Qwen3's 235B instruct model ranked 8th on LMArena, tied with the proprietary Claude Opus 4. The only open weightsA model whose trained parameters are published for anyone to download and run — unlike API-only models you can access but never possess.Full definition → models above it, DeepSeek 3.1 and Kimi K2, are about 3x and 4x larger, so Qwen3 wins on performance per parameter.
Qwen released a 1T-parameter 'max' variant that beat Kimi K2, DeepSeek 3.1 and Claude Opus 4 on every major benchmarkA standard public test set for comparing AI models — the shared scoreboards behind every "model X beats model Y" claim.Full definition →, and kept it closed. The open Qwen3 weights are deliberately not the lab's strongest model.
The case for reading code over diagrams: an architecture figure shows you the boxes, but only a working PyTorch implementation pins down tensor shapes and the order operations actually run in, which is what you need to modify it.
Terms in this piece · Glossary
LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
mixture-of-experts — A model built from many specialist sub-networks where only a few activate per token, giving big-model capability at small-model running cost.
open weights — A model whose trained parameters are published for anyone to download and run — unlike API-only models you can access but never possess.
benchmark — A standard public test set for comparing AI models — the shared scoreboards behind every "model X beats model Y" claim.
Why it matters
If you want to actually understand how modern open-weight LLMs work, this walks you through reimplementing Qwen3's dense and mixture-of-expertsA model built from many specialist sub-networks where only a few activate per token, giving big-model capability at small-model running cost.Full definition → variants in pure PyTorch — going beyond conceptual diagrams to working code for one of the most relevant model families today.
Key quotes
“While the code sections may look verbose, I hope that they help explain the building blocks better than conceptual figures alone!”
