I wouldn't call it "deterministic output" because that's not really what LLMs do, but there are definitely use cases where you need a certain level of consistency and predictability, you know?

Here's what I keep running into with reasoning models:

During the reasoning process (and I know Anthropic has shown that what we read isn't the "real" reasoning happening), the LLM tends to ignore guardrails and specific instructions I've put in the prompt. The output becomes way more unpredictable than I need it to be.

Sure, I can define the format with JSON schemas (or objects) and that works fine. But the actual content? It's all over the place. Sometimes it follows my business rules perfectly, other times it just doesn't. And there's no clear pattern I can identify.

For example, I need the model to extract specific information from resumes and job posts, then match them according to pretty clear criteria. With regular models, I get consistent behavior most of the time. With reasoning models, it's like they get "creative" during their internal reasoning and decide my rules are more like suggestions.

I've tested almost all of them (from Gemini to DeepSeek) and honestly, none have convinced me for this type of structured business logic. They're incredible for complex problem-solving, but for "follow these specific steps and don't deviate" tasks? Not so much.

Anyone else dealing with this? Am I missing something in my prompting approach, or is this just the trade-off we make with reasoning models? I'm curious if others have found ways to make them more reliable for business applications.

What's been your experience with reasoning models in production?