Imagine a giant mixing console, but with trillions of adjustment knobs. Each knob controls a tiny nuance in how the AI transforms what it reads into what it writes. A parameter is one of those knobs. "1.5 trillion parameters" means: 1.5 trillion adjustable knobs. Training consists of finding the right setting for each one. There you go, you've already got the essentials.
Grok 4.5: 1.5 trillion parameters. LongCat-2.0: 1.6 trillion. GPT, Claude, Gemini: figures trotted out at every release like flexed muscles. The parameter count has become the go-to marketing argument for AI models. But what does it really refer to, and does it deserve the importance we give it? Let's untangle this.
The simple definition
A parameter is a numerical value internal to the model, adjusted during training, that helps transform an input (your text) into an output (its response). Going back to the console image: each parameter is a tiny setting. Taken in isolation, a parameter means nothing. It's their combination, by the billions, that encodes everything the model "knows" how to do.
During training, the steps of which we described in our article on how AIs learn, these billions of values are adjusted gradually, over and over, until the model produces good predictions. The final result, this "trained" model, is at bottom nothing more than a gigantic set of these frozen values. When you download an open-source model, you are literally downloading its parameters, also known as its "weights".
Why the number matters (a bit)
The parameter count is not meaningless. As a general rule, the more parameters a model has, the more "capacity" it has to memorise knowledge and capture subtle nuances of language. A tiny model will never be able to encode as much knowledge as a giant one, just as a ten-knob console will never allow as much finesse as a thousand-knob console.
That's why the race for size long dominated the sector: you increased parameters to increase capabilities. This relationship does exist, and it partly explains why recent models are more impressive than those from three years ago.
A larger number of parameters does NOT guarantee a better model. A giant model trained on mediocre data will be beaten by a smaller model trained on excellent data and well fine-tuned. Size is a potential capacity, not a guaranteed performance. Judging a model solely by its parameter count is like judging a book by its page count: it tells you something about the scope, nothing about the quality.
The revolution that changed the game: experts
There's a recent subtlety that makes the raw figure even more misleading. Many modern models use an architecture called Mixture-of-Experts. Instead of activating all their parameters for every word generated, these models only activate a small fraction at a time, routing each task to the most relevant internal "experts".
Concretely, LongCat-2.0 boasts 1.6 trillion parameters in total, but only activates around 48 billion per request. It's like having a huge team of specialists, but only calling in the three or four who matter for a given question. Result: a model can be huge in stored knowledge while remaining relatively economical in computation. The "total" figure and the "active" figure then tell two very different stories, and marketing releases often highlight the more flattering one.
What to take away
The parameter count is an indicator of a model's scale, its raw capacity to store knowledge. It's useful information, but partial and easy to misinterpret. A high figure is impressive, but on its own it says nothing about data quality, training finesse, or the architecture that decides how those parameters are actually used.
Next time a lab sells you its "trillion parameters", you'll know how to read between the lines. Ask yourself: how many are actually active at any given moment? On what data was the model trained? And above all, what are its results on independent tests worth? Because in the end, what matters isn't the number of knobs on the console, but the music that comes out of it.