Thoughts on the new Mixtral model option in HuggingChat? Is it worth switching to?

I tried Mixtral for similar tasks last week. For summarizing docs, it's a bit more coherent and sticks to the point better than the default. The upgrade is noticeable.

For brainstorming workflows though, I didn't see a huge jump. It's still a bit hit or miss with follow-up questions if you get too abstract. I'd say it's worth switching just for the summarization clarity alone. Have you run any direct comparisons on the same doc yet?

I've been running those direct comparisons for a week, feeding it the same ugly API documentation I'm cursed to parse daily. You're spot on about the summarization clarity. Mixtral seems to extract the actual endpoints and required parameters more reliably, leaving out the marketing fluff the default model sometimes includes as filler.

But that "hit or miss" on abstract follow-ups is the real catch. I've found it falls apart completely if you're trying to get it to iterate on a workflow diagram based on a previous summary. It'll lose the thread, start contradicting itself. For brainstorming, I'm starting to think the model choice matters less than how you structure the prompts. Have you tried feeding it a strict JSON schema before asking for workflow ideas? Forces a bit of coherence back into the conversation.

You've hit on something important with the JSON schema trick. I've found it's a useful scaffold, but it can also limit the model's ability to propose genuinely novel workflow structures it wasn't explicitly prompted for. It becomes a fill-in-the-blank exercise.

For avoiding contradictions in iterative brainstorming, I've had better results with a different method: using the chat history itself as the schema. Before each follow-up, I'll prompt it to "Review the workflow steps we've agreed on so far" and then "List only the points of contradiction or ambiguity in the following new suggestion." It adds steps, but it forces the model to ground itself in the established context.

The contradiction problem you see with Mixtral on abstract follow-ups is amplified when the workflow involves conditional logic or error handling. That's where it really struggles to maintain consistency across turns.

That "review and list contradictions" prompt is a good hack. I've done something similar for outlining integration requirements between tools.

But it makes me question the actual ROI. If we're spending more time engineering prompts to correct the model's inconsistency than we would just drafting the workflow ourselves, the "assistant" part starts to vanish.

For conditional logic, I've stopped using any chat model directly. I get a basic structure, then use the output as a spec for a dedicated diagramming tool. It's a better use of time.

Good timing, I just tried it yesterday for the exact same stuff. For summarizing those dense SaaS docs, it's a clear step up. It pulls out the key limits or API changes without the fluff.

But I'm not sure about brainstorming yet. I asked it to help outline a sprint review process and it kept changing the steps I'd already agreed to. Maybe it's better for one-shot questions than a long conversation.

Did you find it faster or slower to respond compared to the default model? That was my other hesitation.

I've been tracking inference latency across several models for a different project, so I have some relevant data on your speed question. In my tests, Mixtral's response time is roughly 15-20% slower than the default model for equivalent token counts, which aligns with its larger parameter count. Whether that's a "hesitation" depends heavily on your use case; for a one-shot summarization, the extra half-second is negligible for the quality gain, but in an iterative brainstorming session, those delays compound and can disrupt the conversational flow you're trying to maintain.

Your observation about it being "better for one-shot questions" is key. The contradiction issue on iterative tasks, like your sprint review process, stems from how the model manages its internal context window during long exchanges. Each new prompt causes it to re-weight the entire conversation history, which can lead to it "changing the steps already agreed to." The prompt engineering hacks mentioned earlier, like forcing a context review, are essentially attempts to manually re-anchor that attention mechanism, but as user794 noted, the ROI diminishes quickly.

For a sprint review outline, you might get more consistent results by treating it as a single, detailed prompt asking for a complete, static process, rather than trying to co-create it step-by-step in a chat.