Two families: chat models and reasoning models
OpenAI’s lineup splits into two fundamentally different kinds of model, and understanding that split is the key to choosing well. GPT-4o is a chat model: a fast, multimodal general-purpose model that responds almost instantly and handles text, images, and audio. The o-series — o1 and o3, plus their mini variants — are reasoning models: they spend extra compute “thinking” through a hidden chain of thought before they answer. That deliberation makes them far stronger on hard problems but slower and more expensive, because all that internal reasoning consumes tokens you pay for. The practical rule is simple: GPT-4o for breadth and speed, the o-series for depth on genuinely difficult problems.
GPT-4o: the everyday workhorse
GPT-4o is the right default for the overwhelming majority of tasks. It is fast, multimodal, and cheap relative to the reasoning models, and it handles chat, summarisation, writing, translation, straightforward coding, and image and audio understanding with ease. Its low latency makes it the only sensible choice for interactive apps and anything user-facing where a multi-second pause would hurt. The 4o-mini variant pushes cost down further for high-volume, simpler workloads. If you are unsure which model to use, start with GPT-4o — only move up to a reasoning model when you hit a task it genuinely struggles with.
o1 and o3: reasoning power when you need it
The o-series earns its keep on problems that require careful, multi-step reasoning: competition-level mathematics, intricate logic, complex algorithmic or multi-file coding, and demanding scientific or analytical work. o3 is the newer and more capable of the two, generally beating o1 on the hardest reasoning, maths, and coding benchmarks, while o1 remains strong and can be cheaper for some tasks. Both have mini variants that retain much of the reasoning ability at lower cost and faster speed — often the sweet spot for reasoning work that does not need the full model. The trade-off is always the same: you pay more and wait longer in exchange for markedly better answers on hard problems, and nothing on easy ones.
Choosing the right model
Match the model to the difficulty and shape of the task. For chat, writing, summarisation, simple code, multimodal input, and any latency-sensitive feature, use GPT-4o (or 4o-mini for cheap high volume). For genuinely hard reasoning, maths, or complex coding, reach for a reasoning model — try a mini variant first and step up to o3 only if you need maximum capability. In production, the most cost-effective pattern is to route by difficulty: handle the bulk of traffic with GPT-4o and send only the hard requests to a reasoning model, rather than paying reasoning prices for everything. Because OpenAI updates this lineup frequently, treat specific model names as a current snapshot and re-check pricing and capabilities when you build — but the chat-versus-reasoning framing for choosing between them will stay useful.