TechnicalAI glossary
Parameters
The billions of internal "settings" in a model, adjusted during training. The more there are, the more nuances the model can capture (but the more it costs).
In other words
The billions of tiny knobs on a mixing console, all set automatically.
Alyriaz articles that mention it
- Six hundred billion parameters at a dollar per million tokens
StepFun has launched Step 5 Preview, a 600-billion-parameter model with a one-million-token context window, priced at 1 dollar for input. We put that rate into context within the current landscape.
- An open 744-billion-parameter agent beats the best closed model on web navigation
Shanghai AI Lab has quietly released a 744-billion-parameter agent that beats GPT-5.6 Sol on a web navigation benchmark. We look at what that result actually means.
- K2 Horizon: six open models, from 0.9 to 375 billion parameters
The Institute of Foundation Models has released six open models under Apache 2.0, spanning a range from 0.9 to 375 billion parameters. We explain why publishing a full range changes the game for those building.
- An Arabic model with 428 billion parameters, built on Chinese weights
A Saudi lab has released a 428-billion-parameter model for Arabic, built on Chinese open weights. We analyse what this combination reveals about the new geography of AI.
- Meta releases a 30-billion-parameter agent that runs on a single graphics card.
Meta has released a 30-billion-parameter agentic model designed to run on a single graphics card. We explain why this hardware constraint is more interesting than the race for giant models.
- What does "1.5 trillion parameters" mean? The figure we brandish without ever explaining it
Grok 4.5 and its 1.5 trillion parameters, LongCat and its 1.6 trillion: the parameter count is brandished as a mark of power. We explain what a parameter really is, why it matters, and above all why a larger number does not guarantee a better model.