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Continuing from the first part, let’s look at some more of OpenAI’s model naming conventions.

Pro

While mini models sacrifice performance for improved speed and decreased costs, pro-class models do the opposite. They are optimized for accuracy and better reasoning, and as such, are slower and more expensive. Thus, they are more suited to mission-critical use cases.

To put the mini-regular-pro comparisons into perspective, mini models are generally roughly half the cost of regular models (comparing cost per million input and output tokens). On the other hand, o1-pro is 10-50x as expensive as regular models.

Comparing performance, let’s look at AIME (American Invitational Mathematics Examination) benchmark results, popularly used as a maths benchmark.

The mini-class of models sit at ~70-80% on the benchmark. Regular models score ~75-90%. o1-pro, however, scored 93% on it! A 3% increase might not seem like much, but interpreting it differently, the o1-pro model makes 30% fewer mistakes than the regular models, which could be a very useful improvement.

.5

GPT 3.5 is an improvement on GPT-3, but it was built upon GPT-3, and was not revolutionary enough to warrant a new number. Hence, the “.5”. This shouldn’t be unfamiliar if you’ve interacted with software versions before. 

Turbo

Turbo models are optimized for speed, and to a lesser extent, cost. Pricing sits between regular and mini models, and performance is lower, but close to regular. While model size is reduced for mini models, turbo models maintain a similar size to regular.

Others

Those were the main naming conventions, but let’s take a look at a couple more.

Moderation

Moderation models are designed to screen outputs for policy-violating content.

Realtime

Realtime models are designed to deliver low-latency models suitable for streaming input and output to and from the model. You might use them in TTS/STT applications, or other applications where low-latency is critical.