We Tested Every Major AI Chatbot Before Building Labiba. Here’s What We Found.

Before we built Labiba, we used every major AI chatbot we could find. Not as developers poking at APIs, as regular users. Same questions, same conditions, same frustrations. We wanted to understand exactly what was broken before we committed to building something new.

What we found wasn’t surprising in retrospect. But seeing it laid out across hundreds of real interactions made the problems impossible to ignore, and gave us a clear picture of what Labiba needed to be.

This is that story.

The First Problem Was Just Getting Started

We tested ChatGPT, Gemini, the DeepSeek chatbot, and a handful of smaller tools. The first thing we noticed had nothing to do with AI quality. It had to do with registration walls.

ChatGPT lets you start without an account these days, for a few messages, until the popup appears asking you to sign up to continue. Gemini requires a Google login from the first question. Some tools asked for a phone number. Others wanted you to verify an email before you could ask anything at all.

We watched this happen to people around us. A student would open a chatbot to get help with homework, see the “create your free account” screen, and close the tab. Not because they were opposed to accounts, just because that friction, right at that moment, was enough to lose them. They didn’t come back.

That’s the first thing we decided Labiba would never do. No registration. No email. No phone number. Open the page, ask your question, get your answer. A genuinely AI chatbot with no sign up required, not “free with a hidden wall three clicks in.” Instant, from the first second.

Math Was Broken in Subtle Ways

We put a lot of math questions through every chatbot we tested. Quadratic equations, unit conversions, calculus, word problems. The issues weren’t always wrong answers, sometimes the bigger problem was how the right answer was presented.

Several tools would give you the correct result written as a string of symbols crammed into a sentence. x = (-b ± sqrt(b²-4ac)) / 2a is technically correct. But it’s not how math is supposed to look, and for a student trying to follow along, that presentation gap matters. A formula rendered properly in a textbook format is easier to understand, easier to copy, and harder to misread.

We also found accuracy gaps. Across roughly forty math questions, some chatbots gave confident wrong answers, no hedging, no “you may want to verify this,” just a clean incorrect result presented as fact. That’s worse than a vague answer. A vague answer prompts you to check. A confident wrong answer doesn’t.

We built Labiba to render equations properly and to get the math right. Math is one of the most common reasons students and professionals reach for an AI chatbot. Getting it wrong, or getting it right in an unreadable format, wasn’t something we were willing to accept.

Languages Were Where the Competition Really Fell Apart

Labiba was always going to be a global product. We’re a Canadian company and from the start we knew our users would be asking questions in Arabic, French, Spanish, Norwegian, Japanese, and dozens of other languages. So we tested the competition hard on this.

The major chatbots handled the big European languages reasonably well. French, Spanish, Italian, broadly fine. But as we moved into Arabic, Norwegian, Icelandic, and other languages with smaller user bases, the gaps opened up.

The most consistent failure we saw wasn’t in the first reply. It was in follow-ups. When someone asks a follow-up question using just a short phrase, “tell me more,” “expand on that,” “what else?”, written in their language, a chatbot needs to understand from context that the conversation is still in that language, even though the prompt itself is only three words long. Several of the tools we tested would slip back into English at this point. The user would ask a follow-up in Norwegian. The chatbot would answer in English. Not because it couldn’t speak Norwegian, it could, but because it lost the thread when the message got short.

For a native English speaker, this is an annoyance. For someone who relies on their own language to understand a complex topic, it’s a dealbreaker. We saw it happen repeatedly across multiple platforms and we made fixing it a core requirement for Labiba.

Long Conversations Exposed Who Actually Understood Context

We ran extended conversations, fifteen, twenty back-and-forth exchanges, on a single topic, intentionally building on each answer. We wanted to see which chatbots would hold the thread versus which ones would start treating each message as if the conversation had just begun.

The better tools held context well. The weaker ones would drift, giving generic answers by message ten that ignored everything established in messages two through nine. This is frustrating in a way that’s hard to describe until you experience it. You spend several exchanges building toward something and then the chatbot forgets the foundation and starts from scratch.

Context retention across a full conversation is one of the things we spent significant time on when building Labiba. The goal was for someone to be able to explore a topic deeply, asking follow-up after follow-up, and have Labiba remember the whole thread, not just the last message.

Speed Was a Differentiator Nobody Talked About

The tools we tested varied a lot in response speed, and nobody seemed to talk about this in reviews. Benchmarks measure accuracy. Marketing talks about features. Nobody published a “which AI chatbot actually feels fast” comparison.

But speed is something users feel immediately. A three-second wait for a simple factual question is noticeable. A one-second response to the same question feels like a different product. We noticed this in our own usage and we made sure Labiba’s response time stayed fast, not at the expense of quality, but as a deliberate goal alongside it.

What We Built Labiba to Be

After weeks of testing, the picture was clear. The major AI chatbots had real strengths, they’d invested enormous resources in training and infrastructure, and it showed. But they also had consistent gaps, and those gaps weren’t random. They clustered around the same things: registration friction, multilingual follow-up handling, math presentation, and long-conversation drift.

Those gaps became Labiba’s design requirements.

No sign-up, ever. Proper math rendering with accurate answers. Consistent multilingual support, including short follow-ups in any language. Context retention across long conversations. And speed that makes the whole experience feel immediate rather than patient.

We also made a decision about the business model that we think matters: Labiba is ad-supported, not because it’s a hook to sell you a subscription. The ads are there, they’re clearly there, and if you use an ad blocker you still get a generous number of questions before we ask anything of you. The model is honest. You get AI, we get to keep the lights on, nobody pretends otherwise.

The pattern holds across every row: Labiba is the only option that combines instant access, no registration, strong multilingual support, and built-in safety filters in one product. The bigger tools do some things better in isolation, usually behind a paid plan. We built Labiba for everyone who does not want to pay or sign up just to get a good answer.

Where Things Stand Now

We’ve been running Labiba on real traffic across fourteen languages for months. The things we set out to fix, the multilingual follow-ups, the math rendering, the no-registration experience, are working the way we designed them to.

That doesn’t mean we’ve beaten the competition on every dimension. The biggest AI labs have resources that dwarf ours, and there are things they do exceptionally well. We’re not claiming otherwise.

What we’re saying is this: the specific problems that frustrated us as users, the ones we documented across hundreds of test questions, are the ones Labiba solves. And for the people who run into those problems most often (students, non-native English speakers, anyone who doesn’t want to hand over an email address just to ask a question), we think that matters more than any benchmark score.

If you haven’t tried it yet, the page is right there. No account, no setup, no catch. Just open it and ask something.

Try Labiba, no sign up required →