Qwen3 ships a whole family under Apache 2.0
Eight models from 0.6B to 235B, hybrid reasoning modes, and a genuinely permissive license across all of them.
Alibaba released Qwen3 today: eight models spanning 0.6B to 235B parameters, including two mixture-of-experts variants, all under Apache 2.0.
The license is the headline. Apache 2.0 with no user threshold, no naming requirement, and no acceptable-use addendum that functions as a license restriction. You can fine-tune it, ship it, sell it, and never mention where it came from.
the lineup#
| model | type | active params |
|---|---|---|
| Qwen3-0.6B → 32B | dense | all |
| Qwen3-30B-A3B | MoE | 3B |
| Qwen3-235B-A22B | MoE | 22B |
The MoE variants are the interesting ones. 30B-A3B has 30 billion total parameters and activates 3 billion per token. That means memory footprint of a 30B model and inference cost closer to a 3B model. On a machine with enough RAM to hold it, throughput is dramatically better than a dense model of comparable quality.
For local deployment this is the shape that matters. Memory is cheap and getting cheaper; compute per token is the thing you feel.
hybrid thinking#
Every model supports two modes: thinking and non-thinking, switchable per request. You can also switch mid-conversation with a token in the prompt — /think and /no_think — which is a hacky interface and also extremely practical.
text = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True,
enable_thinking=True, # or False
)The design conclusion the field has reached, from three different labs independently, is that reasoning should be a runtime dial rather than a model choice. That is now clearly correct and I expect it to be universal within a year.
multilingual coverage#
119 languages and dialects, which is a genuinely differentiating number. Most open models are strong in English, decent in a handful of European languages, and degrade sharply after that. If you are building for a market outside the usual list, this is worth evaluating specifically.
the geopolitical thing#
A large share of the best open-weights models now come from Chinese labs — Qwen, DeepSeek, GLM, Kimi, MiniMax. That is a real shift from two years ago when open weights meant Llama and Mistral.
The engineering reason is straightforward: releasing weights is a good strategy when you are not the market leader, because it buys mindshare, ecosystem, and research feedback that you cannot get otherwise. Meta understood this in 2023. Chinese labs understand it now.
The policy conversation around this is loud and mostly not technical. What is technical, and worth being clear about: weights are weights. A downloaded model runs on your hardware, in your VPC, with no network egress. The supply chain questions worth asking are about what the model does — evaluate it for backdoored behavior, test it on your own adversarial cases — not about where the gradient descent happened.
what to actually do#
If you are running anything local, the 30B-A3B is the current best quality-per-watt option for a workstation. If you are fine-tuning, Apache 2.0 removes the legal review that was blocking you.
Download it before someone decides you cannot.
— Dom, April 29, 2025