GPT-4 and ChatGPT Open-Source Alternatives: LLaMA 3 and Mixtral 8x7b

In this blog article, we explore GPT-4 and ChatGPT open-source alternatives: LLaMA 3 and Mixtral 8x7b. These cutting-edge language models are making waves in the AI community and are paving the way for more efficient and effective natural language processing. Join us as we delve into the features and capabilities of these up-and-coming models and how they compare to their more well-known counterparts.

LLaMA 3 and Mixtral 8x7b

The ChatGPT / GPT-4 Breakthrough

ChatGPT and GPT-4 are advanced language models developed by OpenAI. ChatGPT is a conversational AI model that uses natural language processing to generate human-like responses to user inputs, while GPT-4 is a more powerful and complex model capable of generating text that is virtually indistinguishable from human writing.

Both models have been trained on vast amounts of text data, allowing them to generate highly accurate and contextually appropriate responses to a wide range of questions and prompts. They have a large range of applications in areas such as customer service, content generation, and language translation, and are continuing to evolve and improve as technology advances.

The Limitations of ChatGPT and GPT-4

While OpenAI has undoubtedly revolutionized the field of artificial intelligence, particularly in the realm of natural language processing, their models do have some drawbacks when compared to open-source alternatives like LLaMA 3 or Mixtral 8x7b.

One major drawback is the cost associated with using OpenAI's services, as they require a subscription or payment per usage, which can be prohibitively expensive for some individuals and organizations.

Another concern about ChatGPT and GPT-4 is the data privacy aspect: OpenAI does not offer strong guarantees about how the customer's data is processed, which is a problem for sensitive applications like medical or financial applications.

Last of all, OpenAI has implemented content restrictions on ChatGPT and GPT-4 to ensure that the AI-generated text adheres to their guidelines, by monitoring and regulating the content generated by their models. Some use cases are simply not compatible with OpenAI's models and some think that these restrictions make ChatGPT and GPT-4 less original and accurate than their unrestricted counterparts.

Let's see which options you can consider as alternatives to ChatGPT and GPT-4.


The LLaMA 3 model family, released by Meta, serves as the successor to the original LLaMa 1 models, providing both base foundation models and fine-tuned "chat" models. Unlike the LLaMa 1 models released in 2022 under a noncommercial license, LLaMA 3 models are available for free for both AI research and commercial use.

Meta's Llama models aim to democratize the generative AI ecosystem by making the code and model weights freely available, and focusing on advancing the performance capabilities of smaller models instead of increasing parameter count. With 7 billion, 13 billion, or 70 billion parameters, smaller organizations can deploy local instances of LLaMA 3 models or Llama-based models developed by the AI community without requiring expensive computing time or infrastructure investments.

In comparison to its proprietary counterparts, LLaMA 3 demonstrates superior performance in aspects such as safety and factual correctness. While LLaMA 3 might not possess the comprehensive abilities of much larger models, its open nature and increased efficiency offer distinctive benefits.

LLaMA 3 can either be deployed manually on-premise, or used through a dedicated API like NLP Cloud.

Mixtral 8x7b

Mixtral, released by the French startup Mistral AI, is a network that combines the functionality of multiple experts into a single model. It is a decoder-only model, meaning it only decodes information, not encodes it. Within the model, there are 8 different groups of parameters, and at each layer and for each token, a router network selects two of these groups to process the token and combines their outputs.

This approach allows the model to increase its number of parameters while still controlling cost and latency, as only a fraction of the total set of parameters is used per token. For example, Mixtral has 46.7 billion total parameters, but only 12.9 billion are used per token. This means it processes input and generates output at the same speed and cost as a 12.9 billion parameter model.

In comparison to other models, Mixtral outperforms LLaMA 3 70B on most benchmarks with 6x faster inference. It is the strongest open-weight model with a permissive license and offers the best cost/performance trade-offs. It matches or outperforms GPT3.5 on most benchmarks.

Mixtral 8x7b can either be deployed manually on-premise, or used through a dedicated API like NLP Cloud.

How to Use LLaMA 3 and Mixtral 8x7b?

Large Language Models like LLaMA 3 and Mixtral are interesting options because you can either deploy them by yourself or leverage an AI vendor that provide these models out of the box.

Deploying LLaMA 3 and Mixtral by yourself can be interesting if you have the right devops and AI skills in your team, and if you are lucky enough to have access to the right hardware. It will allow you to maintain advanced data privacy for your application since you won't have to share your data with a cloud provider.

Keep in mind that deploying an generative model can be tedious though, and maintaining such LLMs so they can behave reliably in production is even harder. Finding the right engineers for such a job can be challenging. For instance, the hardware requirements to install LLaMA 3 70b in fp16 mode without quantization will be at least 140GB of vRAM. Given the current high demand on NVIDIA GPUs, provisioning advanced GPUs with 140GB or vRAM is very complex.

If you prefer to use LLaMA 3 or Mixtral through a managed AI API that does not sacrifice data privacy, we encourage you to try our NLP Cloud API. (See NLP Cloud's generative AI API here)! And you can also fine-tune LLaMA 3 and Mixtral 8x7b on NLP Cloud, so the model is perfectly tailored to your use case.

Documentation about LLaMA 3, Mixtral 8x7b, and more LLMs
Documentation about LLaMA 3, Mixtral 8x7b, and more LLMs


GPT-4 and ChatGPT are amazing AI models that really changed the AI game. For the first time in AI history, it's impossible to say whether the generated content is coming from a human or a machine, which leads many companies to integrate GPT-4 and ChatGPT into their product or their internal workflows.

However, GPT-4 and ChatGPT can be disapointing because of their poor guarantees in terms of data privacy, and their use case limitations due to OpenAI restrictions. The open-source community made a great job at designing open-source alternatives to GPT-4 and ChatGPT like LLaMA 3 and Mixtral 8x7b.

If you want to leverage LLaMA 3 and Mixtral, don't hesitate to have a try on the NLP Cloud API (try it here)!

Marketing manager at NLP Cloud