Why we ask more than one model the same question
Ask a frontier model a question and you get one answer, delivered fluently and in the same even tone whether the model is right or guessing. That fluency is the problem. A single reply gives you no sense of how settled the question is: whether any careful reader would say the same thing, or whether this is one plausible take among several.
We built Burst into BusyBot because the way we get that sense is to ask more than one model at once. This post is about how we use it, how we read the answers, and when we don’t bother.
One model is one opinion
GPT, Claude, Gemini and Grok come from different companies, are trained on different data and are tuned toward different defaults. On the same prompt they often agree on the substance and differ on emphasis: one leads with caveats, another with a recommendation, another with a worked example. Sometimes they differ on the substance itself, and those are the cases worth knowing about.
We are not going to tell you which model is best. That changes from release to release and from task to task, and any ranking we printed here would soon be out of date. The point is narrower. From a single answer you cannot see the spread. You see one draw and have to guess what the others would have looked like.
What Burst does
Burst sends one prompt to several models in parallel and streams their replies side by side, so the comparison is the output rather than something you assemble by hand across browser tabs. You pick a squad by intent. Lite runs each provider’s fastest, cheapest model; Regular runs the flagship daily drivers; Reasoning runs the extended-thinking models; Custom lets you choose the line-up yourself. You read the answers as they arrive and keep the one you like best.
Every model in a squad is metered on its own, because every one of them really ran. A Burst therefore costs more than a single chat turn, and the pricing page shows how tokens are charged on each plan. That cost is a good reason to be deliberate about when you use it.
How we read side-by-side answers
Several answers at once can be more tiring to read than one, so we read them in a fixed order.
First, the bottom line. Before reading any of them closely, find each model’s actual answer to the question you asked. Do they land in the same place? If they all do, you have already learned something, and the remaining work is choosing the clearest explanation.
Second, the assumptions. When answers differ, the cause is often an assumption rather than a fact. One model reads “customers” as active subscribers, another as everyone who has ever paid. One assumes you are on the latest version of a library, another does not. The answer that states its assumptions is usually the more useful one, even when it is not the one you end up keeping.
Third, the claim only one model makes. A specific fact, number, name or citation that appears in one answer and nowhere else deserves a check before you rely on it. It may come from the best-informed answer in the set. It may also be invented. The side-by-side view does not tell you which, but it does tell you where to look.
Last, length and tone. A longer answer is not a better one, and a confident tone is not evidence. We try to judge the answers on what they claim, not on how they sound.
When disagreement is the signal
Agreement is weaker evidence than it looks. Models are trained on overlapping material, so they can share a common misconception and repeat it in slightly different words. When the question is a matter of fact that matters, agreement is a reason for some confidence, not a substitute for checking.
Disagreement is more informative, and it usually comes in one of three kinds.
- Factual disagreement. The models give different dates, figures, function names or legal positions. At least one of them is wrong. Check against a source before going further; inside a workflow, a grounded search node such as Gemini Google Search returns an answer together with the URLs it came from.
- Interpretive disagreement. The models answered different questions, because yours could be read more than one way. This kind is useful in a direct way: it tells you how to fix the prompt. Rewrite it to rule out the readings you didn’t mean, and run it again.
- Disagreement of judgement. The models weigh the same facts differently, for example on which of two designs to choose. There is no error to find. The spread is the result: it shows you the trade-offs, and the decision stays with you.
When one model is enough
Most of what we ask a model does not need a second opinion. Rewriting a paragraph, reformatting data, drafting an email, explaining an error message you can test straight away: if you can check the answer yourself faster than you can read several of them, use one model. For that kind of work we stay in Chat.
We reach for Burst when an answer is hard to check, when being wrong is expensive, or when we suspect the question is ambiguous and want to find out how. A Lite squad is the inexpensive way to run a quick check; Reasoning is for questions with real steps in them.
Carrying on with the answer you trust
Comparing is only worth it if you can act on what you learn. In Chat you can switch models mid-thread and the context carries over, so every model picks up exactly where the last one left off. One way we use that is to draft with one model, then switch to another in the same thread and ask it to critique the draft. The second model sees not only the question but the draft and everything that led to it.
The same idea inside a workflow
The habit does not have to stay in the chat window. In a workflow, two branches that do not depend on each other run at the same time, so you can send the same input to more than one of the AI nodes, one per provider, and bring their outputs back together with Merge. If each branch uses a structured-output node, the answers come back as fields rather than prose, and an If node can compare them and send the items where the models disagree down a separate branch for review.
That is the whole idea, made automatic: when the models agree, carry on; when they don’t, stop and look.
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