281–290 of 616 posts
It is both less intelligent and more expensive than GLM-5.2, while being closed weight.
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I'm a professional blogger now. I still also work on open source software. I'm even fine being called an "influencer" ( shudder ), but I take offense to accusations of unethical behavior. I think very hard about the ethics of what I'm doing and how I can best use my "platform" ( shudder again) in as constructive a way as possible.
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I'd rather you do this than him. Chill out, please. The pelican pic is fine.
I have a side business selling custom fingerprint jewelry and I use gemini nano banana to clean up customer submitted fingerprint images. This was a step I used to do by hand at 10 - 15 minutes per image and nano banana is the first model that is able to do the task (it is astonishingly good at it). I can't wait to see what the next nano banana can do, hopefully its released soon.
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…
I wonder if the broad use of AI overviews on Google search results is having an impact. Maybe the numbers make it more profitable to use their compute on several billion searches a day rather than selling API access.
It's a bit disheartening to see no comparison to other models here - and I'm not sure this pushes the curve anywhere. 3.6 flash is more expensive than GLM 5.2 - but seemingly worse, although this post is really light (lite?) on details. It seemed for a time that Google had finally gotten the ball rolling, but I'm doubting that more and more as time passes. We'll see what happens with 3.5 pro I suppose.
Here, my comparison of 3.6 Flash vs Sol vs Luna vs Terra: https://aibenchy.com/compare/google-gemini-3-6-flash-medium/...
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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.
Vibe code an extension that autocollapses any post mentioning pelicans and by simonw?
It's scary relying on Google's models. I have a very price sensitive workload that used to run on flash 2.5 lite - it's deprecated now. The replacement 3.1 flash lite is a lot more expensive, but now also has a sunset date. 3.5 flash lite is even more expensive. So the price is rising and you have no choice but to keep paying more and more.
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I moved directly from 2.5 flash lite to deepseek v4 flash, its already cheaper and if your prompt caching is good you can save so much more money.
could you explain how to optimize prompt caching or point to a doc about it?
All of the models, you need to have a consistent input to get the cache hit. So if you are chatting with a document, and change the system prompt, it will be a cache miss, even if the rest of the items are all the same. If you even pass in the document in not the same order as the prompts, it will be a cache miss. Or if you add tool calls or structured outputs, it will be a cache miss. (Since those generally go at the beginning of the prompt call, not at the end.)
Most of the time when reading documents from URLs directly it will never cache. (Need to typically pass in the bytes directly, or use the provider document store index.)
Gemini has a 4096 minimum token size with the 3 version models before even getting a cache hit. OpenAI it is lower (1024), and is automatic, but only happens in increments of 124. Anthropic can also get cache hits at 1024 tokens, but you need to explicit ask for it (and pay extra).
Caching by default typically lives for 5 minutes since the last cache hit across providers. But some of them you can ask for longer. AWS for Anthropic models can be tricky with multiple endpoint routing, so can get cache misses if it happens to route to a different endpoint.