@TheSpacerr Claude's model is very expensive, and when I'm conducting security audits, I often have to switch to other GPT or Grok models, which means I have to split up many workflows, adding a lot of extra work.
3)
- Hybrid attention + 48-layer low-VRAM recurrent state for extremely low long-text inference cost Perfect for developers who need maximum cost efficiency on long-code understanding and long-document reasoning.
https://t.co/ZaUR8iPkjw
2) **Key features:**
- 14.2 GB weights with native 262k ultra-long context
- Built-in MTP draft head + NVFP4 KV cache keeps full 262k context in VRAM on a single 24GB card
That's crazy! I'm really curious, this looks more like it's being handled by visual image recognition. This might be the most anticipated top tier model in recent times.😳
Qwen3.8-Max-0902 flexes into #1 with 2.4T params and 1M context, coding & cowork on steroids, $2/$6 per mil, now live and ready to cook real work while others still buffering.
@cb_doge Physical AI is the most important area of focus for the next five years; it will determine whether autonomous driving can be used safely on a truly large scale and replace menial, mechanical tasks.
Teams across Google have been building with (and loving) Gemini 3.7 Flash.
We’re seeing:
💻 Real-time website generators
⚛️ 3D physics simulators
📹 Interactive webcam tools
🐚 Personalized field guides
Take a look at how Googlers are using 3.7 Flash across @GoogleAIStudio, @Antigravity, and @GeminiApp Spark 👇