Introduction
As we know, Mistral AI is one of the buzziest companies in the AI ecosystem. Established in April 2023, the organization was founded by former Meta, OpenAI, and Google DeepMind researchers, namely Arthur Mensch, Timothée Lacroix, and Guillaume Lample.
Recently, it has gained traction in the LLM trend. But why is Mistral in the limelight? This Paris-based AI startup plans to build an alternative to OpenAI and Anthropic. Mistral AI has announced Mistral Large, the latest model from Mistral.
Interestingly, it outperforms Mistral Medium, boasting the ability to handle 32k context tokens. It has native English, French, Spanish, German, and Italian fluency. Intriguing right? It doesn’t stop here! Additionally, Mistral AI has also announced – Mistral Small has been upgraded to a model that surpasses the performance of Mixtral 8x7B while maintaining faster processing speeds.
Lastly, Mistral is unveiling Le Chat, a chat interface currently in beta, built on top of their models. But is Mistral Large just another pretty face, or does it offer genuine advantages for multiple users of different domains? Let’s delve into the model’s capabilities, compare it to other models, and explore its potential impact on our coding endeavors.
![Mistral AI's New Model](https://cdn.analyticsvidhya.com/wp-content/uploads/2024/02/Mistral-Large-An-Alternative-to-ChatGPT_.jpg)
Mistral AI: An Overview
Based in France, Mistral AI has long served as a standard-bearer for the open-source AI movement. However, amid the previous year, the emergence of Mistral marked a significant shift. Until then, Meta Llama had been unquestionably dominant, capturing the spotlight in the entire open-source conversation. A vast community of developers was actively contributing and building upon its foundations. With the introduction of Mistral, a noticeable shift occurred, redirecting attention and influence within the open-source landscape. Mistral drew considerable attention, gradually becoming a focal point in the open-source community.
Nowadays, large language models (LLMs) are a bustling marketplace, with established vendors and new challengers vying for your attention. As learners, we constantly seek tools to streamline workflow and boost productivity. So, when a new LLM named Mistral Large struts into the market, claiming to be a worthy alternative to the popular ChatGPT, it piques our interest.
Mistral Large capacities
To comprehend its capacities, we will compare them with other LLM models.
Mistral Large: Boasting Reasoning and Affordability
Firstly, let’s understand – What exactly is Mistral Large?
What is Mistral Large?
Mistral Large, Mistral AI’s flagship model, boasts unparalleled reasoning capacities and is now accessible on Azure, marking our first distribution partnership. This cutting-edge language model, available through la Plateforme, also excels in top-tier reasoning capabilities. Its versatility extends to handling complex multilingual reasoning tasks, such as text comprehension, transformation, and code generation. It ranks as the world’s second-best API-available model globally, following GPT-4:
Mistral Large is positioned as a competitor to established LLMs like ChatGPT and Claude 2. Its key selling point lies in its purportedly superior reasoning capabilities. Mistral AI claims the model excels in tasks requiring logical deduction, information retrieval, and question answering.
Another potential advantage is its price point. Compared to its competitors, it boasts a significantly lower cost. It comes at a 20% lower cost than the latest GPT iteration, GPT-4 Turbo. In contrast, GPT-4 Turbo, featuring a 128k-token context window, is priced at $10 per million input tokens and $30 per million output tokens. Therefore, it is 20% more affordable than GPT-4 Turbo. It’s important to note that the AI industry experiences rapid changes, with companies frequently updating their pricing structures. We can comprehend that the equivalent service on OpenAI’s GPT-4 can cost up to five times more.
Mistral Large’s Multi-lingual Capacities
Possessing inherent multi-lingual capabilities, it significantly surpasses LLaMA 2 70B on various benchmarks such as HellaSwag, Arc Challenge, and MMLU, particularly excelling in evaluations conducted in French, German, Spanish, and Italian languages.
Mistral Large’s Maths & Coding Capabilities
It demonstrates exceptional proficiency in coding and mathematical tasks, showcasing top-notch performance. The table below details the model’s performance across widely recognized benchmarks, offering a comprehensive evaluation of its coding and math capabilities compared to other leading LLM models.
Beyond Benchmarks: Real-World Performance Matters
While Mistral AI throws around impressive benchmark results, real-world performance truly matters to the users. We need to see how the model fares in tackling the everyday challenges of the user. Independent evaluations and head-to-head comparisons with established LLMs on various coding-related tasks are crucial for making informed decisions.
Remember, the LLM landscape is constantly evolving. Today’s top performer might not hold the crown tomorrow. Continuous development and refinement are essential for any LLM to stay ahead of the curve.
Currently, it is accessible on both La Plateforme and Azure. It is also featured on our beta assistant demonstrator, Le Chat.
Le Chat: A User-Friendly Bridge to Mistral Large
Alongside Mistral Large, Mistral AI introduced Le Chat, their consumer-facing chatbot. Le Chat is a user-friendly interface that allows coders to interact with the powerful AI model without getting bogged down in technical complexities.
First, you must log on to the Le Chat platform to access it.
![Le Chat by Mistral AI](https://cdn.analyticsvidhya.com/wp-content/uploads/2024/02/Le-Chat-1.png)
After creating the account, you will be asked to wait in the queue. ( It usually takes around 1-2 hours).
Here is the interface of Le Chat Mistral:
![Le Chat by Mistral AI](https://cdn.analyticsvidhya.com/wp-content/uploads/2024/02/Le-Chat-2.png)
Currently, the service is free, and users can select from three distinct models: Mistral Small, Mistral Large, and a concise prototype named Mistral Next. It is important to highlight that Le Chat does not have internet access during usage.
- Large – Top reasoning capabilities
- Next – Prototype model with extra concision
- Small – Fast and cost-effective
Choose as per your requirements.
![Le Chat by Mistral AI](https://cdn.analyticsvidhya.com/wp-content/uploads/2024/02/Le-Chat-3.png)
Let’s give Le Chat a prompt: Give me a Python code for bubble sorting
Here is the output with the description:
![Le Chat by Mistral AI](https://cdn.analyticsvidhya.com/wp-content/uploads/2024/02/Screenshot-from-2024-02-27-15-49-28.png)
Ethical Considerations: A Shared Responsibility
As LLMs like Mistral Large become more powerful and accessible, ethical considerations become paramount. We must ensure these models are used responsibly and ethically. Issues like bias, misinformation, and potential misuse require careful attention and the development of responsible AI practices.
The Future of LLMs: Collaboration is Key
The future of LLMs is likely to be collaborative, with different models working together to complement each other’s strengths and address weaknesses. Mistral Large’s emergence adds another player to the mix, potentially fostering a more competitive and innovative LLM landscape. While its long-term impact remains to be seen, its presence serves as a reminder of the dynamic nature of this field and the exciting possibilities it holds for the future of coding.
So, is it a viable alternative to ChatGPT?
![Le Chat by Mistral AI](https://cdn.analyticsvidhya.com/wp-content/uploads/2024/02/2-80-3.jpg)
Like most things in technology, the answer is “it depends.” Both models have their strengths and weaknesses. While its affordability and reasoning capabilities are enticing, real-world performance and ongoing development remain crucial. Ultimately, the best LLM for you will depend on your specific needs, budget, and coding style.
However, one thing is certain: the arrival of Mistral Large injects a dose of healthy competition into the LLM market, ultimately benefiting the coders. With more options to choose from and continuous innovation, we can expect a future where LLMs become increasingly powerful and valuable partners in our coding endeavors. I have also provided the output of both models using the same prompt:
Conclusion
In conclusion, Mistral Large offers a compelling alternative to ChatGPT for developers and coders looking to enhance their AI capabilities. With its advanced architecture, improved accuracy, and enhanced customization options, it is quickly becoming popular for those seeking a powerful and cost-effective AI solution. As the field of artificial intelligence continues to evolve, models like Mistral Large will play a crucial role in shaping the future of AI technology.
You can also read: From GPT to Mistral-7B: The Exciting Leap Forward in AI Conversations