Comment by zorked
6 hours ago
People have been using LLMs for two year. It's not just this week's LLM release that is capable something.
6 hours ago
People have been using LLMs for two year. It's not just this week's LLM release that is capable something.
A capability isn't binary. There is a massive difference between can produce an impressive demo and can reliably complete the task without a human babysitting it.
Yup, new SOTA models especially with high/xhigh/max reasoning too often overengineer solutions, good for benchmarks that usually measure task completion, bad for normal development where you don't want 'rewrite in rust and 1k LOC unit tests' style solutions when agent does mundane bug fixes.
When it comes to mundane bug fixes the value is in actually finding the cause of the bug, and I find SOTA models way outperform smaller ones here. I don't care about their output - I can write the correct 5 line patch myself once I understand what's wrong.
It's not this week's change. Fable was the step change for programming. And most of truly useful and powerful capabilities arrived in the last eight months.
> Fable was the step change for programming.
AFAIK: Mistral does not even try to compete in this field. There are other use cases for LLMs beside coding. As Mistral AI wrote:
> During the first wave of generative AI, the central question was who could build the most powerful model. Organizations and governments are now asking a different one: how to harness the power of AI for their mission-critical needs without surrendering control over the infrastructure and intelligence loop. Demand for that combination of performance with control, choice and independence is growing internationally, as enterprises and governments weigh the long-term technology dependencies, data governance requirements and deployment choices that come with any AI investment.
> Mistral is the only AI company in the world building the full stack required to answer that question: open-weight models, the infrastructure and the compute capacity they run on, and the products that bring them into production; ensuring that customers are never locked into a single vendor's roadmap, pricing or availability.
> Mistral’s full-stack and open approach also allows organizations to build on it without exposing their most valuable data, workflows and institutional knowledge to anyone outside their own walls. That's what makes Mistral’s stack the sovereign AI layer, meaning retaining control across four dimensions: data that stays inside the organization's boundaries, models that are controllable and customizable, compute that is private and predictable, and systems in production that are fully controllable and auditable.
>> Fable was the step change for programming > AFAIK: Mistral does not even try to compete in this field.
They have released models speficially for programming that ”vibe coding” would be safer.
https://mistral.ai/news/leanstral/
People said this for Opus 4.6 too. Every release the models get RLHF'ed into accomplishing a new task and the people who need to do this task think there was a step change.
To be fair Opus 4.6 was genuinely a really good model when it came out, in fact I'm not sure Opus 5 is even any better.
It's definitely a lot slower, though
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If the choice is between Mistral and no AI, I'll take Mistral any day
Even those old llama models were ok for coding
Yes yes they won't be like Claude's fire and forget (until you see how many tokens you burned to write "Hello World")