331–340 of 507 posts
For those curious, on openrouter this is $0.14 input and $0.40 output, or ballpark half of Gemini flash lite 3.1 (googles current cheapest current gen closed model)
The wait is finally over. One or two iterations, and I’ll be happy to say that language models are more than fulfilling my most common needs when self-hosting. Thanks to the Gemma team!
Now that coding agents are a thing my frame of reference has shifted to where I now consider a model that can be that my most common need. And unfortunately open models today cannot do that reliably. They might, like you said, be able to in a year or two, but by then the cloud models will have a new capability that I will come to regard as a basic necessity for doing software development.
All that said this looks like a great release and I'm looking forward to playing around with it.
For the first time ever, a Chinese lab is at the frontier. Google and Nvidia are significantly behind, not just on benchmarks but real-world performance like tool calling accuracy.
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There are issues with the chat template right now[0], so tool calling does not work reliably[1]. Every time people try to rush to judge open models on launch day... it never goes well. There are ~always bugs on launch day. [0]: https://github.com/ggml-org/llama.cpp/pull/21326 [1]: https://github.com/ggml-org/llama.cpp/issues/21316
What causes these? Given how simple the LLM interface is (just completion), why don't teams make a simple, standardized template available with their model release so the inference engine can just read it and work properly? Can someone explain the difficulty with that?
[0] https://huggingface.co/google/gemma-4-31B-it/blob/main/token...
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Reversing the X and Y axis, adding in a few other random models, and dropping all the small Qwens makes this worse than useless as a Qwen 3.5 comparison, it’s actively misleading. If you’re using AI, please don’t rush to copy paste output :/ EDIT: Lordy, the small models are a shadow of Qwen's smalls. See https://huggingface.co/Qwen/Qwen3.5-4B versus https://www.reddit.com/r/LocalLLaMA/comments/1salgre/gemma_4...
Small qwen models are magical
I have an app I've been working on for 2.5 years and felt kinda stupid making sure llama.cpp worked everywhere, including Android and iOS.
The 0.8B beats every <= 7B model I've used on tool use and can do RAG. Like you could ship it to someone who didn't know AI and it can do all the basics and leave UX intact.