DeepSeek R1

DeepSeek R1 is a reasoning model — one that works through a problem step by step before answering, rather than producing an answer directly. Its significance is that it made that capability available at open-weights pricing, which had until then been the preserve of expensive closed models.

The trade is explicit: it spends more tokens and more time to be more reliable on problems where working through the steps genuinely matters.

Mathematical and logical problems are where the approach pays. On multi-step problems, a model that shows its reasoning catches its own errors partway through in a way that direct-answer models cannot.

The reasoning trace is also auditable. For any application where you need to justify an answer rather than just produce it, being able to read how the model got there is worth more than the answer alone.

It is slower and more verbose by design, which makes it a poor fit for anything interactive or high-volume. You pay for the reasoning tokens whether or not the problem needed them.

Applying it to simple tasks is actively wasteful — a question with an obvious answer gets three paragraphs of deliberation first. It is also weaker than dedicated models on creative writing and general conversation.

Use R1 where the problem has a right answer that requires several steps to reach: mathematics, logic, structured analysis, anything you need to be able to audit. Route everything else elsewhere — reasoning models are a tool for a specific job, not a general upgrade.