A quality bar for each use case, and the cheapest model that clears it

Published 2026-10-18 · Prose last reviewed 2026-10-19 · Figures recomputed nightly from the 2026-10-22 catalogue snapshot

A FinOps guide by OptimNow · prices from the live OptimToken catalogue

Most teams choose one model for everything, and it is the newest one. The bill then carries top-tier prices on work a smaller model does as well. The method on this page runs the other way: decide per use case how good the answer has to be, then buy the cheapest model that clears that bar.

A router works on the same principle. It does not set the quality bar. It finds the cheapest path that clears the bar it is given, and the bar belongs to the product owner. Here the bar is a minimum LMArena score, the path is a model, and the catalogue answers the price.

Step 1: score the need, not the model

A quality bar describes the work, not the technology. Three questions set it: what a wrong answer costs, who catches it before it does harm, and whether one error feeds the next. An invoice field is checked against a schema and a meeting summary is read by a person, so a mistake is cheap and caught early. A patch merged into a codebase, or a step inside an agent loop, is neither.

The scale we measure the bar on is LMArena, a public ranking built from human preferences between models. It is one general benchmark, and it says nothing about a team's own documents or formats. It is also the only quality measure the catalogue carries for every ranked model, which is what makes a bar comparable across model makers.

The bar is written as a distance below the best ranked model, not as a fixed score. Arena scores drift upward as votes accumulate, so a fixed number gets easier every month. "Within 25 points of the best" keeps its meaning.

Step 2: three thresholds

Three thresholds cover the 8 use cases the pricing table prices:

• Frontier-grade, within 25 points of the best ranked model: work where an error compounds or costs engineer time. Coding task and agent workflow.
• Strong, within 60 points of the best ranked model: text a customer or a colleague reads, and nobody checks line by line. Support ticket, knowledge Q&A and marketing content.
• Standard, within 110 points of the best ranked model: output checked against a schema or read as a draft. Meeting summary, invoice processing and call summary.

The first threshold sits just past the benchmark's own noise. A gap under 20 points does not separate two models on Arena, and Frontier-grade allows 5 more, so it means "as good as the best, as far as the benchmark can tell, or nearly". On the 2026-10-22 snapshot the best ranked model scores 1525, which puts the three bars at 1500, 1465 and 1415.

These thresholds are OptimNow's working assumptions from AI cost reviews, not measurements. A team that runs an evaluation on its own traffic replaces them with its own, and should. What the method keeps is the order of operations: the bar first, the model second.

Step 3: which models clear each threshold, and at what price

Each threshold is cleared by a set of models, and the set is not priced alike: at the Standard level the dearest model clearing the bar costs 246x the cheapest. The cheapest model in the set is the floor for that threshold:

• Standard: DeepSeek V4 Flash 0423 at $0.1547 per 1M tokens.
• Strong: MiMo-V2.5-Pro at $0.5176 per 1M tokens (3.3x the Standard floor).
• Frontier-grade: Gemini 4 Argon at $7.60 per 1M tokens (14.7x the Strong floor).

The shape matters more than the figures. The floor rises between the lower two thresholds and climbs 14.7x at the top: the last 35 points of quality cost more than all the others together. A team that can state why its use case needs them pays that price with a clear conscience. A team that cannot is paying for a score. Compare the two ends before deciding.

A floor is one model and one host's published rate. The Standard floor, for instance, is DeepInfra's rate for DeepSeek V4 Flash 0423, one of 15 hosts selling it. Rule that model out, for licence or data-residency reasons, and the next maker at the Standard level is 1.3x dearer. Gemini 4 Argon is quoted at a promotional rate, 50% off its standard price, and Google states that it ends on 30 Nov 2026. MiMo-V2.5-Pro is quoted at a promotional rate, 30% off its standard price, and its end date is not published.

Step 4: from use case to price

Put the three steps together and each use case gets a threshold, a model and a monthly figure. The table costs 100,000 requests a month at list price, on each use case's own token mix, and sets it against the same work on the best ranked model, Gemini 4 Argon. The floor is found again on each mix, because a model cheap on input and dear on output can be the right answer for a support ticket and the wrong one for a coding task.

Across the 8 use cases, matching the bar costs $7.3K a month against $16.3K on the best ranked model everywhere: 55% less, for the same volume. The Frontier-grade rows take 93% of the matched total, so that is where an evaluation that lowers a bar pays for itself fastest.

What the method does not do

• It does not replace an evaluation. Arena measures general preference on open-ended prompts. A model can clear a bar there and fail a structured-output task that a lower-scored model passes. The bar shortlists; the evaluation decides.
• It does not price the move. Prompt rework, a second vendor relationship and the evaluation itself are real costs, and the gaps in the table are what they have to be smaller than.
• It reads one day's prices. A floor is one host's published rate for one model. Promotions end, preview builds change, and a host can be ruled out by data residency. The table rebuilds nightly; re-read it before each budget cycle.

How to use it

The order of work follows FinOps maturity. A team at Crawl, with no evaluation set and no per-workload token bill, starts by writing the bar down for each use case, in words, before any score. A team at Walk, with an evaluation it trusts, reads the floor off this page for each bar and runs that model through the evaluation. A team at Run routes between the floor and the models above it per request, and re-reads this table each month. Figures on this page come from the 2026-10-22 price snapshot and Arena scores as of 2026-10-08, and rebuild nightly.

The FinOps reasoning behind this guide comes from OptimNow's open-source practice library: github.com/OptimNow/cloud-finops-skills. If you want your own use cases given a quality bar and costed against the floor that clears it, OptimNow runs that review.

Data snapshot: 2026-10-22 · Compare all models · Cloud compute pricing · All guides