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blog-cost-charts

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Updated 10-27 16:29

About this skill

Blog Cost/Value Charts

You help turn a ranking table (score, cost, time, per item) into two companion charts for a blog post on this Hugo site: a cost-vs-score bubble scatter, and a score/(cost×time) value-ranking bar chart. Both are produced dark-themed, one PNG per language the article ships in (normally pt and en), and get embedded right after the ranking table.

This came out of the v4 LLM benchmark article (content/2026/09/15/novo-llm-benchmark-v4-*), which needed exactly this pair of charts to make a 39-row table skimmable. The script here is the generalized version of the one-off script written for that post.

When to use

  • The article has a table with a numeric score, a cost, and a time (or any three comparable numeric axes) per row, and the user wants a visual "which one is the best deal" comparison instead of just the raw table.
  • Explicitly requested: "add graphs/charts after the table", "make this easier to compare visually", "cost-benefit chart", etc.

Workflow

1. Pick the dataset and scope

Not every row in a big table belongs in the chart. Decide, and say so in the chart's own footnote:

  • Which rows have a genuinely comparable cost figure? Exclude free, uncommitted, or flat-rate-plan entries that can't sit meaningfully on a dollar axis (a $0 point breaks a log scale; a flat-rate plan has no per-run dollar figure at all). Call these out by name in the footnote rather than silently dropping them.
  • If costs come from different harnesses/pricing bases (e.g. a real per-token API cost vs. a notional share of a flat subscription), don't hide that — color-code by group (see below) and say so in the footnote and in the article's own prose, the same way the source table already should.
  • A curated subset (15-25 points) reads far better than cramming all 39+ rows in; prefer restricting to the tier/category the article's own prose is actually discussing (e.g. "Tier A only") over trying to fit everything.

2. Build the JSON config

Copy scripts/config.example.json and edit it. Shape:

{
  "slug": "v4",                    // filename prefix for the output PNGs
  "x_log": true,                    // log-scale cost axis (usually yes: cost ranges span 100x+)
  "x_lim": [0.7, 160],               // tune to the actual data range with a little padding
  "y_lim": [84.5, 102.2],
  "x_ticks": [1, 3, 10, 30, 100],
  "x_tick_fmt": "${}",
  "group_colors": { "codex": "#5fd35f", "claude": "#e8965a", "...": "..." },
  "models": [
    {"name": "GPT-6 Astra", "score": 100.0, "cost": 30.55, "minutes": 100, "group": "codex"}
  ],
  "text": {
    "pt": { "scatter_title": "...", "scatter_subtitle": "...", "scatter_xlabel": "...",
            "scatter_ylabel": "...", "groups": {"codex": "Codex (OpenAI)"},
            "footnote": "...", "ranking_title": "...", "ranking_subtitle": "...",
            "ranking_xlabel": "...", "ranking_footnote": "..." },
    "en": { /* same keys, English copy */ }
  }
}

Every key under text.<lang> must be filled per language — there's no translation step in the script, it just renders whatever string you give it. Match the site's voice rules for both languages (PT-BR: Akita's direct, skeptical voice, no em dashes; EN: New Yorker tone, same rules) — these strings are visible chart copy, not throwaway labels.

3. Tune label offsets to avoid overlap

Bubble charts with several models at a similar cost/score WILL collide on first render. Run the script once, actually look at the output image (Read tool, not just "it ran without error"), and add a label_offsets map to the config for any name whose label overlaps another:

"label_offsets": { "GPT 5.6 sol": [14, -22], "GPT 5.6 terra": [-14, 20] }

Values are [dx, dy] in points from the bubble center. Expect 2-3 iterations of render → look → nudge before it's clean. This is the single most time-consuming part of the workflow; budget for it rather than shipping the first render.

4. Generate

python3 scripts/make_charts.py path/to/config.json --outdir /path/to/scratchpad/charts

Outputs <slug>-cost-score-<lang>.png and <slug>-value-ranking-<lang>.png per language key in text. Read each image back before moving on — check for: overlapping labels, title/subtitle collisions, a legend sitting on top of data, and that the value-ranking numbers look sane (spot-check one or two by hand: score / (cost * minutes) * 1000).

5. Upload to S3 — do not skip this

Static blog posts can't reference local files. Every PNG must go up before it's embeddable:

set -a; source ~/.config/zsh/secrets 2>/dev/null; set +a
for f in <slug>-cost-score-pt.png <slug>-cost-score-en.png <slug>-value-ranking-pt.png <slug>-value-ranking-en.png; do
  aws s3 cp "/path/to/scratchpad/charts/$f" "s3://new-uploads-akitaonrails/YYYY/MM/DD/$f" \
    --region us-east-2 --content-type image/png
done

Use the article's own publish date for the YYYY/MM/DD path, matching the convention already used for post images. After uploading, verify each URL actually resolves (don't just trust the upload exit code):

curl -s -o /dev/null -w "%{http_code}\n" \
  "https://new-uploads-akitaonrails.s3.us-east-2.amazonaws.com/YYYY/MM/DD/<file>.png"

Expect 200 for all four.

6. Embed in the article

Insert right after the ranking table's own footnotes/caveats (before moving on to narrative sections), one language-appropriate image per post, with a short lead-in sentence and a plain-language alt text describing the axes:

![Bubble chart: dollar cost on the X axis (log scale) against score on the Y axis, bubble size is wall-clock time, color is the harness](https://new-uploads-akitaonrails.s3.us-east-2.amazonaws.com/YYYY/MM/DD/<slug>-cost-score-en.png)

Do this in both the index.md (PT) and index.en.md (EN) siblings in the same turn, each pointing at its own language's PNG — never mix languages in one post's images.

7. Rebuild and verify before committing

hugo --minify
grep -o 'https://new-uploads-akitaonrails.s3[^")]*' public/<pt-path>/index.html
grep -o 'https://new-uploads-akitaonrails.s3[^")]*' public/en/<en-path>/index.html

Confirm the build has no errors and each language's page references its own PNG. Then commit both markdown files together (they're a translation pair — see the site's bilingual-editing rule) and push per the user's usual publish flow.

Design notes (why the script looks like this)

  • Dark theme, log-scale cost axis, bubble size = time: mirrors a reference chart style the user has used before (#0d1117 background, colored bubbles, muted gridlines). Cost in a benchmark context routinely spans two-plus orders of magnitude ($0.90 to $120+), so linear cost axes make cheap options indistinguishable — always log-scale it.
  • Color = group, not score: coloring by cost basis/harness (real API $ vs. notional subscription $ vs. free) visually carries the "these aren't directly comparable" caveat that the article's prose already has to state in words. Don't color by score/tier instead; that duplicates the Y axis.
  • Value index = score / (cost × time), scaled ×1000 for readable numbers: this combined ranking routinely surprises — a model with a merely-good score but very low cost AND fast wall time can out-rank the single highest-score cheap model. Call that out explicitly in the article's prose next to the chart; it's usually the actual news, not filler.
  • Never automate the "which rows to include" decision: that's an editorial judgment about the article's argument, not a data-cleaning step.