Add optional cost & token-efficiency columns to the leaderboard

[lakshvantb]

What

Adds an optional cost / token-efficiency view to the leaderboard table:

• "Show Cost & Tokens" toggle → appends two columns for every model:
  • Output Tokens — avg output tokens per question (includes reasoning tokens)
  • Cost / Question — estimated USD per question
• "Only Models With Cost Data" filter → narrows the table to models that have published cost data (disabled unless the cost toggle is on).
• Both are URL-persisted (?cost=true, ?costonly=true) and reset by Clear Filters, matching the existing toggle pattern (showProvider, showReasoners, …).

How partial coverage is handled (top-models-only)

Cost/token metrics are published only for the top set of models. This is shown elegantly, not as breakage:

• Models without data render a muted "—" with a tooltip ("published for the top models only").
• "—" cells are stored as null, so they sort to the bottom regardless of direction (existing SortTable null-handling).
• Want a clean list? the Only Models With Cost Data filter hides the rest.

Data

• New optional artifact public/cost_<date>.csv (model, avg_input_tokens, avg_output_tokens, cost_per_question), merged into each row by model id so columns sort with the existing machinery.
• Absent file = no-op — other dates are unaffected.
• cost_2026_01_08.csv covers 14 top models. Values = billed output_tokens + reconstructed input_tokens (tiktoken o200k_base / Gemini count_tokens API / provider tokenizers for Qwen·DeepSeek·Kimi) × per-model official API prices.

Testing

• npm run build compiles cleanly (no new warnings; feature present in the bundle).
• Data-layer test against the real CSVs — 6/6 pass: merge correctness, missing→— (null), cost-only filter (14 rows), and null-to-bottom sorting (cheapest first = deepseek-v4-pro, $0.029/q).

Notes / follow-ups

• Anthropic thinking models are intentionally excluded from the cost CSV (their stored output tokens predate the billed-usage fix and undercount reasoning — they need a rerun, not a backfill).
• Natural follow-ups: a cost-vs-quality scatter (Pareto), and extending cost_<date>.csv coverage as more models are validated.

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