ChatGPT, Claude, Gemini, Llama, DeepSeek, Grok, Claude, Perplexity…and whatnot!
Every week we hear of a new LLM, and existing ones release improved versions frequently due to fierce competition.
We are spoilt by choices; each model excels at something that the others don’t.
Many users stick with ChatGPT, because of its first mover advantage, while others prefer Google Gemini as they are already users of the Google ecosystem.
Should you stick to just one model, or if given the option, would you tap into the strengths of many?
Let’s look at the disadvantages of relying on a single model and the benefits of using multiple models in your workflow.
Reasons for not depending on one single LLM
Below are the top reasons why you should avoid depending on one single LLM.
1. Hallucinations
LLMs are prone to hallucinations. When uncertain, they generate made-up or biased responses instead of admitting that they don’t know. Using multiple models helps cross-check responses and spot inconsistencies.
Take a look at the below examples.
The above example shows how GPT 4o mentions Helsinki as the capital of Australia, which is incorrect.
Look at another example below.
This is my own chat with Gemini, where it said Pete Sampras has won the French Open 5 times, while the truth is that he never won the French Open, as confirmed by ChatGPT.
2. Perspectives and Tones
If you use a single model, you will never know what you are missing. Different LLMs are trained on different data and respond differently to the same prompt. You get various perspectives and creative directions from multiple LLMs.
You may also want variety in the tone while creating content. If you rely on one single model, your messages may start sounding monotonous.
You can instead give the same prompt to multiple models and choose the response you like the most. ChatGPT may sound formal, whereas Grok is known for using slang.
3. Redundancy and Backups
LLM providers have limits on usage. Hit a daily limit or encounter an outage, and your work comes to a standstill, just when you have to submit a proposal to your client. Multiple models offer backups.
4. Different Use Cases of Each LLM
Each model has a different use case and is tuned to perform certain tasks better. For example, while GPT-4 is good at general AI chat and academic writing, a software developer may find Claude Opus better as it is better at complex code generation. If you work on large datasets and need advanced reasoning capabilities, you may want to use Gemini 2.5 Pro Preview.
Your organization will most likely have people with diverse requirements and a preferred model to accomplish their task. Employees with different needs (developers, marketers, analysts), all relying on a single model, may feel unsupported.
A Multi-Chat AI platform is the solution.
Now you know your company needs to use two or more LLMs to support your employees’ diverse tasks. But how do you implement this in a way that’s scalable, secure, and cost-effective?
Would you let each employee purchase individual subscriptions to their preferred platforms and reimburse them for it?
No. That’s not cost-effective. You’ll end up paying for multiple overlapping tools, lose bulk discount opportunities, and create an accounting nightmare with a flood of claims. Plus, there’s no visibility into how the tools are being used.
Would you purchase company-wide APIs and share them with individual users?
That’s a terrible idea. Sharing raw API keys across teams is risky. There’s no usage control and a high chance of misuse or accidental exposure. Once an API key is compromised, your company may incur runaway costs or face security breaches.
Without a unified interface and centralized control, you can’t monitor usage, apply cost limits, or enforce role-based access.
That’s where a multi-chat AI platform comes to your rescue.
Multi-chat AI platforms offer one single interface to access different LLMs like OpenAI, Google Gemini, Claude, DeepSeek, Mistral, and many more. You can interact with your preferred model via the same user interface. You can switch between models, chat with 2 or more different models in parallel, and compare results side-by-side.
How Geekflare AI makes your task easy
Geekflare Connect is a multi-chat AI platform that lets you plug in your API keys and access multiple LLMs through one unified interface across your organization.
For companies, this means:
- Employees can access their preferred models
- Admins can manage access centrally
- API usage can be monitored, and costs can be controlled.
You can even set role-based permissions and limits on usage. Each model has a different pricing for the same number of tokens; therefore, you can restrict someone who is only using AI for generating email copies from accessing higher-priced models that are required for advanced reasoning or code generation.
For users, this means:
- Chat with multiple LLMs using the same UI.
- Organize your chats into folders and share them with your team.
- Collaborate and avoid duplication of efforts (which consumes more tokens).
- Build a knowledgebase across various projects and teams.
- Create your own custom prompt library and share it across your organization.
- Focus on your task and leave the handling of subscription/API keys to the admin.
Chat smarter!
Relying on a single AI model may seem convenient at first, but it limits creativity, introduces risks, and fails to meet the diverse needs of modern teams. Geekflare AI gives your employees access to multiple models while providing admins centralized control over usage, model flexibility, and a consistent AI interface across your company.
