Why in the name of fuck is Gemini 3.1 Flash Lite priced at $1.50/M output tokens? lil bro it's not that good 🥀🥀
Volla... 🙄
funny how it's the same 3 companies that come up with stuff like that every single time
I'm really, really looking forward to Deepseek V4! Let's just hope it releases soon, because the competition is evolving a lot right now...
We're happy to announce a long-term partnership with Motorola. We're collaborating on future devices meeting our privacy and security standards with official GrapheneOS support.
motorolanews.com/motorola-thr...
There's just no better option security-wise. But rest assured, they are working on their own phone together with a large OEM, (likely) coming 2027.
Flash (Fast) avoids mistakes a pure instruct model would never be able to avoid with the current SOTA. Even situations where way too much relevance would be put onto the first token of the response - and where every other instruct model fails - are handled well by Flash.
Gemini 3 Flash (Fast mode) is literally just a reasoning model that pretends like it's not and any comparison between instruct models is inherently unfair. Even minimal reasoning is still reasoning and you can clearly feel the difference in the response quality my opinion.
Graphene Is All You Need.
Every VLM Implementation except for Qwen's and Gemini's feels botched.
Update, support for the model improved with newer versions of llama.cpp and hits >60 t/s decode speed now.
I still don't believe this thing will run well on a phone though.
Doubling the number of active parameters but cutting the number of experts in half feels arbitrary and has not shown an improvement in output quality so far. The model runs slower though.
We'll have to see what Magistral can make out of it.
I'm glad the RL slop has been reduced in the Ministral series of models. The models don't perform absolutely SOTA, but at least they do not seem to spam \boxed for every single problem now.
Their Magistral pipeline could probably continue to scale extremely well in the future though.
With enough modifications made to it, I'm certain Mistral could create an entirely new high-performance LLM from it.
They could also try to begin tackling problems like hallucinations with their own research, one of the most pressing issues in LLMs.
What Mistral needs to replicate is the quality of ML research; there is little use in training a European LLM just for it to perform worse than the counterparts it took it's technologies from.
Furthermore, efforts in reverse-engineering GPT-OSS seem to be worth a shot.
I'm slightly disappointed in Mistral Large 3 being solely based on a recycled Deepseek architecture with minor changes.
Mistral has a lot of potential, and while trying out custom architectures is risky for smaller ML startups, it's the only way to remain independent in the long term.
Meta will probably be making a much bigger comeback with their future Llama-series LLMs than Google did with Gemini 3, at least towards the technical community.
My stance on OpenAI for building the most "reliable" LLMs prevails. They just work most of the time.
I genuinely thought Google could do better. They have an absurdly good vertical integration. They are literally the perfect candidate for building LLMs due to their sheer dataset size, compute capability and top-tier researchers.
The model still makes such stupid mistakes I do not find myself using it at all anymore, not even for Nano Banana Pro whose capabilities have been severely over-hyped.
Nothing might come close to Gemini 3 in helping the AI bubble pop.
I can't help but think that Gemini 3 Flash has been even more benchmaxxed than other models...
Besides that, they waited way too with publishing Gemini 3. The models are only barely SOTA a few weeks after their initial release. What was the point of all that?!
Brace,
Mistral might be dropping bombshell LLMs very soon. 3B, 8B, and one proprietary.
THEY FUCKING FIXED IT
Yep, that's what I meant to say - device support will come eventually, but Android 16 QPR1 releasing doesn't go hand in hand with that immediately. :)
You will likely need to wait for a bit longer than for the generic QPR1 release until everything regarding the Pixel 10 is sorted out. I might be wrong though.
As far as I am aware, Android 16 QPR1 being ported doesn't necessarily mean Pixel 10 device support is coming. Google dropped the AOSP device source trees for their new phones, which not only made the jump to Android 16 harder but also hindered Pixel 10 adoption.
Still the case btw
I know the Artificial Analysis Index can be inaccurate as hell
But boy, is that a hell of a beautiful sight.
Still the case btw
YouTube is rage baiting everyone, yet again:
They finally introduced a selector on MOBILE WEB for the "Audio track" which allows you to get rid of the terrible auto-translation... BUT you can only use this for Shorts. Not for full videos. They don't have that option there.
How the fuck.
