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Earlier quoted context omitted.
Better than Qwen? I guess the best overall is Gemini, right?
Gemini is the worst
https://generativehistory.substack.com/p/gemini-3-solves-han...
Which one's the best?
Don't sleep on Mistral. Highly underrated as a general service LLM. Cheaper, too. Their emphasis on bespoke modelling over generalized megaliths will pay off. There are all kinds of specialized datasets and restricted access stores that can benefit from their approach. Especially in highly regulated EU. Not everyone is obsessed with code generation. There is a whole world out there.
I also think that this is the best approach for businesses wanting to adopt AI to automate, streamline, etc their business. The problem they have is that this is not a moat - their approach is easily reproducible. If they can pull ahead in having the most number of pre-trained models (one for this ERP, one for that CRM, etc) and then being able to close sales to companies using these products and sell them on post-tr…
My 2ct: Currently the moat may be that they are not US-American which is not reproducible by any of the US alternatives.
Don't sleep on Mistral. Highly underrated as a general service LLM. Cheaper, too. Their emphasis on bespoke modelling over generalized megaliths will pay off. There are all kinds of specialized datasets and restricted access stores that can benefit from their approach. Especially in highly regulated EU. Not everyone is obsessed with code generation. There is a whole world out there.
> Their emphasis on bespoke modelling over generalized megaliths will pay off. Isn't the entire deal with LLMs that they are trained as megaliths? How can bespoke modelling overcome the treasure trove of knowledge that megaliths can generically bring in, even in bespoke scenarios?
When generating images most services will have a small agent that rewrites your request and hands it off to the generative image model.
So from the treasure trove point of view, optimized agents have their place. From companies building pipelines, they also have their place.
I like a lot what they are doing and I'll be watching them a lot more closely. I'd love to work for them btw!
Earlier quoted context omitted.
> Their emphasis on bespoke modelling over generalized megaliths will pay off. Isn't the entire deal with LLMs that they are trained as megaliths? How can bespoke modelling overcome the treasure trove of knowledge that megaliths can generically bring in, even in bespoke scenarios?
ChatGPT is already a small agent that receives your message and decides which agent needs to respond. Within those, agents can have sub agents (like when it does research). When generating images most services will have a small agent that rewrites your request and hands it off to the generative image model. So from the treasure trove point of view, optimized agents have their place. From companies building pipelines,…
Right, but this was done to value-optimize the product, i.e. try to always give you the shittiest (cheapest) model you can bear, because otherwise people would always choose the smartest (most expensive) model for any query.
Taking away the model choice from the user introduces a lot of ways to cut down costs, but one thing it does not do is make the product give users better/more reliable answers.
Earlier quoted context omitted.
I also think that this is the best approach for businesses wanting to adopt AI to automate, streamline, etc their business. The problem they have is that this is not a moat - their approach is easily reproducible. If they can pull ahead in having the most number of pre-trained models (one for this ERP, one for that CRM, etc) and then being able to close sales to companies using these products and sell them on post-tr…
> The problem they have is that this is not a moat - their approach is easily reproducible. My 2ct: Currently the moat may be that they are not US-American which is not reproducible by any of the US alternatives.
I like Mistral, it hits the exact sweet spot between cost and my data staying in the EU, withouth a significant drop in quality, but man are their model naming conventions confusing af. They mention they have a model called Devstral 2, which is neither Codestral nor Devestral. I want to use it, but the api only lists devstral-2512, devstral-latest, devstral-medium-latest, devstral-medium-2507, devstral-small, devstra…
Or it can just be a Google like problem where a big company one part doesn't talk to the other.