Over the course of the week, the battle between closed-sourced vs open-source titans intensified, all in the name of “build it together” and “make models more accessible”. OpenAI released GPT-4o mini on 18th July, Meta released Llama 3.1 405B on 23rd July, and Mistrial released the large2 model on 24th July

Apparently, everyone is rallying for developers’ attention, gunning for apps to use their models. Motives aside, what are the key differences between these models?

This article provides analysis of all three models and suggestions in terms of the top use case and, as well as a glimpse into the East with a prediction of what might be on the horizon for the Chinese LLM scene.

GPT4o mini –  OpenAI’s most efficient AI model to date

1. Designed for low latency and high throughput, enabling real-time applications like customer support chatbots and automated documentation
2. Model Size: While the exact parameter count is not specified, it’s described as a “small model” compared to larger versions like GPT-4.
3. Modalities: Currently supports text and vision inputs, with plans for audio and video support in the future.
4. Safety Features: Integrated safety measures to resist jailbreaks, block prompt injections, and prevent system prompt extractions.
5. Pricing: $0.15 per million input tokens and $0.60 per million output tokens

LLama 3.1 405B –  Meta’s largest AI model to date 

1. It was trained on over 15 trillion tokens using 16,000 Nvidia H100 GPUs.
2. The model supports eight languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.
3. Enhanced reasoning and problem-solving skills
4. Long-form text summarisation and advanced conversational abilities
5. Meta highlights “Developers can run inference on Llama 3.1 405B on their own infra at roughly 50% the cost of using closed models like GPT-4o, for both user-facing and offline inference tasks” in its announcement yesterday. 

Mistral Large 2 123B –  Mistral’s (a French startup) latest AI model 

1. Designed for single-node inference with long-context applications in mind,  making it highly efficient and capable of high throughput
2. Known for its strong performance in code generation and math reasoning given and support for 80+ coding languages. 
3. Advanced Reasoning and Knowledge
4. Reduced Hallucinations as it is trained to acknowledge when it lacks sufficient information
5. Free for research and non-commercial usage

Feature/Model	GPT-4o Mini	Llama 3.1 405B	Mistral Large 2
Parameters	Not specified	405 billion	123 billion
Context Window	128,000 tokens	128,000 tokens	128,000 tokens
Languages Supported	50+	Eight	Dozens
Coding Languages Supported	Not specified	Not specified	80+
Language understanding & reasoning score (MMLU)	82%	88.6%	84%
Performance Highlights	Cost-effective, customisable	Reasoning, coding, tool use	Code generation, maths
Commercial Use	Available with pricing	Requires licence for large companies	Requires paid licence
Deployment	Efficient, customizable	Requires multiple GPUs	Single-node inference

Comparison table of GPT-4o Mini vs. Llama 3.1 405B vs. Mistral Large 2  

So what’s the big deal? The no.1 practical use case of the three models.

GPT-4o Mini: Best suited for businesses seeking cost-effective and customisable AI solutions for narrowed task-specific applications. The top use case is edge side chatbots and customer support.

GPT-4o Mini’s low latency and cost-effectiveness make it ideal for developing real-time customer support chatbots, especially on the edge side, e.g. a smartphone. Its strong language understanding and generation capabilities can provide quick, accurate responses to customer queries across multiple languages.

Llama 3.1 405B: Integrated into Meta’s products, Llama 3.1 405B is suitable for advanced reasoning, coding, and multilingual tasks. Its large parameter count and context window make it powerful but resource-intensive. The top use case is synthetic data generation.

Llama 3.1 405B excels at generating high-quality synthetic data, which is particularly valuable for training and fine-tuning other AI models. This capability is especially useful in industries like healthcare, finance, and retail, where access to real-world data may be limited due to privacy and compliance requirements. The model’s large size and extensive training allow it to recognise complex patterns and generate diverse, realistic datasets while preserving privacy.

Mistral Large2: Ideal for applications requiring strong code generation and maths reasoning capabilities. and Its support for dozens of languages and single-node inference design make it suitable for research and non-commercial uses, with potential for commercial applications through a paid licence. Top one use case is advanced code generation and debugging.

Accelerate application development such as rapid prototyping, e.g. generating code skeletons, Code Migration and Refactoring, e.g. Help in translating code between different programming languages. Debugging Assistance: Provides interactive debugging support, helping developers understand and resolve issues more efficiently.

Conclusion 

Each model has its strengths:

• Mistral Large 2: Excels in code generation and maths reasoning with a focus on efficiency and high throughput.
• Llama 3.1 405B: Offers robust reasoning and coding capabilities with extensive language support, ideal for complex tasks.
• GPT-4o Mini: Provides a cost-effective and customisable solution suitable for businesses with specific needs.

A Glimpse into the East 

Whilst this battle of LLM of Titans escalates, the LLM dragons and tigers from the east will surely not be sleeping. The likes of Bytedance, Zhipu AI, Baichun, and Moonshot are all working around the clock to push for their models’ release. Baichuan just announced the closure of its series A raise of $700M to accelerate its model development. A very mysterious and stealthy Chinese model company, Deepseek, released the DeepSeek-V2 model, a 236B MoE open source model, in May that provides a very competitive performance to GTP-4o turbo when it comes to maths and code generation.   

So, my prediction is that there will be an on-par performance model, benchmarking against Llama 3.1 405B, released by a Chinese LLM company in the next three months. And if the name of the race is for developers’ attention and applications that run on these models, considering China has the biggest number of software developers in the world – almost 7 million people, how will this competition evolve in the midst of global AI ecosystem split is yet to be seen. 

An Updated Three-Way Fight: GPT4o Vision vs. Llama 3.2 Vision vs. Mistral Large 2 (Oct 2024)

As a continuation of this comparison series, we relook now in Q4 2024 at newest versions of these three powerful models, each pushing the boundaries of AI applications: OpenAI’s GPT4o Vision, Meta’s Llama 3.2 Vision, and Mistral Large 2. These models are poised to revolutionize the way industries handle text, images, video, audio, and even code generation, with each one excelling in its own niche.

As the AI space grows increasingly competitive, the debate between multimodal all-rounders and specialized tools is more prominent than ever. Should businesses focus on a versatile model like GPT-4o, capable of handling a wide range of inputs, or a specialized one like Llama 3.2, designed for precision in specific tasks? And where does Mistral Large 2, a model with strong code generation capabilities, fit into the equation? In this article, we explore the distinct strengths, use cases, and practical applications of each model to help you decide which is best suited to your needs.

GPT4o Vision

GPT4o Vision is OpenAI’s latest multimodal model, designed to excel at tasks that involve complex, multi-step reasoning. This model can process not just text and images, but also audio and video inputs, making it the most versatile AI model available today. With its large context window of 128,000 tokens and an output capacity of up to 16,384 tokens, GPT4o Vision can handle lengthy conversations, detailed data analysis, and in-depth reasoning tasks.

For businesses that require an all-in-one solution capable of managing different types of inputs across a variety of tasks, GPT4o Vision is the clear choice. However, this versatility comes at a premium, with a pricing structure that could quickly add up for large-scale implementations. Despite this, GPT4o Vision’s strength in fields like healthcare, autonomous vehicle navigation, and content creation make it a worthy investment for companies pushing the limits of AI technology.

Use Cases:

1. Medical Imaging and Diagnostics: GPT-4o Vision analyses complex medical data by integrating text, images, and audio. For instance, in hospitals, it can process medical imagery alongside patient notes to assist in diagnostics, speeding up the identification of abnormalities in MRI or CT scans. Its multimodal capabilities allow for holistic patient record interpretation, improving diagnostic accuracy.

2. Autonomous Vehicle Navigation: In self-driving cars, GPT-4o Vision can process video feeds, sensor data, and even audio inputs to help make real-time decisions about navigation and safety. By integrating video from onboard cameras with sensor data, it ensures that the vehicle can detect obstacles, road conditions, and other variables while on the road.

3. Content Creation: GPT-4o Vision is also a powerful tool for industries like media, where it can generate captions for videos, write scripts based on visual data, and create interactive content that merges images, text, and even audio—perfect for applications like news reporting and digital marketing.

Llama 3.2 Vision

Llama 3.2 Vision, Meta’s latest model, is designed to be a highly efficient, specialized tool for handling text and image inputs. While GPT4o Vision’s strength lies in its multimodal versatility, Llama 3.2 Vision is more focused, excelling at tasks that require precision and efficiency in visual data processing. With 90 billion parameters and a context window of 128,000 tokens, this model is tailored for industries like finance, logistics, and legal tech, where document analysis and image processing are critical.

In particular, Llama 3.2 Vision is ideal for businesses dealing with large volumes of visual and textual data, such as financial reports, legal documents, and logistics charts. Its ability to interpret static images and structured documents at a lower cost than GPT4o makes it a more budget-conscious option for businesses that don’t require the multimodal capabilities of its OpenAI counterpart.

Use Cases:

1. Financial Report Analysis: Llama 3.2 Vision is perfectly suited for industries such as finance, where vast amounts of static data, charts, and graphs need to be processed. A financial analyst could use Llama 3.2 to automatically interpret quarterly financial reports, reading through balance sheets and visualizing key trends like revenue growth and expense allocation. Its precision in chart analysis allows it to generate insights faster and more accurately than manual review.

2. Legal Document Interpretation: For legal firms, Llama 3.2 Vision can analyze contracts and legal documents, flagging critical clauses, compliance risks, or areas requiring further attention. Its ability to understand the structure of legal documents and analyze accompanying visual data, such as charts or tables in reports, enhances productivity for law firms handling large volumes of contracts and case files.

3. Logistics and Supply Chain Management: In industries like logistics, where efficiency is key, Llama 3.2 Vision can process warehouse reports, inventory charts, and shipment records to provide real-time insights. Its visual data interpretation allows logistics managers to optimize routes, minimize costs, and ensure that supply chains run smoothly by detecting bottlenecks in the data.

Mistral Large 2

Mistral Large 2 may not be as widely recognized as GPT4o or Llama 3.2, but it has quickly made a name for itself in the AI community, especially for developers and researchers. This model specializes in code generation and math reasoning, making it particularly useful for industries that rely on programming and complex computational tasks. With support for over 80 coding languages and its ability to function efficiently on a single node, Mistral Large 2 is a cost-effective solution for developers who need an AI model that excels at code generation, debugging, and other long-context applications.

Its free availability for research and non-commercial use also makes it an attractive option for academic and smaller-scale projects. However, for businesses looking to deploy it commercially, a paid license is required.

Use Cases:

1. Rapid Prototyping and Code Skeleton Generation: Mistral Large 2 is highly efficient at generating code from scratch, making it the perfect tool for developers working on rapid prototyping. Whether a startup is developing a new app or a tech company is testing different software approaches, Mistral can quickly generate code skeletons that can be fleshed out into functional applications.

2. Code Refactoring and Migration: For businesses transitioning from one programming language to another, Mistral Large 2 offers valuable support by automatically refactoring code or translating it between languages. For instance, if a company is migrating legacy systems written in C++ to a more modern language like Python, Mistral can assist in ensuring that the migration happens smoothly without introducing errors.

3. Debugging Assistance: Developers can also use Mistral Large 2 to debug complex codebases. The model can analyze the code, identify potential bugs or issues, and even suggest fixes. This helps developers save time by automating part of the troubleshooting process, leading to faster development cycles.

Comparison Table: Key Features of GPT4o Vision vs. Llama 3.2 Vision vs. Mistral Large 2

Real-World Use Cases: Which Model Fits Your Needs?

• GPT4o Vision: Ideal for businesses that need to process diverse data formats (text, images, audio, video) simultaneously. For high-stakes, complex tasks—such as medical imaging, autonomous driving, or real-time video processing—GPT-4o Vision offers unparalleled versatility and performance.
• Llama 3.2 Vision: A budget-conscious option for businesses focusing on static image and text analysis. If your company deals with document interpretation, financial reports, or chart analysis, Llama 3.2 Vision’s precision and cost-effectiveness make it a perfect fit.
• Mistral Large 2: Best suited for developers and researchers looking for strong code generation and math reasoning capabilities. Its support for 80+ coding languages and free research license make it a great tool for academic institutions, startups, and developers working on software projects.

Conclusion: Choosing the Right Model for Your Business

In the battle of GPT4o Vision, Llama 3.2 Vision, and Mistral Large 2, there’s no clear winner—only the best model for your specific use case. If you need a versatile, all-encompassing solution, GPT4o Vision is the Swiss Army knife of AI, capable of handling a broad range of tasks. For businesses focused on document and image analysis at a lower cost, Llama 3.2 Vision is a strong contender. Finally, Mistral Large 2 stands out in code generation and math reasoning, making it the go-to model for developers and researchers.

As we look ahead, the competition between these models—and new entrants from China’s AI scene—will only intensify. With companies like Bytedance and Zhipu AI pushing the envelope, we can expect even more specialized models to emerge. The question is, who will ultimately capture the market’s attention in this rapidly evolving space?

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