For years, using Microsoft 365 Copilot meant using OpenAI, whether you realised it or not. One model humming away behind everything. Take it or leave it.

Not anymore. And honestly, the shift is bigger than it looks at first glance.

Copilot has become multi-model, now featuring Anthropic’s Claude alongside OpenAI’s GPT. Microsoft has also integrated its own MAI models, allowing you or Copilot to choose the best engine for each task.

Somewhere along the way, model choice stopped being invisible and became a real decision with real consequences. So let’s explore why it matters and what your business should do about it.

What “Multi-Model” Actually Means

Multi-model AI simply means Copilot isn’t tied to a single provider. Rather than a single model answering everything, several are on hand, and the best can be chosen for each job.

Right now, that line-up includes OpenAI’s GPT models, Anthropic’s Claude (both Sonnet and Opus), and Microsoft’s own MAI family. In the main Copilot Chat, a model picker lets you swap between them, and often Copilot auto-selects whatever suits the task at hand.

The reasoning is refreshingly blunt. Microsoft’s Charles Lamanna put it well: “It is this multi-model advantage that makes Copilot different.” Put more simply: no single model excels at all tasks, so there’s no need to pretend one does.

Why One Model Was Never Enough

Different models are genuinely good at different things. That’s the whole point.

One excels at long, structured reasoning. The other writes in a more natural voice, handles careful analysis better, or simply costs less to run when you’re doing it at scale. Push every task through a single model, and you’re quietly settling for “good enough” on all the jobs it’s weaker at.

Multi-model breaks that compromise. A drafting job, a deep research task, and a two-line summary no longer have to share one engine. Match the model to the work, and the output gets better, plain and simple. For a business leaning on AI across dozens of tasks a day, those little gains stack up faster than you’d think.

The Clever Bit Models Working Together

Now here’s where it gets genuinely interesting. Multi-model isn’t only about picking one model over another. Sometimes the best answer comes from two models working as a team.

Copilot’s Researcher agent showcases this with a feature called Critique. One model leads the generation, planning the task and knocking out a first draft, while a second steps in as an expert reviewer, checking and tightening it before the final answer lands.

Microsoft made this the default because pairing a writer with a separate reviewer measurably lifts quality. It’s the same reason your best documents were never written and proofread by the same person in one pass.

Then there’s Model Council, which fires a single prompt at GPT and Claude together and lets you compare their reasoning side by side. On a high-stakes question, watching two leading models agree or fall out is genuinely useful.

This shift is worth noting. The future of enterprise AI isn’t a single all-conquering model. It’s the right model, well-orchestrated. The same principle sits behind building RAG pipelines, where how you direct and ground a model counts every bit as much as which one you reach for.

The Governance Catch Every Admin Must Know

Now comes the part that turns a nice feature into a real decision. Choosing a model isn’t a neutral flick of a switch, and this is where careful organisations need to sit up and take notice.

The moment a request is routed to a Claude model, Anthropic becomes a subprocessor in Microsoft’s data processing chain. Microsoft lists it as such. And for anyone conducting a data protection audit that examines that processing chain, that’s a brand-new entry to record and assess, not one to wave through.

Availability shows the same caution. Anthropic models have been on by default across most regions since early 2026, but EU, UK and EFTA tenants must opt in via an admin first. Disable Anthropic, and agents fall back to OpenAI. Which means model choice lives firmly with IT, exactly where it belongs.

If you’re laying those foundations, our guide on governing the Power Platform at scale covers the control side that slots in alongside all this.

What This Means for Your Business

Strip it right back, and multi-model hands you three practical wins and one job to stay on top of.

Better outputs, because each task can run on whichever model suits it best. Strategic freedom, because you’re no longer chained to one vendor’s roadmap, pricing or bad days. And resilience, because if one provider trips up, the others are right there. The job? Know which models are switched on, know where your data goes, and set it up to match your compliance needs.

Get that balance right, and it’s a strong place to stand. You’re building on a platform designed for choice rather than lock-in, which is precisely what you want as the AI landscape keeps reshuffling itself month after month.

Maximizing the Benefits of Multi-Model Copilot

You don’t need to become a model expert overnight. A few sensible habits go a long way.

  • Confirm your admin settings so enabled models match your data and compliance requirements
  • Teach power users the model picker, so they learn which model suits which task
  • Use Auto selection for everyday work and let Copilot choose
  • Reach for Model Council or Critique on high-stakes research where quality really counts
  • Document your data flows, especially if you operate under strict regulatory rules

None of this is heavy lifting, and together it turns model choice from a hidden setting into a genuine advantage.

That’s the work we do with clients every day. Our AI and Copilot services and AI Enablement Programme help organisations properly configure multi-model Copilot, get governance right, and capture the quality gains on offer. As an ISO 27001–certified Microsoft partner, Stallions Solutions ensures compliance is in place before you scale.

Final Thoughts

Multi-model AI turns Copilot from a one-engine tool into a platform where the right model, or the right mix, takes on each job. That means better output, greater strategic freedom, and real resilience, so long as you keep a firm grip on which models are enabled and where your data ends up.

The organisations that come out ahead won’t be the ones chasing whatever model tops the leaderboard this week. They’ll be the ones who set Copilot up thoughtfully, govern it properly, and let the platform reach for the best tool for the task.

If you’d like help configuring multi-model Copilot for quality and compliance, that’s exactly what we do. Book a free assessment, and we’ll help you get the most from every model available, honest advice, no hard sell.

Frequently Asked Questions

What is multi-model AI in Microsoft Copilot?

It means Copilot can use multiple AI models, including OpenAI’s GPT, Anthropic’s Claude, and Microsoft’s MAI models, and choose the best one for each task rather than relying on a single provider.

How do I choose a model in Copilot?

Use the model picker in Copilot Chat to manually select a model, or let Copilot auto-select the most suitable one. In Copilot Studio, makers can choose a model when building an agent.

Is Claude available in Microsoft 365 Copilot?

Yes. Anthropic’s Claude models run alongside OpenAI’s. They’re enabled by default in most regions, though EU, UK and EFTA tenants require admin opt-in first.

Does using different models affect data compliance?

Yes. When a request uses a Claude model, Anthropic becomes a subprocessor in Microsoft’s data processing chain, which regulated organisations should document and assess as part of their compliance review.

Why does model choice matter for my business?

Different models excel at different tasks, so choosing the right one improves quality, avoids vendor lock-in and adds resilience, provided you manage the governance that comes with it.