They claim to undercut competitors of similar quality by half for both models, yet they released both as Apache 2.0 instead of following smaller - open, larger - closed strategy used for their last releases. What's different here?

weights:https://huggingface.co/mistralai/Voxtral-Mini-3B-2507 and https://huggingface.co/mistralai/Voxtral-Small-24B-2507

There is also a Voxtral Small 24B small model available to be downloaded: https://huggingface.co/mistralai/Voxtral-Small-24B-2507

Running Voxtral-Mini-3B-2507 on GPU requires ~9.5 GB of GPU RAM in bf16 or fp16.

Running Voxtral-Small-24B-2507 on GPU requires ~55 GB of GPU RAM in bf16 or fp16.

My Whisper v3 Large Turbo is $0.001/min, so their price comparison is not exactly perfect.

Does it support realtime transcription? What is the ~latency?

24B is crazy expensive for speech transcription. Conspicuously no comparison with Parakeet, a 600M param model thats currently dominating leaderboards (but only for English)

In demo they mention polish prononcuation is pretty bad, spoken as if second language of english-native speaker. I wonder if it's the same for other languages. On the other hand whispering-english is hillariously good, especially different emotions.

Im pretty excited to play around with this. I’ve worked with whisper quite a bit, it’s awesome to have another model in the same class and from Mistral, who tend to be very open. I’m sure unsloth is already working on some GGUF quants - will probably spin it up tomorrow and try it on some audio.

Won't comment on the 24B model as I see no use for it personally, but regarding purely ASR tasks, I honestly can't see voxtral taking off. For personal usage, I've been running a quant of whisper tiny(for english), as well as whisper small(for spanish, as is my native language), and have never experienced major latency when using for globally available voice commands. Considering my machine runs an Ivy Bridge processor, using CPU inference, the pricing seems unreasonable.