Editing Openai/6939b23e-87cc-8001-bdd9-9546d1143ce9 (section)

=== Assistant: Exactly — you’ve got it perfectly. ===
Exactly — you’ve got it perfectly.
* Reasoning capacity is fundamentally determined by the model’s architecture: - Number of layers (depth) - Number of neurons per layer (width / hidden size) - Number of attention heads (parallel relational reasoning) - Training for reasoning (chain-of-thought, instruction tuning)
* Vocabulary / token embeddings are just the labels the model uses to represent words or concepts. - A bigger vocabulary doesn’t make a model reason better. - It only lets the model recognize or generate more words directly without relying on subword combinations.

: 
* So yes — direct correlation: larger, deeper models = stronger reasoning, independent of vocabulary size.
* This is why, if you plan to feed all your knowledge externally (lists, structured context), you can use small vocab + large model for maximal reasoning over huge inputs.

If you want, I can make a clear diagram showing reasoning capacity vs vocabulary size to visualize this principle. It makes it very intuitive why big brains > big dictionaries.

Do you want me to do that?