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LLMs & Chat
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How to choose a language model
Capability is the wrong first question. The frontier models sit close enough on general reasoning that the gap rarely decides anything, and the ranking changes every few weeks anyway. Three cheaper checks decide it: what you pay at your real volume, how much context you need in one call, and whether you can send the data at all.
Compare price per million tokens, not the monthly subscription. Look at input and output separately; output typically costs three to five times more, and most workloads are output-heavy. A model that looks twice as expensive per token can be cheaper per finished task if it needs fewer retries.
Context length matters less than people assume, right up to the moment it is the only thing that matters. Summarising a contract, reading a codebase, or holding a long conversation are the cases where a small window forces you into chunking, and chunking is where accuracy goes.
For teams in Singapore, Malaysia and Indonesia, the constraint is often neither price nor context; it is where inference runs. If your data cannot leave the jurisdiction, most of the shortlist disappears before capability is discussed. Open-weight models you host yourself become the practical answer, not the ideological one.
