ab-v3 succeeds ab-v1 (frozen 2026-04-28). Carries forward all 40
v1 obvious yaml files unchanged so the v1↔v3 overlap stays
comparable, then layers in two new dimensions.
**1. Token cost.** All clients (openai-compat, ark, bailian,
deepseek, minimax, codex-cli, stub-model) now return a
`ChatCallResult { content, usage }` instead of bare string.
Provider usage stats (`prompt_tokens` / `completion_tokens`) plumb
through realModelCall → run.ts → scoreRun → ScoreRow.{prompt,completion}Tokens.
aggregate adds avgPromptTokens{Baseline,Treatment} +
avgCompletionTokens{Baseline,Treatment} per ModelSummary.
write-report emits a new "Token cost" table with Δ columns so
narrow-tools-saves-tokens (the ab-v2 hypothesis) is measurable.
avgUsage skips rows with 0/0 usage so codex-cli (CLI doesn't
surface tokens) and harness errors don't deflate the average to
near-zero — they show '—' instead.
**2. Composite difficulty.** New 'composite' value alongside
obvious / optional. Composite prompts express multi-tool intents
where no single expected_tool_if_any applies. classifyRouting
routes composite-treatment runs into multi-tool / fallback /
garbage (3-bucket sum to 1, distinct from obvious's 4-bucket
right/wrong/fallback/garbage). aggregate adds m6_multi_tool +
m6_fallback + m6_garbage; write-report emits a "Composite routing"
table that gracefully degrades to a placeholder when no composite
yaml exists yet.
Harness side: scripts/ab-corpus/run.ts accepts --corpus ab-v3
(enum + parseArgs guard); dry-run on the v1-mirror corpus produces
a 160-row report including populated token table.
Tests: 4 new aggregate cases (composite, token avg with skip-zero,
NaN-when-no-data) + 4 new score-run cases (composite routing
multi-tool/fallback/garbage/baseline-n/a) + 2 new score-run cases
(usage plumbing) + 2 new openai-compat cases (usage parsing,
missing-usage fallback). Existing 5 retry tests updated for new
return shape. 3727 → 3740 vitest tests, all green; tsc + format
clean.
Token-cost docs and composite docs go straight into types.ts /
score-run.ts / aggregate.ts JSDoc — keeps the contract close to
the code that owns it.
`/models` now returns only deepseek-v4-pro and deepseek-v4-flash;
deepseek-chat / deepseek-reasoner sunset 2026-07-24 and the
deepseek-v3.2 hard-coded in the ark-coding fallback list never
existed. Both v4 models default to thinking enabled and the API
toggles via `{"thinking":{"type":"disabled"}}` — keep
`thinkingMode: 'disabled'` so the app's fast/non-thinking default
stays intact (server reasoning paths honor it; the Zig openai-compat
path doesn't emit the toggle yet, so calls through that path still
get provider-default thinking until it's wired). v4-pro promoted to
full tier; legacy aliases pinned to an exact RegExp so future
deepseek-* variants don't inherit a forced disabled mode.
Bandaid for the unwired toggle: v4-pro gets `timeoutMultiplier: 2`
because its default-on reasoning blows past the orchestrator's
planning timeout on long system prompts (observed in dev: planning
phase falls back, sub-agent then succeeds — UX degraded but
functional). Drop the multiplier once the Zig path actually sends
`thinking:{type:disabled}`.
Don't add a BUILTIN_MODEL_LISTS.deepseek entry — DeepSeek exposes
/v1/models, so let `fetchProviderModels` pull the live catalog
through `/api/ai/provider-models` instead of pinning a snapshot
(the ark-coding `deepseek-v3.2` ghost above shows what those
snapshots drift into).
Harness at scripts/ab-corpus/ wires the pen-ai-skills corpus evaluator to
real model endpoints and pen-mcp handlers:
run.ts — CLI entry (--dry-run / --live / --models A,B,C / --only ID)
apply.ts — ApplyFn impl dispatching tool_call → element handler
and batch_design DSL → handleBatchDesign, against a
fresh tmp .op per run (isolated, auto-cleanup)
build-prompt.ts — B variant strips elements.md + appends batch_design
<op_tool> format instruction; T keeps elements + adds
element-tool PRIMARY / batch_design FALLBACK
instruction. Uniform <op_tool> wrapper in both arms
isolates "tool set width" as the only A/B variable.
stub-model.ts — fixture-based offline model for --dry-run
real-model.ts — router by model id (minimax* / gpt-*/o* / glm-5.1 /
glm-* / kimi-*)
clients/
openai-compat.ts — generic chat/completions POST
minimax.ts — api.minimax.io/v1, MINIMAX_API_KEY
codex-cli.ts — spawns `codex exec` (GPT-5.4 via Codex Pro sub)
bailian.ts — coding.dashscope.aliyuncs.com/v1 CP,
DASHSCOPE_BAILIAN_CODING_KEY (hosts glm-4.7, kimi-k2.5)
glm.ts — open.bigmodel.cn/api/coding/paas/v4 official CP,
GLM_OFFICIAL_CODING_KEY
write-report.ts — Report → report.md + report.json in out dir;
4-way routing breakdown table per model
Kept entirely outside packages/ — scripts are a local dev tool, not part
of the published SDK. API keys never hit disk or git.
v1 run results logged separately in openpencil-docs
superpowers/notes/2026-04-20-ab-v1-results.md (5 models × 24 prompts).
