Reasoning Models vs Standard Chat Models

Reasoning-tuned models spend extra compute "thinking" before answering, which measurably improves multi-step math, logic, planning, and debugging tasks — and measurably wastes time and money on tasks that don't need it. Reach for a reasoning model when: - The task has multiple dependent steps where an early mistake compounds (multi-step math, complex code refactors, planning a sequence of actions). - Correctness matters more than speed, and you can tolerate several extra seconds (or more) per response. - A standard model has already gotten the task wrong in a way that looks like a reasoning slip, not a knowledge gap. Stick with a standard chat model when: - The task is retrieval-shaped ("what does this say", "summarize this") rather than derivation-shaped. - You're running at high volume where the latency/cost multiplier adds up fast. - The task is simple enough that a standard model already gets it right close to 100% of the time — reasoning mode won't improve on that, it'll just cost more. Rule of thumb: if you can't explain why an answer might require multiple steps of intermediate reasoning, you probably don't need a reasoning model for it.