A few days ago, I listened to one of The Cloud Pod's podcasts, and they mentioned Microsoft's approach regarding Copilot and how Microsoft does not have its own LLM model. I started to dig a little deeper into this topic, as it has high potential regarding training the team to use one 'interface' and behind the scenes to be capable of switching between different LLMs.
Many think that 'Microsoft Copilot = one big model from OpenAI'. This was true initially, but today Copilot is more like an air‑traffic controller for AI. It can work with several large language models (LLMs), route your prompt to the right place, and bring back an answer grounded in your work data from Microsoft 365. The important part is which model and how the Copilot system chooses actions and protects your data.
What multi‑LLM Copilot means
Inside Microsoft 365, Copilot sits on top of an orchestration layer. This orchestrator is the interface between foundation LLMs and the skills and actions Copilot can run (for example, fetching a file via Graph, calling a connector, or invoking a plug-in). You write a prompt; the orchestrator interprets it, decides which actions are needed, and works with the model(s) to produce the final result. In other words, the system is built to choose, not just to generate.
The model choice is no longer theoretical. At the end of September 2025, Microsoft announced that Microsoft 365 Copilot is expanding model choice: Anthropic's Claude models now appear alongside OpenAI options in the Microsoft 365 Copilot app's Researcher agent and in Copilot Studio. Admins can enable this in the tenant, and Microsoft's docs explicitly state that Anthropic's models are hosted outside of Microsoft and subject to Anthropic's terms.
For builders, Copilot Studio makes the choice very concrete: when you author an agent, you can select the primary AI model from a dropdown, including newer preview models. This gives teams practical control over quality, cost, latency, and even compliance. Microsoft's Copilot Studio blog has also confirmed that Anthropopic models (e.g., Claude Sonnet 4.5) are available for orchestration scenarios.
Beyond Microsoft 365, two other pieces complete the picture. First, Azure AI Foundry offers a catalogue of models from multiple providers, so organizations can bring the model they prefer and connect it to enterprise data and guardrails. Second, GitHub Copilot lets developers pick among available chat models per client, which shows that model choice is becoming a standard, visible control in the Copilot family.
Finally, Microsoft is continuing to roll out the latest OpenAI models in Copilot experiences (you may have noticed headlines around GPT‑5 and a new "smart mode" that can switch for quality vs. speed), a sign that the company plans to keep routing to the best engine for a task rather than locking everything to one vendor forever.
Why this approach is powerful
From a business perspective, the real value of Copilot is not only in the model IQ but also in the integration with your work: Microsoft 365 apps, your permissions in Microsoft Graph, and enterprise compliance. Copilot grounds responses in organizational data you can see and keeps prompts and outputs outside of model training. This is the platform advantage: even when the underlying LLM changes, your security context, auditing, retention, and compliance do not.
Semantic Index also plays a quiet but important role. It maps your tenant's content and relationships so Copilot can understand context and retrieve relevant items, which reduces hallucination and makes multi‑step answers more accurate. Because this indexing is tenant‑level and automatic, it benefits you regardless of which LLM answers the last step.
The multi‑LLM posture makes Microsoft more agile. When a new provider ships a model safer for a regulated workflow or cheaper for a high‑volume scenario, Copilot Studio and the orchestrator can take advantage of it without rebuilding the whole product. Microsoft also develops small models (Phi‑3) for cases where cost, latency, or on‑device constraints matter. This combination of external LLMs & in‑house small models, powered by strong orchestration, lets Microsoft move faster than trying to win every benchmark with a single in‑house giant.
There is a second, practical benefit: global scale and compliance. Some industries or regions prefer specific model providers due to privacy requirements or data‑handling terms. Microsoft's documentation for connecting Anthropic notes clearly states that those models are hosted outside Microsoft and require admin approval, which gives enterprises the choice to opt in based on policy. The option exists, the control is in IT's hands.
Trade‑offs …?
A multi‑model system is more complex to engineer and govern. Orchestration, evaluation, and security must work consistently across different providers and versions. Microsoft's security guidance shows how they mitigate prompt‑injection and related risks in the Copilot prompt flow, but keeping quality steady across multiple backends is non‑trivial.
Transparency is another tension. In some Copilot surfaces, users don't always see which model handled a given turn, and advanced users naturally ask for that visibility. We're seeing the first steps—explicit switches like "Try Claude" in the Researcher agent and per-agent model choice in Copilot Studio—but uniform, fine-grained disclosure across every Copilot experience isn't there yet. For developers, GitHub Copilot's model selector is a good example of the direction.
If a model is hosted outside of Microsoft (for example, Anthropic on its own infrastructure), legal terms and data‑flow boundaries differ from Azure OpenAI. That doesn't mean it's less secure; admins must read the documentation and enable it intentionally. In enterprise reality, this is acceptable: many companies already consume cloud services from multiple providers and are comfortable with that model as long as policies are clear.
Does Microsoft really need its own LLM?
Microsoft doesn't need to rely on a single in‑house giant model to win with Copilot. The company's leverage is the platform: Microsoft 365 apps, Graph, connectors, security, and the orchestration layer that turns free‑form language into actions and grounded answers. As long as that platform can plug into the best models (OpenAI, Anthropic, open models via Azure AI Foundry, and Microsoft's own SLMs), the user experience keeps improving without a religious debate about who made the LLM.
If anything, the last months have proven the strategy. Microsoft added Anthropic in key Copilot surfaces while continuing to roll forward the newest OpenAI models, and it gives authors and admins more knobs to pick the right engine for the job. That is the opposite of vendor lock‑in, and it is suitable for customers because it turns AI into a capability layer rather than a brand decision.
Pros and cons of this approach are available below.
In the end
Copilot is an expert project manager. It knows your files and permissions, picks the best specialist for the task, and keeps the records and safety checks for the whole team. Sometimes the 'brain' changes, but the project manager stays the same." This is the right future for enterprise AI: flexible, grounded in your data, and not married to one brain.
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