Start with the task, not the brand
The single most common mistake is choosing a model by reputation and then forcing every task onto it. The better approach is to define your task precisely first — what goes in, what should come out, how often it runs, and how bad a wrong answer is — and then pick the smallest, cheapest model that clears that bar. Capability, latency, and cost trade off against each other constantly, so the right answer for a high-volume support classifier is rarely the right answer for a one-off legal contract review.
A quick decision framework
Ask three questions in order. First, how hard is the reasoning? Simple extraction, classification, and templated replies need only a small model; multi-step maths, code, and planning justify a top-tier or dedicated reasoning model. Second, how much text is involved? If you feed in long documents or whole codebases, prioritise a large context window. Third, how often does it run? A task called millions of times a day must be cheap and fast per call, while a task run a few times a day can afford the best model available. Score your task on those three axes and the field narrows quickly.
Recommendations by use case
For customer support and chat, a fast mid-tier model handles the bulk of conversations cheaply; route only genuinely tricky cases to a stronger model. For coding, top-tier general models or reasoning models lead on bug fixing and multi-file changes, while a small model is fine for autocomplete and boilerplate. For long-document analysis — contracts, research papers, transcripts — choose a model with a large context window and strong instruction-following. For creative and business writing, models tuned for natural tone and tight instruction adherence produce the least robotic copy. For research and fact-finding, prefer tools with live web access and citations so answers can be verified. For data extraction and structured output, a small model with JSON mode or schema constraints is cheap, fast, and reliable. For high-volume classification or moderation, the smallest capable model wins on cost at scale.
Cost and latency tradeoffs
Pricing spans more than two orders of magnitude between the smallest and largest models, and it is charged separately for input and output tokens. A task that runs once is dominated by quality; a task that runs a million times is dominated by price. Estimate your monthly token volume before committing — a calculator helps — and remember that a cheaper model that needs a retry or a longer prompt can end up costing more than a pricier one that gets it right first time. Latency matters too: interactive, user-facing features need fast small or mid models, while background batch jobs can use slower, cheaper, or larger models without anyone waiting.
Test before you commit
No comparison table substitutes for trying your real prompts on your real data. Build a small evaluation set of representative inputs with known good outputs, run two or three candidate models against it, and compare quality, latency, and cost side by side. Re-run that evaluation whenever you change prompts or providers release new models, because the leaderboard shifts every few months. The goal is not the “best” model in the abstract but the cheapest model that reliably passes your own test — and that is a decision only your data can make.