Comparison of five LLM models: ChatGPT-4, GigaChat Pro, GigaChat Lite, YaGPT Pro, and Llama 3 7B

1. ChatGPT-4

Technical details:

• Architecture: Transformer-based model with billions of parameters (exact number not disclosed).

• Size: According to preliminary estimates, the number of parameters may exceed 100 billion.

• Training: The model was trained on a vast amount of texts in multiple languages using RLHF (Reinforcement Learning from Human Feedback) technique, which improves the quality and adaptability of responses.

Pros:

• Wide range of tasks: ChatGPT-4 is versatile and supports many use cases, from text creation to programming assistance and data analysis.

• Text generation quality: High-quality texts, logical and creative. The model takes context into account and can create coherent narratives.

• Context memory: Capable of considering up to 8,000 tokens of context, allowing for long conversations.

Cons:

• Performance: The model requires significant computational resources and time for generation, which can be a drawback in real-time systems.

• High cost: Using the model, especially for tasks with large volumes of data, can be expensive.

Ideal tasks: Writing articles, creating content, supporting programming, data analysis, multitasking.

2. GigaChat Pro

Technical details:

• Architecture: Also based on the Transformer architecture, but optimized for performance.

• Size: The number of parameters is not disclosed, but the model is smaller than ChatGPT-4, which increases its performance.

• Training: The model's training included specialized datasets for technical tasks such as programming and data analysis.

Pros:

• High performance: The model generates responses faster, making it suitable for interactive applications.

• Optimization: GigaChat Pro is optimized for specific tasks such as programming and technical analysis.

• Flexible integration: The model easily integrates into various systems thanks to API support and developer tools.

Cons:

• Lower text quality: Texts are less coherent and may be less creative than those of ChatGPT-4.

• Narrow specialization: While the model is good at technical tasks, it may fall short in more general and creative tasks.

Ideal tasks: Quick solution of technical problems, programming, integration into applications, creation of chatbots.

3. GigaChat Lite

Technical details:

• Architecture: Simplified version of GigaChat Pro, based on the same transformer principles.

• Size: The model is smaller and lighter than the Pro version, with fewer parameters.

• Training: Narrower and simpler datasets are used, which reduces the cost of training and operating the model.

Pros:

• Cost-effectiveness: Significantly cheaper to use compared to more powerful models.

• Fast generation: Due to its smaller size, the model generates responses faster.

• Lightweight: Consumes fewer computing resources, making it suitable for use on devices with limited capabilities.

Cons:

• Limited functionality: Limited support for complex tasks and contexts.

• Low text quality: Texts may be of lower quality and require additional editing.

Ideal tasks: Simple tasks requiring fast text generation, working with limited resources, cost-effective solutions.

4. YaGPT Pro

Technical details:

• Architecture: Cloud-based architecture based on transformers, optimized for the Russian language.

• Size: The number of parameters is close to large models to maintain high-quality generation in Russian.

• Training: Training was conducted on datasets with a focus on Russian-language texts and cultural features.

Pros:

• Specialization in the Russian language: Better performance with Russian-language content due to training on relevant texts.

• Flexibility of customization: Users can customize generation parameters and adapt the model for specific tasks.

• Efficiency: The model is optimized for quick response and efficient resource usage.

Cons:

• Limited support for other languages: The model is not as effective in other languages, especially in English.

• Average text quality: While the texts are good for Russian, they may not be as coherent or creative as other models.

Ideal tasks: Projects aimed at a Russian-speaking audience, content considering cultural and linguistic features, tasks with flexible customization.

5. Llama 3 7B

Technical details:

• Architecture: Transformer-based model optimized for small data volumes.

• Size: 7 billion parameters — significantly smaller than other models, making it more lightweight.

• Training: The model was trained on open datasets using self-supervised learning techniques, enhancing its adaptability.

Pros:

• Lightweight: The model requires fewer computational resources and operates faster on limited capacities.

• Open Source: Allows easy customization and adaptation of the model to suit specific needs.

• Good Text Quality: For a model with such a number of parameters, Llama 3 7B demonstrates decent text quality.

Cons:

• Limited Capabilities: Due to the small number of parameters, the model cannot handle complex tasks like ChatGPT-4.

• Low Adaptability: The model is less adaptable to new tasks and contexts, which may limit its application in complex projects.

Ideal Tasks: Prototype development, tasks with limited resources, open-source projects, early stages of AI product development.

Conclusion

Each of these models has its own strengths and weaknesses, making them suitable for different tasks and scenarios. ChatGPT-4 is a versatile tool with high text quality, ideal for complex projects. GigaChat Pro and Lite offer performance and cost-efficiency, especially useful for technical tasks and real-time applications. YaGPT Pro is an excellent choice for Russian-language projects where cultural and linguistic nuances are important. Llama 3 7B stands out for its lightness and customization capabilities, making it attractive for resource-constrained projects and developers who prefer working with open-source code.

The choice of model depends on the specifics of your project, available resources, and priorities, whether it is text quality, performance, or customization flexibility.

Our choice

Our team is actively developing an internal product – foxtailbox.ru. This is a service for automated assessment of IT specialists' skills. In it, we use LLMs for generating test questions and evaluating answers.

For our needs, the Llama 3 7B model was ideal, as it provides an optimal balance of text quality and computational resources.

Despite its relatively small size of 7 billion parameters, the model demonstrates good text generation quality and customization flexibility thanks to its open-source code.

This allows us to adapt it to our specific tasks, such as automatic question generation and answer evaluation, without significant infrastructure costs. The model's lightness also makes it ideal for use on limited capacities, which is important for prototyping and developing AI-based products.

We hope the review was helpful. If you have your own thoughts or experience working with these models, share them in the comments. It will be interesting to discuss!