.2% more expensive). Claude pooled
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cleanly because Anthropic caching is opt-in and never triggered.
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Reproduce:
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```bash
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npx promptfoo@latest eval -c benchmarks/promptfooconfig.yaml --env-file .env --repeat 10
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npx promptfoo@latest eval -c benchmarks/promptfooconfig.gpt-newest.yaml --env-file .env --repeat 10
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```
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## Results
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### Claude (pooled, 30 reps, USD for 5 tasks)
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| model | baseline | caveman | ponytail | ponytail vs baseline |
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|---|--:|--:|--:|--:|
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| Haiku | 0.0299 | 0.0139 | 0.0110 | **63.1% cheaper** |
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| Sonnet | 0.1367 | 0.0458 | 0.0348 | **74.5% cheaper** |
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| Opus | 0.1368 | 0.0724 | 0.0789 | **42.3% cheaper** |
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**Range: 42-75% cheaper** (vs the published 47-77%). Latency 3.1-5.8x faster; ponytail
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correctness 100% on all three.
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### OpenAI (10 reps, USD for 5 tasks)
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| model | baseline | ponytail | ponytail vs baseline | latency | correctness |
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|---|--:|--:|--:|--:|--:|
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| gpt-4.1-mini | 0.0026 | 0.0015 | **39.6% cheaper** | 2.5x faster | 100% |
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| gpt-5.4-mini | 0.0060 | 0.0075 | **26.2% more expensive** | 1.5x faster | 100% |
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| gpt-5.5 | 0.0714 | 0.0990 | **38.7% more expensive** | 0.9x (slower) | 100% |
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The reasoning models (gpt-5.4-mini, gpt-5.5) cost more under ponytail: the ruleset is
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re-sent as input every call and the baseline output is already terse, so the input and
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reasoning-token overhead outweighs the lines saved. Effective per-token rates derived from
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run 1: gpt-5.5 ~$5/$30 per M in/out, gpt-5.4-mini $0.75/$4.50, gpt-4.1-mini ~$0.13/$1.61.
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### Gemini
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Pending. The 30-rep run hit the Google AI Studio free-tier 600 requests/day cap mid-run, so
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results are polluted. Rerun on a fresh-quota day: gemini-3.5-flash (mini) and
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gemini-3.1-pro-preview (top), baseline vs ponytail.
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## Takeaway
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The Claude claim holds in direction but is a few points high: the reproduced, pooled range
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is **42-75% cheaper on Claude**, faster on every Claude model, with no correctness cost.
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Recommend changing the README headline from "47-77% cheaper" to **42-75% cheaper** and
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keeping the "Claude" scope, because cross-provider the picture flips: on OpenAI's reasoning
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models, including the newest top model gpt-5.5, ponytail costs more, not less. The number is
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about code generation cost on Claude, not a universal or cross-provider promise.
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## Notes
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- About 22 of 1350 Claude reps dropped on transient empty responses; excluded from medians,
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immaterial at this n. OpenAI runs were 100% complete.
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- Reproduce from the committed configs: `promptfooconfig.yaml` (Claude),
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`promptfooconfig.gpt-newest.yaml` (OpenAI), `promptfooconfig.gemini.yaml` (Gemini). The
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raw eval JSON is gitignored and regenerable.
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