v0.2.78

[0.2.78] — 2026-10-09

Changed

• Each model's efforts are priced by the tokens it was measured drawing
  there. The frontier placed a model at an effort at its blended price
  times one table for every model (low 0.6 … max 2.5). A per-token price is
  the same at every effort, so what an effort costs is the tokens it draws,
  and measured, models differ in that more than efforts do:

  • Sonnet 5.5 draws about the same tokens from low to xhigh, then 3x at max.
  • At medium it draws 1.7x what GPT-6.1 Sol does.
  • GPT-6 Luna draws the same at low and medium.

  update/measure_effort_tokens.py runs every model AA measured at several
  efforts through eight short tasks, twice, at each of those efforts: Claude
  models on Claude Code, the GPT models a ChatGPT sign-in runs on Codex, the
  rest on OpenRouter. OpenRouter's figures are scaled by a measured lane
  factor (0.78: the same model draws about 1.3x the tokens through the bare
  API). Each model's figures are fitted to rise with its effort.
  docs/effort-tokens.json (bundled) holds the result for 29 models;
  scoring.token_multiplier reads it, and the uniform table remains the
  fallback. AA's own token counts are not in its API, and its terms forbid
  scraping them. The first run cost $9.24 on OpenRouter.

  For the operator's context, 7 of 28 ladder rows change:

  • COST on the low rows runs GPT-6 Luna at medium.
  • BALANCED on low coding, planning and knowledge work is GPT-6.1 Sol at
    medium.

  For an anonymous visitor, 11 rows change. Among them, Grok 4.7 (3.6x a
  typical model's tokens) leaves COST on high coding, planning and
  long-context work. The agentic/high row stands: GPT-6.1 Sol · Medium,
  Sonnet 5.5 · Extra high, Opus 5.5 · Extra high.

• QUALITY may run its model at a higher effort that left the frontier.
  Where a category rates a model's efforts alike (multimodal), measured
  token use pushed Gemini 3.8 Flash's medium and high off the AA-Index
  frontier, which would have left QUALITY at Flash · Low. QUALITY now also
  reads the top point's own model at each higher effort that meets the bar
  (_higher_efforts); speed work keeps the lower, faster effort. Under the
  uniform table this changes no row.

• The daily benchmark refresh keeps one issue open, "chore(effort-tokens):
  model efforts not yet token-measured", while AA measures a model or effort
  that the file lacks. It closes the issue when there are none.