Which AI model and vendor is right for you?

The major AI vendors at a glance

The generative-AI market is shaped by a handful of large vendors that differ in focus, ecosystem and strengths. The overview below is deliberately neutral and describes the typical profiles without favouring any single provider.

• OpenAI / ChatGPT: Versatile all-rounder with a large ecosystem, voice and image features and a broad API; often the default entry point for text, code and multimodal tasks.
• Anthropic / Claude: Focus on reliable, safety-oriented models with a large context window; popular for long documents, analysis, structured work and coding.
• Google / Gemini: Deeply integrated into Google Workspace, Android and Google Search; strong on multimodality and connection to the Google ecosystem.
• Microsoft / Copilot: Embedded in Microsoft 365, Windows and GitHub; brings AI directly into existing office and developer workflows, partly built on OpenAI models.
• Perplexity: Answer and research engine focused on source citations; combines web search with AI summaries rather than pure chat conversation.

How the models differ technically

Behind the brands sit entire model families that are updated continuously. Rather than fixating on individual version numbers, it pays to understand the dimensions that actually matter when comparing them.

• Reasoning: How well a model handles multi-step tasks, logic and complex instructions; dedicated reasoning models spend more compute on harder problems.
• Context window: How much text (documents, code, history) a model can process at once. Large context windows help with long contracts, reports or codebases.
• Multimodality: Whether a model understands and generates not just text but also images, audio, video or files, which is decisive for many practical use cases.
• Tools and agents: Whether a model can connect to external tools, web search, code execution or business data and complete multi-step tasks semi-autonomously.
• Speed and cost: Larger models are more capable but slower and pricier; smaller or faster variants suffice for many routine tasks. Check current pricing with each provider.
• Open vs. closed: Beyond closed vendor models there are open or open-weight models (such as Llama, Mistral or the Swiss Apertus model from ETH Zurich and EPFL) that can be self-hosted, offer more control but require in-house expertise.

How to choose the right model

Instead of hunting for the supposedly best model, work structurally from your needs. The following order has proven useful in practice.

• Use case first: Define the concrete task (research, writing, coding, customer service, data analysis) and the required quality before picking a model.
• Integration: A model that fits into your existing tools (Microsoft 365, Google Workspace, CRM, development environment) saves friction and onboarding.
• Data protection: Clarify which data may be processed, whether a data-processing agreement is in place and whether inputs are used for model training. For sensitive data, business or enterprise plans are usually mandatory.
• Cost and scaling: Estimate realistic usage volumes and check the provider's current pricing; teams often mix pricier models for demanding work with cheaper ones for routine tasks.
• Test it yourself: Compare candidates on your own typical tasks (a small test set), not just on public rankings. Quality is task-specific.
• Avoid lock-in: Favour open interfaces and the ability to switch or combine vendors so you do not become dependent on a single model.

Data protection and compliance in Switzerland

Swiss organisations are governed by the revised Data Protection Act (revDSG/nFADP), overseen by the Federal Data Protection and Information Commissioner (EDOEB). Personal data may only flow into AI services with a sufficient legal basis and transparency; particular care applies to sensitive data.

Anyone processing individuals in the EU or offering AI systems in the EU may additionally fall under the EU General Data Protection Regulation and the EU AI Act; both apply extraterritorially. The EU AI Act classifies AI applications by risk and introduces staggered obligations.

In practice: for business data use business or enterprise offerings with a data-processing agreement in which inputs are not used for training. Pay attention to data residency (where data is stored and processed) and document purpose, legal basis and retention.

Comparison at a glance

• OpenAI / ChatGPT: Strength is a broad ecosystem and versatility; fits general productivity, prototyping and building on the API.
• Anthropic / Claude: Strength is reliability, large context and a safety focus; fits long documents, analysis and demanding coding.
• Google / Gemini: Strength is multimodality and Google integration; fits Workspace and Android users and highly visual tasks.
• Microsoft / Copilot: Strength is embedding in office and developer tools; fits organisations already using Microsoft 365 and GitHub.
• Perplexity: Strength is source-based research; fits fast, cited answers and knowledge work that needs traceability.

Common mistakes when choosing

• Relying only on public benchmarks instead of testing models on your own real tasks.
• Clarifying data protection too late and entering sensitive data into free consumer versions.
• Committing prematurely to a single vendor and underestimating switching costs and lock-in.
• Always using the largest model when smaller, faster variants suffice for routine tasks and save cost.
• Accepting AI outputs unchecked instead of verifying results and assigning responsibility for review.