GPT Models
Compared
Mini, 4o, o1, and o3

The lineup exists because one model can't do everything cheaply

Running every request through the most capable model available sounds simple until the bill arrives. OpenAI's lineup splits the work: GPT-4o Mini exists so high-volume, low-complexity calls don't cost the same as a hard reasoning task. GPT-4o sits in the middle as the model most products should call by default. o1 and o3 exist for the smaller slice of tasks where getting the answer right matters more than getting it fast or cheap.

The practical consequence is that model choice is not a one-time architecture decision. It is a per-task routing decision. A feature that classifies support tickets and a feature that generates a project plan from a client brief should not call the same model, even within the same application. Getting this routing right keeps unit economics manageable as usage scales.

How we actually route between them in production

UltimaBot and UltimaWriter, the AI platforms we've built and maintained for our client Steven since 2019, route between OpenAI, Anthropic, and Gemini models depending on the task rather than calling one model for everything. For structured, repetitive calls, we default to the cheapest model that reliably gets the job done, which is often GPT-4o Mini or an equivalent Anthropic model, not the flagship. Reasoning models like o1 get reserved for the specific steps in a workflow where the task involves multiple dependencies or the cost of a wrong answer is high enough to justify the extra latency.

o3 sits at the top of this hierarchy and we reach for it rarely. Most production tasks that developers assume need o3 are actually handled well by GPT-4o or o1. The real use case for o3 is a task that genuinely exhausts o1's reasoning depth, complex agentic workflows with many interdependent steps, advanced mathematical problem-solving, or architectural planning where a confident but wrong answer creates downstream problems that are expensive to fix.

What CV Infotech uses

For day-to-day coding, our team writes in Cursor with Claude Sonnet, not directly against the OpenAI API. Where we do call OpenAI models directly, it's inside client platforms like UltimaBot, chosen per task rather than by default. We use Gemini 1.5 Pro specifically for legacy codebase audits because of its large context window. We don't use DeepSeek or Grok on client work, our US, UK, and Australian clients have data sovereignty requirements neither model clears.

This isn't a statement about OpenAI's models being inferior. It reflects the same routing logic we recommend to clients: use the model that fits the task. Claude Sonnet leads for coding in our evaluation. GPT-4o leads for multimodal features and image generation via DALL-E. Gemini leads for cost-sensitive, high-volume tasks. The best model for your product is the one that solves your specific problem at a cost your business can sustain.

Where we fit

If you're calling the OpenAI API directly for a side project, picking a model is a five-minute decision. Where this gets harder is inside a real product, where model choice affects your unit economics at scale, and a wrong default multiplies across every user. We help teams design that routing logic properly rather than defaulting to the most expensive model everywhere out of caution.

That means looking at your actual task types, matching each to the cheapest model that reliably handles it, and building a monitoring layer so you can detect when quality drops. $30 an hour, written scope before billing. See our AI development services.

Related reading: Claude Models: Haiku vs Sonnet vs Opus · ChatGPT vs Claude · ChatGPT vs Gemini · AI Development Services · AI Models Guide