The problem with Google-Translate-type models is the interface is completely wrong. Translation is not sentence->translation, it's (sentence,context)->translation (or even (sentence,context)->(translation,commentary)). You absolutely have to be able to input contextual information, instructions about how certain terms are to be translated, etc. This is trivial with an LLM.
"As a safe AI language model, I refuse to translate this" is not a valid translation of "spierdalaj".
Also the traditional cross-attention-based encoder-decoder translation models support document-level translation, and also with context. And Google definitely has all those models. But I think the Google webinterface has used much weaker models (for whatever reason; maybe inference costs?).
I think DeepL is quite good. For business applications, there is Lilt or AppTek and many others. They can easily set up a model for you that allows you to specify context, or be trained for some specific domain, e.g. medical texts.
I don't really have a good reference for a similar leaderboard for translation models. For translation, the metric to measure the quality is anyway much more problematic than for speech recognition. I think for the best models, only human evaluation is working well now.
Just whatever small LLM I have installed as the default for the `llm` command line tool at the time. Currently that's gemma3:4b-it-q8_0 though it's generally been some version of llama in the past. And then this fish shell function (basically a bash alias)
function trans
llm "Translate \"$argv\" from French to English please"
endWhisper can translate to English (and maybe other languages these days?), too.
There are plenty of uncensored models that will run on less than 8GB of vram.