Fresh on r/PromptEngineering: u/wittywander is taking prompt requests. You reply with a prompt, and they run it through Kimi K3, DeepSeek V4 Pro and Qwen 3.5 at the same time. Then they post all three answers, unedited, with the model named on each.
The thread is brand new, with 1 upvote and no comments yet. So there are no outputs to show you. What’s worth a look right now is the idea, and it’s a good one. Early threads like this are also the best time to jump in. Fewer requests in the queue usually means your prompt gets noticed, and your result becomes one of the first reference points other people read.
What’s new
The pitch is simple. Most people compare models by facts: who got the answer right. This author says the real differences show up somewhere else. They point to structure, tone, and what each model decides to leave out. That matches how I’d want to test too. If two models both get the facts right, the one that drops your formatting rule or skips a caveat still costs you time.
Think about your own week. How often does a model give you a correct answer in the wrong shape? A summary that ignores the length you asked for, a list when you wanted a paragraph, a confident tone where you needed hedging. Those misses don’t show up on a leaderboard, but they decide which model you actually keep open in a tab. Three open models side by side, answering the same prompt, is a cheap way to see those habits directly.
The twist
The twist is the unedited part. Most model comparisons you see online are curated. Someone picks the prompt, trims the answers, and the winner is already decided. Here you bring the prompt, and the answers come back as they are. That flips the usual dynamic. The person running the test doesn’t know your prompt in advance, so they can’t tune it toward a favorite.
The author also says up front that they run this in Vresk, a workspace they work on, which has a compare view that sends one question to up to three models. They’re not linking it, and they say the thread is about the outputs. I can’t vouch for the tool, since I haven’t tried it. The disclosure is clear, though, and that’s more than a lot of showcase posts give you. You don’t need the tool to benefit. You just need a good prompt and a few minutes to read the results.
How to use it (mini-workflow)
- 🧪 Pick a prompt you actually use. A system prompt, a strict formatting ask, or a tricky reasoning question all fit what the author invited. Real work prompts beat clever puzzles, because they tell you something you can act on.
- 📝 Post it as a reply in the thread, with your constraints written out. If you need a table, a word limit, or a specific tone, say so. Paste the full prompt rather than a summary, since small wording changes can shift how each model responds.
- 🔍 When the three answers come back, don’t read for “who’s right.” Check what each one skipped, how each one structured it, and whether it obeyed your rules. A quick pass is to scan each answer against a short checklist you wrote before you posted.
- 📌 Note which model drifted from your instructions. That’s the one to avoid for that kind of task. Write down the pattern too, such as “adds an intro I didn’t ask for” or “drops the last constraint,” so you can spot it again.
Pro tips
- Choose a prompt where you already know a good answer. That makes omissions easy to spot.
- Add one rule that’s easy to break, like “no bullet points” or “exactly three sentences.” Instruction-following gaps show up fast.
- Save the three outputs. They’re a free baseline you can reuse when new model versions land. Rerun the same prompt later and compare the old answers to the new ones.
- Write your pass criteria before you see the answers. If you decide what “good” means afterward, you’ll tend to favor whichever response read best first.
- Come back and read the other replies once they appear. Other people’s prompts will show you failure modes you hadn’t thought to test.
Heads up
One run on one prompt tells you about that prompt, not about the models in general. Treat it as a quick sample, and test the same prompt a couple of times if the decision matters. Also keep in mind that these are public replies, so don’t paste anything private, like client details, internal documents, or keys, into the thread.
If you’ve got a prompt you’ve been curious about, go drop it in the thread and see what comes back. 🚀
Drop a prompt and I’ll run it through three open models side by side
by u/wittywander in PromptEngineering