The engine panel

Every shopper question is asked of four model families in parallel, through the Vercel AI Gateway.

The panel

Family	Primary model	Fallbacks
OpenAI	`openai/gpt-5.6-terra`	`openai/gpt-5.4`, `openai/gpt-4o`
Anthropic	`anthropic/claude-sonnet-5`	`anthropic/claude-sonnet-4.6`, `anthropic/claude-sonnet-4.5`
Google	`google/gemini-3.6-flash`	`google/gemini-3.5-flash`, `google/gemini-2.5-pro`
Perplexity	`perplexity/sonar`	`perplexity/sonar-pro`

Fallbacks are same-provider only, by design: falling back across providers would silently change which family answered, and the panel exists precisely to measure per-family difference.

Model IDs are pinned in source and re-asserted against the live gateway catalog in CI, so a model retirement fails the build rather than silently degrading an audit.

Call parameters

temperature: 0
max_tokens: 1024
Per-call timeout: 20 seconds
Engine concurrency: 4

Volume per run

Up to 4 engines × 5 questions = 20 engine calls, plus one brand-profile call and one judge call per response that came back.

Two supporting models

Not part of the panel, and not scored:

Role	Model
Brand profile extraction	`openai/gpt-4o-mini`
Response judging	`openai/gpt-4o-mini`

Skipped engines

An engine that could not be reached is recorded in engine_panel.skipped with a reason, and each of its unanswered questions counts as a hole in the evidence denominator. Enough holes trips the coverage gate and the score is withheld.

Zero available engines is a hard failure. It is never a 100/100.