161–170 of 616 posts
A couple tidbits: > Beyond today’s releases, Gemini 3.5 Pro is currently testing with partners and we plan to make it broadly available as soon as it’s ready. > We have started our most ambitious pre-training run yet, for Gemini 4, and are excited by the progress.
Hopefully 3.5 Pro is soon, and that Gemini 4 can be here end of year and finally have an updated knowledge cutoff.
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could you explain how to optimize prompt caching or point to a doc about it?
Anything Sam Rose is worth reading: https://ngrok.com/blog/prompt-caching but the implementation will be up to your provider and harness, for deepseek, they expose some numbers: https://api-docs.deepseek.com/guides/kv_cache/ and Anthropic has a list of actions invalidating your cache: https://platform.claude.com/docs/en/build-with-claude/prompt... Basically, you avoid anything dynamic: model change, tool change, etc…
If you’re constructing the prompt you don’t have to jam everything together you can arrange it appropriately.
It is both less intelligent and more expensive than GLM-5.2, while being closed weight.
You can get decent open-weight models now. That's not difficult. The difficulty is 1) running them and 2) compliance.
My company runs Claude on GCP's Vertex AI solution. We're in the US healthcare IT space, so the models need to be from somewhere that American healthcare agencies and companies have traditionally been okay with sourcing code from - which means the US, Canada, and maybe Europe. The stuff that handles PHI/PII must be in the US. The expense of hosting is more of a PITA than most customers want to go through this early in the technology's lifecycle, and intelligence gains are simply a matter of degree for most business tasks.
In theory, we could find some open-weight model (likely from China) for our development agentic work and host it anywhere you can host AI models. We don't, though, and I think Google, OpenAI/Microsoft, and Anthropic see that as the core of their business.
I wonder how big the Pro model is that Google is using behind the scenes to train these smaller ones. Going on baseless speculation, the lack of accompanying pro models with these flash releases either means: 1) the model is too big to be economical, 2) google doesn't have the compute to serve the big model, 3) their big model has too many alignment issues to serve to the public. edit: looks like benchmarks are up on…
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Just switch the model, its not that much effort tbh. And u can also get a cheaper model than 2.5 lite for the same intelligence
its not always that simple. dropping in a new model is trivial, but highly specific workflows may rely on specific _invisible_ aspects of a model. when that model gets deprecated, the workflow needs to be rebuilt/re-tuned to work with a different model. google's inability or unwillingness to provide stable timelines for model deprecation makes it risky to build complex workflows using their models
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I am growing tired of these pelicans posts every time a new model is published. Feels to me like low effort personal brand promotion. Just sharing my 2 cents.
I feel the same way. It was fun at first but has gotten tiresome. Does anyone actually use these models to generate SVGs?
But why is this an indication of literally anything else?
Pelicans for 3.6 Flash and 3.5 Flash-Lite (Cyber isn't available to me through the API yet.) https://tools.simonwillison.net/markdown-svg-renderer#url=ht...
I am growing tired of these pelicans posts every time a new model is published. Feels to me like low effort personal brand promotion. Just sharing my 2 cents.
They're easy enough to skip - click the little "-" icon and you'll collapse the entire sub-thread.
My results [0] put Gemini 3.6 Flash at the top.
3.6 Flash high has same $1.5 input price as 3.5 Flash, but output is cheaper from $9.0 to $7.5.
Google said 3.6 Flash is more token efficient, but in my tests it's actually LESS token efficient[1] than 3.5 Flash, so despite the output price reduction, it still costs more.
[0]: https://aibenchy.com/compare/google-gemini-3-6-flash-medium/...
[1]: https://aibenchy.com/compare/google-gemini-3-6-flash-high/go...