NEWTON’SSame problem, different integration cost
Every provider switch adds setup friction and skews comparison timing.
COMPARISON WITHOUT REWRITES
Test models.
Change integration last.
If your team evaluates AI capability, your bottleneck is usually process, not model access. Newton’s keeps the schema stable and pushes comparison work into one visible layer.
Your week before Newton’s
Mon: shortlist models. Tue: each model means a new config path. Wed: benchmark scripts drift. Thu: output quality improves, budget review stalls. Fri: no common receipt format slows decisions.
Keep prompt schema stable
Use one OpenAI-compatible flow and vary model IDs instead.
Quality + cost together
Route IDs and receipts make each benchmark output both attributable and auditable.
What you can validate in one pass
The same prompts, same code, different model target IDs. Then a single receipt surface confirms route identity and spend.
Exact route IDs
Match model names to published route IDs before interpreting benchmark output.
Receipt proof
Compare accepted route, usage, and cost across candidates from one layer.
Keep architecture unchanged
Endpoint and key swap is the only structural action for most evaluations.
Evaluator proof lane
Use this to move teams from “looks good” to “safe to scale.”
Recommendation-to-implementation sequence
One minimal request, one receipt check, one budget check. Then add candidate set depth only after route and cost are stable.
Use this when evaluation confidence matters
Start with Start with $5, run a minimal smoke set, and decide by evidence not promises.