Which AI Model Is Good Enough? A Calculator by Task and Budget

Research-based — not hands-on tested. Our scores are editorial judgements compiled from vendor documentation, published pricing and independent user reports. How we review.

Most “best AI model” advice answers a question nobody asked: which model is best overall. The question people actually have is narrower and cheaper to answer — is the cheap one good enough at the specific thing I do, and what will it cost me a month?

Pick the task, pick roughly how much you run, and set how far below the best score you are willing to sit. The calculator names the cheapest model that clears that bar.

How it decides

Each task maps to one named benchmark that measures it, and every number shown is a published score on that benchmark. The “good enough” line is simply the best published score minus the tolerance you choose. The cheapest model at or above that line is the pick.

Costs are estimated from list API prices at the token volumes shown, so they are a comparison between models rather than a forecast of your bill. Cache discounts, batch pricing and negotiated rates all push real spend down.

What it deliberately will not do

• No overall score. There is no single 0–99 quality number here, because producing one means adding up results from benchmarks that measure different things on different datasets. We published a composite like that once and removed it — SWE-bench Pro and SWE-bench Verified are not the same test, and a weighted blend of the two is not a meaningful quantity.
• No comparing across benchmarks. A model’s Terminal-Bench figure tells you nothing about its ARC-AGI standing. Change the task and the whole ranking legitimately changes.
• No filling in blanks. Models without a published score on the selected benchmark are listed underneath rather than dropped or scored zero. Vendors publish different benchmark sets; DeepSeek in particular overlaps the others on almost nothing. A missing figure is missing, not a failure.

The one caveat that matters most

Reasoning effort moves these scores more than the choice of model does. On ARC-AGI-2, GPT-5.4 ranges from 67.5% to 83.3% depending purely on whether it runs at High, XHigh or Pro-XHigh — a wider spread than the gap between many of the models here. Read every score as “this model, at the effort setting its publisher used”, and expect your own results to move when you change that setting.

For the generational version of this question — whether today’s cheap tier has caught up with last year’s flagship — see Is the cheapest AI model good enough?, which compares GPT-5.6 Luna against the frontier models of late 2025 and early 2026.