I strongly agree with this sentiment and I feel the same way.
The one exception for me though is when non-native English speakers want to participate in an English language discussion. LLMs produce by far the most natural sounding translations nowadays, but they imbue that "AI style" onto their output. I'm not sure what the solution here is because it's great for non-native speakers to be able to participate, but I find myself discarding any POV that was obviously expressed with AI.
one solution that appeals to me (and which i have myself used in online spaces where i don't speak the language) is to write in a language you can speak and let people translate it themselves however they wish
i don't think it is likely to catch on, though, outside of culturally multilingual environments
You can expect this model to have similar performance to the non-omni version. [2]
There aren't many open-weights omni models so I consider this a big deal. I would use this model to replace the keyboard and monitor in an application while doing the heavy lifting with other tech behind the scenes. There is also a reasoning version, which might be a bit amusing in an interactive voice chat if it pronounces the thinking tokens while working through to a final answer.
1. https://huggingface.co/Qwen/Qwen2.5-Omni-7B
2. https://artificialanalysis.ai/models/qwen3-30b-a3b-instruct
last i checked (months ago) claude used to do this