Ever since the introduction of OpenAI’s ChatGPT, it’s AI everywhere. From writing emails to developing software, this AI technology has truly sparked a big revolution in technology. While ChatGPT was the first to allow people to communicate with the computer in human language, it’s for sure not the only one.
In this article, we’ll see much more free & affordable ChatGPT alternatives, exploring their capabilities, expertise, and use cases.
ChatGPT Alternatives
Yes, ChatGPT alternatives. Not some applications built using ChatGPT API, but full-fledged AI models that are being used and developed in the same world. While ChatGPT is the most popular, it’s not the most affordable. As time passes, the free ChatGPT plan is extremely slow that it’s barely usable in working hours.
To use the faster model, one needs to pay $20/month. The other models in this article are freely available and perform much better than ChatGPT.
1. DeepSeek V2 & DeepSeek Coder V2
Deepseek is an open-source conversational AI model developed by the team at Deepseek AI. This model is designed to be highly customizable to allow developers to fine-tune it for specific use cases and domains. Deepseek’s architecture is based on the popular transformer model, which enables it to understand and respond to complex queries with ease.
Expertise: Deepseek expertise lies in handling multi-turn conversations. That’s why it’s a great choice for customer support, chatbots, and virtual assistants. Its ability to understand context and follow conversations makes it a great alternative to ChatGPT.
DeepSeek Coder V2 is the model that said to perform in the same spectrum as GPT4-Turbo in programming-specific tasks.
The best thing about Deepseek is that, like many others in the list, it’s also an open-source AI model. If one has knowledge and resources, it can also be hosted locally or in the cloud, such as AWS, GCP or any other.
DeepSeek API
DeepSeek is available to be used for free. Just head over to their website and use it. What I like the most about this model is that they also provide an API. So we can integrate it within our applications, and it’s almost half the price of GPT-4o.
2. Phi-3
The next in the list is Phi-3, developed and trained by Microsoft. Phi-3 is the family of several models. If you’re looking to run the model on your laptop or even on a mobile phone, it can fit and perform really well on those devices.
At this time, we have hundreds of AI models available, but it’s not easy to run most of those models on your regular system. For example, in this list alone, we have models that weigh 130GB in size. Running such a model requires higher memory, GPU and CPU power.
At the moment, Phi3’s smallest model, called Phi-3-mini, is a 3.8B parameter language model, and is only 2.2GB in size. Almost capable of running on most modern laptops perfectly fine. The benefits of running smaller models are that they’re faster, and we can train them on our own data. In another article, I will show you how to train AI models easily.
- Phi-3-vision
- Phi-3-Mini – It’s s small 3.8B parameter language model (2.2GB)
- Phi-3-Small – It’s a 7B parameter language model available in two context lengths (128K and 8K)
- Phi-3-Medium – It’s a 14B parameter language model also available in two context lengths (128K and 4K)
Use cases: Phi 3 can be used for building conversational interfaces, creating chatbots for customer support, and developing virtual assistants for various industries. The smaller versions are highly performant and require less memory to run.
3. LLaMA 3
LLaMA 3 is a conversational AI model developed by the team at Meta AI. This model is designed to be highly conversational, with the ability to understand and respond to user queries in a more human-like way. LLaMA 3’s architecture is based on a combination of transformer and language models, which enables it to understand complex relationships between entities and concepts.
Expertise: LLaMA 3 excels in handling open-domain conversations, making it a good choice for chatbots, virtual assistants, and customer support platforms. Its ability to understand nuances and context makes it a great alternative to ChatGPT.
4. Bloom
BLOOM stands out as a significant alternative to proprietary GPT AI models. Developed through the collaborative efforts of hundreds of researchers under the BigScience initiative, BLOOM is a 176-billion-parameter language model designed to be accessible and versatile.
Unlike many large-scale models that remain the exclusive property of resource-rich organizations, BLOOM is openly available, embodying a commitment to democratizing AI technology. This model is a decoder-only Transformer architecture, trained on the diverse ROOTS corpus, which includes data from hundreds of sources across 46 natural languages and 13 programming languages, making it one of the most multilingual models available.
BLOOM’s capabilities extend to a wide array of natural language processing tasks, including translation, summarization, and question-answering, among others. Its training on such a broad and inclusive dataset allows it to perform competitively on various benchmarks, often surpassing expectations, especially after undergoing multitask prompted fine-tuning.
5. Gemma or Gemini
Gemini is a conversational AI model developed by Google DeepMind. This model is designed to be highly conversational, with the ability to understand and respond to user queries. Gemini’s architecture is based on a combination of transformer and language models, which enables it to understand complex relationships between entities and concepts.
Gemini, with its multimodal capabilities, certainly sounds impressive on paper. However, its massive size and the requirement for specialized data center hardware can be a significant barrier for many users. The cost and complexity of deploying Gemini might be overkill for smaller projects or businesses without substantial resources. BTW, it’s not open-source so you won’t be able to host it yourself even you want to or can.
Gemini is built-into Google’s services. Most of you interested in AI models or just using Android would have interacted with Gemini in one or the other. But for developers and those interested in learning about its open-source counterpart (only text-to-text model) from Google, it’s Gemma.
Differences between Gemma and Gemini
Gemma is a more approachable option for those looking to integrate AI into their projects. Gemma is open-source in nature and smaller in size. However, it’s simplicity and portability come with trade-offs. Gemma’s capabilities are more limited compared to Gemini, focusing only on text-to-text tasks.
Recommended Models for Readers to Explore
In addition to the models mentioned above, there are several other ChatGPT alternatives that readers may find useful:
- DALL-E: A conversational AI model developed by OpenAI, DALL-E is designed to generate images from text prompts. Its ability to understand context and generate images makes it a great alternative to ChatGPT.
- Perplexity AI: A conversational AI model developed by Perplexity AI, this model is designed to understand and respond to user queries in a more human-like way. Its ability to understand nuances and context makes it a great alternative to ChatGPT.
- Rasa: A conversational AI model developed by Rasa, this model is designed to build conversational interfaces for various industries. Its ability to understand context and follow conversations makes it a great alternative to ChatGPT.
Conclusion
ChatGPT alternatives are rapidly evolving, with new models emerging every day. Each of these models has its unique strengths and weaknesses, and make them suitable for specific use cases and industries. By exploring these alternatives, developers and businesses can find the perfect model for their needs, unlocking the full potential of conversational AI.
In this article, we’ve explored five ChatGPT alternatives, including Deepseek, Phi 3, LLaMA 3, Bloom, and Gemma/Gemini. Each of these models has its unique expertise, ranging from multi-turn conversations to creative writing tasks. My personal favorite is Phi-3 since its performance is extremely good and I can train it to answer using my data. If you’re using any other model, please let us know in the comment section below.
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