MYSTERY SOLVED - [updated]
Thanks to @kerighan I realised I was using a wrong model. I was sure that by using "mistral-large-latest" I'm using ML4! Now when I switched to mistral-large-4 after his suggestions it changed the performance dramatically! It feels like in fact the model is useful and effective. It also explains why GLM-5.3 performed so much better, because I was comparing it against ML3.
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I genuinely wish I could decouple from American tech giants and rely exclusively on Mistral for my daily workflows, but every single time I try, it’s an exercise in frustration. A generous free allowance doesn't matter when a basic task requires endless prompting, burns tokens, and gets progressively worse with each iteration. Still, I find myself coming back every few weeks or months hoping this will finally be the time it clicks.
It never does.
Example 1: The batch file renaming disaster
A few days ago, I tried standardizing the naming conventions of project files across my drive. I wrote a dedicated document with strict, crystal-clear naming rules and fed it to Mistral using Mistral Vibe CLI and later a custom Python script via the API.
After chewing through a huge number of tokens, the result was a chaotic mess: some folders were renamed completely wrong, others were only half-modified, and several were untouched entirely - all while the agent acted like the task was executed flawlessly.
Example 2: The Obsidian vault breakdown
Yesterday, I gave it another shot with my Obsidian vault. I needed Mistral Large to create 41 notes from an index list, distribute text from 9 source files into 27 of those notes based on matching titles, and apply specific formatting from an established note template. I thought nothing can go wrong.
After over an hour of babysitting, I had to throw in the towel. It would modify two or three random files, announce that the entire batch was finished, and repeat the cycle. When I finally forced it to produce the 41 files, the formatting was wildly inconsistent, content was mapped to the wrong notes, and strict instructions were ignored.
Doing both tasks manually copying, pasting, and renaming by hand would have taken me 30 minutes tops. Instead, I wasted hour, burned lots of tokens, and had to revert everything anyway.
The Free Tier dilemma
I’ve been testing this with the €8.50 free API credit. I know free tiers often come with constraints like tighter rate limits, but does Mistral actually serve a degraded or heavily quantized model on the free tier?
If this performance is somehow throttled because it’s a trial, what is the point of offering it? A free tier should demonstrate capability and convert people into paying customers. Instead, it leaves such a sour taste that I immediately run back to Claude or Gemini, feed them the exact same prompts, and watch them execute complex multi-file agentic loops on the first try without hallucinating completion.
I want to love Mistral so much, but it feels like it actively goes out of its way to make itself impossible to use for serious workflows.
