A Three-Way Fight: GPT-4o mini vs. Llama 3.1 405B vs. Large 2

OpenAI, Meta, Mistral — The Race for Developers

Source: AI Business Asia

• OpenAI released GPT-4o mini on 18th July
• Meta released Llama 3.1 405B on 23rd July
• Mistrial released the large2 model on 24th July

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”. 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

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.

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