Yes, 300 MW from SpaceX helps a lot, but I think that’s mainly to support Opus demand, which has grown faster than expected. If Mythos is roughly 5× more expensive to serve than Opus, as the pricing suggests, then 300 MW is nowhere near enough to enable large-scale deployment of Mythos.
As an ordinary developer who relies on a $20–$200/month subscription, I feel disappointed by the release of a paper describing a model that I can’t actually use.
It’s clear that Anthropic has run out of the compute capacity needed to serve Mythos publicly.
They’re using security concerns to mask their inability to deliver the model at scale, while still trying to maintain their lead over OpenAI. As a result, they’ve chosen to release it privately under the banner of an “ethical” rollout.
Opus is definitely in its own league. I use Kimi/Gemini-cli code regularly to save cost and from my experience, Kimi 2.5 is more solid than Gemini Flash 3.0 for coding. While Gemini Flash 3.0 is generally faster, it usually break the syntax and skip important prompt. Kimi 2.5 can write very good code and can plan very well.
Except that, In OpenRouter, Deepseek always maintain in Top 10 Ranking. Although I did not use it personally, i believe that their main advantage over other model is price/performance.
I think the opposite. Having NVIDIA investing in TSMC's bleeding-edge process node should benefit Apple rather than disadvantage.
It means that Apple doesn't have to be sole investor in latest node development which is more harder to justify, especially in the year where smartphone upgrade cycle is slowdown. Having NVIDIA (and AI boom) in the picture should help Apple reduce CAPEX for their semi-conductor investment.
you can easily reach 50$ per day.
by force switching model to opus
/model opus
it will continue to use opus eventhough there is a warning about approaching limit.
i found opus is significantly more capable in coding than sonnet, especcially for the task that is poorly defined, thinking mode can fulfill alot of missing detail and you just need to edit a little before let it code.
Just refactored 1000 lines of Claude Code generated to 500 lines with Gemini Pro 2.5 ! Very impressed by the overall agentic experience and model performance.
To professionals in the field, I have a question: what jobs, positions, and companies are in need of CUDA engineers? My current understanding is that while many companies use CUDA's by-products (like PyTorch), direct CUDA development seems less prevalent. I'm therefore seeking to identify more companies and roles that heavily rely on CUDA.
Gemma 3 is way way better than Llama 4. I think Meta will start to lose its position in LLM mindshare. Another weakness of Llama 4 is its model size that is too large (even though it can run fast with MoE), which greatly limits the applicable users to a small percentage of enthusiasts who have enough GPU VRAM. Meanwhile, Gemma 3 is widely usable across all hardware sizes.