Which AI models are there, and when do you use which one?

AI is now a permanent fixture in the world of software development and business operations. Even so, many organisations can no longer see the wood for the trees. Because which AI model do you actually choose? One moment you hear about ChatGPT, the next about Claude or Gemini. And in the meantime open source models such as Llama and Mistral keep cropping up as well.

In this blog we give companies a clear overview of the most important AI models around at the moment, and of when to use which one. So that you can judge better which solution fits your organisation and your challenges, certainly if you want to use AI within software development.

Update July 2026: do not look only at the model, look at the application

The question "which AI model is the best?" is understandable, but for organisations it is usually too narrow. A choice of model only becomes valuable once it is clear which process you want to improve, which data is available and how the outcome is checked.

So use this overview as a starting point. Do you want to use AI in an existing application or business process? Then start with three questions:

• Which task currently takes a lot of time or is prone to error?
• Which data may the AI model use, and which may it not?
• Who checks the output before it has an impact on clients, staff or decision-making?

After that you can decide whether you are better off with a general model such as ChatGPT, Claude or Gemini, an open source model such as Llama or Mistral, or a combination within your own application.

Further reading on AI in software development

Are you still finding your bearings with AI? These articles help you move from choosing a model to practical application, management and development strategy.

Integrating AI into existing software: where do you start? The tipping point from low-code to AI AI makes building easier. But who maintains it afterwards?

ChatGPT (OpenAI)

Probably the best-known model. ChatGPT excels at natural conversation, creative output and fast prototyping.

Companies use it for:

• Customer service: chatbots that handle questions directly.
• Content generation: texts, emails, summaries.
• Prototyping: quick proofs of concept within apps.

For software development? ChatGPT is particularly strong at code generation, debugging and documentation. Ideal for building prototypes, working out user stories or testing straight away with function calling.

Strength: widely applicable, high-quality language and code. Watch out: it runs in the cloud, so it is less suitable for organisations that want to stay on-premise.

Claude (Anthropic)

Claude was developed with the emphasis on safety and reliability. What makes it unique: the enormous context length, which lets you submit hundreds of thousands of words at once. Perfect for:

• Document analysis: going through long reports or contracts.
• Compliance and legal work: summarising and structuring information safely.
• Safety-critical applications.

For software development? Excellent for code review and analysis of large codebases. Thanks to the long context, Claude can understand complete repositories, generate documentation and suggest refactorings.

Strength: extremely long context, focus on responsible use. Watch out: sometimes less creative or "out of the box" than ChatGPT.

Gemini (Google DeepMind)

The successor to Bard, now integrated into the Google ecosystem. Strong in multimodal applications (text, image, code) and ideal for companies already working with Google Workspace. Use it for:

• Smart assistants in Docs, Gmail or Sheets.
• Multimodal input: analysing photos and turning them straight into reports, for instance.
• Data analysis linked to Google Cloud.

For software development? Gemini works well as an AI copilot in development environments, especially in combination with Google Cloud and Vertex AI. Handy for automatic code completion, bug detection and integration with existing pipelines.

Strength: seamless integration with Google products. Watch out: less popular outside the Google ecosystem.

Llama (Meta)

An open source model that keeps growing in popularity. You can run Llama locally or in your own cloud environment, which gives you maximum control over data and costs. Companies mainly use it for:

• Privacy-sensitive applications (in healthcare or government, for example).
• Custom AI: training models on your own data.
• On-premise AI without depending on external parties.

For software development? Ideal if you want to integrate AI functions into your own apps without depending on external cloud APIs. You can train Llama locally and use it for internal code assistants or generative features within your software.

Strength: open source and flexible. Watch out: it requires more technical knowledge and more management.

Mistral

A European player with compact, fast models. Mistral focuses on efficiency and cost saving. Think of applications such as:

• Real-time AI in apps (chat, recommendations).
• Smaller models for edge devices.
• Cheap alternatives for generic tasks.

For software development? Mistral is lightweight and fast, so it is excellent for embedded AI functionality or applications that have to run locally. Think of edge apps, IoT systems or low-cost AI features in existing software.

Strength: fast, lightweight and cost-efficient. Watch out: less well known (as yet) and therefore a smaller community than OpenAI, for example.

How do you choose the right model as a company?

The answer depends on your situation:

• Want to start quickly and use it broadly? → ChatGPT.
• Working with a lot of documents or compliance? → Claude.
• Fully in Google Cloud? → Gemini.
• Is privacy your top priority? → Llama.
• Looking for efficiency and low costs? → Mistral.

For software development:

• Want to prototype and build → ChatGPT or Gemini.
• Want to review and optimise → Claude.
• Want to integrate AI into your own products → Llama or Mistral.

From AI model to business application

Choosing an AI model is one step. The real value only appears once AI becomes a reliable part of your application, workflow or decision-making.

For business software, these are usually the most important choices:

• Integration: does AI have to work with existing systems such as CRM, ERP, document storage or a client portal?
• Control: may AI only make suggestions, or carry out actions as well?
• Privacy: which data may go to external AI services and which data has to stay within your own environment?
• Management: who monitors quality, errors, costs and further development after go-live?

That is why AI in software development is usually not a standalone tool choice, but a design question. Read on about integrating AI into existing software, AI-assisted development, AI-assisted web application development, mobile application development and maintenance and support.

Conclusion

There is no single "best AI model". It depends on your use case, your infrastructure and your ambitions. It is often sensible to combine several models: ChatGPT for the chatbot, Claude for code analysis, and Llama for internal AI features.

At JAM-IT we help organisations make the right choices and put AI to genuinely valuable use in apps and processes, whether they are built with low-code, traditionally or as a hybrid.

Want to know which AI model is most suitable for your organisation? Let us talk it through.