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.
They have released models speficially for programming that ”vibe coding” would be safer.
It's definitely a lot slower, though
There are things GPT 6.0 can accomplish for me that 5.3 was not able to. But there are also things it still fails at, and it doesn't seem to be much better at the big picture. It does spam about 100x more tests though and I wonder if just RLHFing it to test everything constantly is carrying it more. 6.0 writes so many tests and spends so much time verifying it's work in python sandboxes. Slow as hell but it tends to get things right the first time more which is good, I guess. I don't love the thought of a 500loc feature adding +4000loc due to tests though.
And yeah still for some reason they often can't understand how to set up any project locally without handholding, which is something you'd think an LLM would actually be good at
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")