A year ago, choosing an AI model meant deciding between OpenAI and Anthropic. In 2026 the landscape has shifted. Open models like Llama, Qwen and Mistral compete with proprietary ones across many tasks, and more companies are asking whether migrating —or at least supplementing— their current stack makes sense.
What matters now is whether open models fit your specific use case.
What changed
Three developments have reshaped the equation.
The first is quality. Open models in 2026 perform at a level that proprietary models offered only a year ago in reasoning, information extraction and text generation. For many enterprise applications —document classification, internal assistants, data analysis— the gap with closed models is marginal.
The second is cost. Running an open model on your own infrastructure or through a provider like Together, Fireworks or Groq costs a fraction of what OpenAI or Anthropic charge per API call. At volume, the difference shows up in the monthly bill.
The third is the European context. The AI Act requires transparency in data usage and, in many cases, that sensitive data stays within the EU. With an open model, you decide where it runs, who accesses the weights and what data gets processed. With a closed model, you depend on what your provider states in their terms.
When migrating makes sense
Not every use case warrants a switch. Three scenarios where open models have a clear advantage.
High volume of repetitive queries. If your application makes thousands of daily calls with similar patterns —classifying tickets, extracting form fields, answering frequent questions about your documentation— an open model fine-tuned for your task performs as well as or better than a generalist one, at much lower cost per call.
Sensitive or regulated data. Healthcare, finance and public sector companies that cannot send data to external APIs find in open models a way to maintain control. The model runs on your infrastructure, data stays in your network, and regulatory compliance becomes easier to demonstrate.
Deep customization needs. When you need the model to learn your company's tone, terminology or specific processes, open models allow direct fine-tuning on your data. Closed models offer more limited personalization options.
When closed models still win
Closed models retain real advantages in three situations.
Tasks requiring cutting-edge reasoning. For problems that demand the highest reasoning level —complex legal analysis, architecture design, research— frontier closed models still lead.
Small teams without ops capacity. Running an open model requires infrastructure, monitoring and technical expertise. If your team has three people and nobody dedicated to ML ops, the operational cost can exceed the savings.
Rapid prototyping. To validate an idea in a week, calling an OpenAI or Anthropic API remains the fastest path. Open models make sense once you know the use case works and want to optimize.
A practical approach
The decision does not have to be binary. Many companies we advise use a closed model for the most complex tasks and open models for daily volume. The frontier model handles the 10% of difficult cases; the fine-tuned open model manages the remaining 90% at a fraction of the cost.
The first step is auditing your current usage: how many calls you make, what task types dominate, what data you handle and how much you spend per month. With those numbers on the table, the decision stops being ideological and becomes economic and technical.
At Luxion we help companies evaluate these migrations without commitment: we analyze your actual usage, show you which parts can safely move to open models, and build a comparative prototype before any final decision.