Question Everything runs several AI models in production, every day. This is our honest, evidence-rated answer — including the places where the honest answer is “this is genuinely contested,” not a confident ranking we can’t back up.
Every claim below is rated by how confident we actually are, and why — the same discipline QE applies to every claim on every knowledge page.
A fast, low-cost model (e.g. Gemini Flash tier)
For most questions, the gap between a fast, cheap model and the most expensive frontier model is small relative to the cost difference — often 10-25x cheaper per query for output that's very close in practical usefulness.
Why this confidence level: Directly measured — this is why QE itself defaults to a fast, low-cost model as its primary provider, not its most expensive one.
A dedicated reasoning-tier model, deliberately
Reasoning models spend part of their response budget on internal deliberation before producing visible output — genuinely different behavior from a standard model, not just "the expensive version." This makes them better suited to problems that benefit from that deliberation, and a poor fit for simple lookups where the reasoning overhead adds cost and latency without adding value.
Why this confidence level: Directly observed — see Field Notes for a real incident this caused when the difference wasn't accounted for in advance.
Varies — this is genuinely contested, not settled
Different model families are widely reported to lead on coding benchmarks at different times, and benchmark performance doesn't always translate cleanly to a specific real codebase or task. QE doesn't have enough first-party comparative data across a large enough sample of real coding tasks to make a confident universal claim here.
Why this confidence level: This reflects the current state of public benchmarks and developer discussion, not QE's own measured comparison — stated honestly rather than asserted as fact.
The cheapest model that reliably clears your quality bar — test, don't assume
Per-token cost varies by roughly 60x across the models QE runs today (see the cost table below) — cheapest to most expensive. The right choice depends entirely on whether the cheaper model's output quality is good enough for your specific use case, which is task-specific, not a general fact about the models.
Why this confidence level: Directly measured from QE's own production billing.
Approximate, blended per-million-token rates across the tiers QE runs — rough by design, not implied precision we don’t have.
| Tier | ~Cost / 1M tokens | Note |
|---|---|---|
| Fast, low-cost tier (e.g. Gemini Flash) | ~$0.15 | Cheapest tier QE runs — the default for most traffic |
| Efficient mid-tier (e.g. GPT-4.1 mini) | ~$0.40 | Good balance of quality and cost |
| Reasoning tier (e.g. GPT-5.6-class) | ~$2.00 | Spends part of its budget on internal reasoning — see Field Notes |
| Full frontier tier (e.g. GPT-4.1 full) | ~$4.00 | Deepest single-pass analysis, highest cost |
| Premium tier (e.g. Claude Sonnet-class) | ~$10.00 | Most expensive tier QE runs — roughly 60x the cheapest |
Want the real incidents behind this guidance — what actually broke, and how it got fixed?
Read Field Notes