Open Weight Models Are Catching Up Faster Than People Realize
By Venkata Anirudh Devireddy · Endoblog.dev
There used to be a clear line between the best closed models and the best open weight models. However, the gap between these two categories is starting to shrink, which is something every AI developer should be aware of.
What "open weight" means
Open weight models mean that the trained parameters of the model have been published so that anyone can download and use the model. While the model may not necessarily be open source (as the training data and the training code might not be published alongside the model), the fact that the trained parameters are made available means that anyone can use the model on their systems rather than having to use an API to access the model.
Why the gap closing matters
Because the best open models are close to the best closed models means that people will have to consider other factors when choosing between the two: cost, privacy, control over the model, whether you need to keep it offline or on your own infrastructure. These are healthier factors to weigh up against performance alone.
Who benefits most from this
Those who are developing products where sharing data with a third party is not an option. Researchers who need to examine and change the model. Those who would like to fine tune the model with their own data.
The tradeoffs that still exist
You can always run a strong open model yourself on your hardware or spend money to host it somewhere else. The best closed models will still beat the open models on many difficult tasks, such as coding and logical reasoning. Furthermore, you must maintain the infrastructure for open models yourself.
What this means for you as a student or builder
You can choose to use the closed models for the tasks in which you want to maximize the quality of the results you receive, and use open models for the tasks in which you care more about the privacy of your data, cost, or otherwise controlling the model yourself. Understanding these options and in what contexts each type of model makes the most sense is a more valuable skill to learn than just picking a favorite model to use for all your projects.
