Today, businesses seek to automate as many processes as possible to gain efficiency, reduce costs, and allow teams to focus on high-value tasks. Artificial intelligence (AI) offers powerful solutions to achieve these objectives. However, choosing the right AI engine for your automation depends heavily on the technical characteristics and specificities of your processes.
Here is a comparison based on the AI models by use cases available in DINA, specifically adapted to the challenges of process automation (note: as AI models evolve very rapidly, what we describe here will undoubtedly be obsolete within a few months).
For confidentiality and local use
- GPT-OSS (20B & 120B): Ideal if your data is sensitive. Requires computing power. Open source, flexible for fine-tuning.
- Mistral Small/Medium: Efficient, simple, suitable for edge deployments.
For processing large volumes
- Llama 4 (Scout/Maverick): Unprecedented context windows (up to 1 million tokens), native multimodality.
- Use cases: contract analysis, scientific research, medical archives, legal data.
For advanced reasoning
- DeepSeek R1: Excellent for reasoning chains, logic, and mathematics.
- AM-Thinking-v1 (32B): A powerful and affordable compromise, adapted to business needs.
- Use cases: audit, strategy, engineering, education.
For versatility and multilingual capabilities
- Qwen 3: Supports 119 languages, open source, efficient for global conversational assistants.
- Use cases: multilingual chatbots, customer service, content generation for international markets.
For efficiency and cost
- Mistral (Small/Medium/Magistral): Excellent compromise for standard business applications, with an enhanced Magistral version for reasoning.
- Use cases: economical deployment, companies seeking speed and efficiency.
Conclusion: adapt your choice to real challenges
Based on technical characteristics, budgetary constraints, and especially the type of process to automate, you now have some key information to effectively choose the ideal AI model for your project.
Choosing the right model ensures smooth, reliable, and efficient automated processes, thereby optimizing your AI return on investment.
