Welcome to the forefront of conversational AI as we explore the fascinating world of AI chatbots in our dedicated blog series. Discover the latest advancements, applications, and strategies that propel the evolution of chatbot technology. From enhancing customer interactions to streamlining business processes, these articles delve into the innovative ways artificial intelligence is shaping the landscape of automated conversational agents. Whether you’re a business owner, developer, or simply intrigued by the future of interactive technology, join us on this journey to unravel the transformative power and endless possibilities of AI chatbots.
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One way to look at AI is: “What do I do, what’s going to happen with me?” Or you can say: “Here’s a new tool and I can do a lot more, like with any new technological revolution.” You may ask how artificial intelligence can help in daily life. It’s not about using it to write emails that sound clichéd, lacking in the human touch. Where AI can come in use is to summarise a meeting or to draft a complex set of points; it saves a lot of time. At work, it can help with a number of tasks, depending on what you do. For example, it can help with battery design, come up with compositions for new antibiotics, discover tough and durable materials and so on. Of course, your team still has to check every detail of the new battery design and has to have know-how of how to go about it.
At the same time, with the rising quality of OpenAI’s ChatGPT and the likes, it’s becoming increasingly difficult to choose the AI you would like to have on your computer. You can’t pay for five AI chatbots every month. The more you search for answers, the harder it gets.
Some people start by looking at benchmarks, but what could be a good starting point is to consider the problem that you’re trying to solve.
       While GPT and other SaaS-based models (Cloud-based software delivery method) are an easy and fast way to begin prototyping, many need the full control, customisation, and flexibility that an open-source model like Llama or Mistral may or may not offer. No matter what you choose, you’ll need to consider the performance, speed, and price of a model. Others need something closed and proprietary. There’s nothing wrong with that.
There are some trends. For example, higher intelligence typically results in a higher price or higher cost. While at the same time, smaller models might result in faster speeds and lower costs at the same time.
One of the best community-based platforms to evaluate models is Chatbot Arena Leaderboard by UC Berkeley, which combines over a million blind user votes on models to rank them and essentially provide a vibe score. Since benchmarks sometimes can be reverse-engineered by models, Chatbot Arena is a great way to understand what the general AI community thinks is the best model. And this directly correlates to its abilities on reasoning, maths, writing, and more. Further, if you want to compare two different models, go ahead.
For a simple open-source foundation, Open LLM Leaderboard has a wide variety of model metrics and filters in order to understand which model might be best for your specific use case. For example, if you have a GPU or you want to run it locally on your machine or even do real-time inferencing on a mobile or edge device. You can easily select these filters and see the model directly on Hugging Face.
For accuracy, OpenAI’s ChatGPT is generally good, but scoring high points is Perplexity. In the creativity department, ChatGPT does a good job, and Claude is a few steps behind. The model that works the best, most of the time in this scenario, is Midjourney. It’s image-only in Midjourney, which uses a different kind of model.
Coming to research, ChatGPT once again scores high marks but falls behind slightly once Perplexity enters the scene. Gemini? Good, but not as good as the other two.
Talking about language needs, ChatGPT earns four stars, but Gemini and Perplexity are reasonably better. Claude also puts out good results, but it doesn’t score high marks across categories.
Images? ChatGPT scores well, yet again, maybe not five stars, but it’s almost there with the best, which would be Gemini and Midjourney. 
Finally, the retention department, that is, how much a model can remember. Here, OpenAI and Google’s Gemini score very well. OpenAI’s custom GPTs are fantastic.
There’s more when it comes to choosing an AI model. Consider how often new features are shipped, what is available free of cost, and if it’s good for research. We are finding ChatGPT and Gemini to be the most consistent when it comes to offering new feature sets often. Perplexity wins hands down for those who want to do research because of the level of accuracy. Gemini has the advantage of integrating with G Suite. Midjourney is stunning if you are an artist.
What about Copilot from Microsoft and Grok from Elon Musk’s xAI? Despite working with OpenAI, Microsoft’s Copilot is not at the same level as ChatGPT. The answers are somewhat bland and not always up to date. Grok, on the other hand, is too colourful for everyday use. It is not something one can always rely on, especially when you consider the man running the show — the moody Elon Musk. The “personality” of Grok is not something everyone would appreciate.
For deep research, ChatGPT can offer a lot of information without getting into a war of words, which is a failing in the case of Gemini; it can be verbose (read fluff). This is a function that shouldn’t be about how long it takes; consider how accurate the answers are. Perplexity too scores as much as ChatGPT.
Most people have too many options. They think they don’t have enough. So, they’re worried that they’re going to fail by not saying ‘yes’ to everything.
First, audit your task list. You need to look at all the things you are doing each day and how much time these are taking up. Are these tasks draining your energy, and can AI help you with reasonable accuracy?
Next, pick two tools that fit your tasks. If it’s writing emails or generating graphs, consider how AI can help. After you zero in on two options, like Claude Sonnet 4 and ChatGPT-4o, pick the one that feels most organic to you.
Commit to the model you choose for seven days. If you don’t like the model after a week, move to a new model.
There is no way to know if an AI model is thinking of an answer in-depth or has seen the answer a lot of times during training. Many call this intellectual property theft.
A few months ago, OpenAI CEO Sam Altman said: “The creative spirit of humanity is incredibly important and we want to build tools that lift it up, so that new people can create better art, better content, write better novels that we all enjoy. I believe very deeply that humans will be at the centre of that. I also believe that we probably need to figure out some sort of new model around the economics of creative output. I think people have been building on the creativity of others for a long time. But as access to creativity gets incredibly democratised and people are building off each other’s ideas all the time, I think there are incredible new business models that we and others are excited to explore.”
He said we can’t copy someone else’s work, but also asked how much inspiration you can take is morally correct. The question of where that line should be, nobody knows. 
What is particularly interesting at this point is AI for science. The most “important driver” of our lives will get better and help push the frontier of what’s possible. Let’s hope chatbots don’t goof up with the next vaccine or medicine.

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