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Microsoft Copilot vs. Google Gemini: How do they compare?

By Marius Sandbu

Businesses are increasingly showing interest in generative AI productivity tools such as Microsoft's Copilot and Google's Gemini. But selecting the best option for a specific organization can be challenging due to the numerous features and capabilities each tool provides. 

Adopting any GenAI tool requires a thorough technical evaluation. A good place to start is a Copilot vs. Gemini comparison, examining how they stack up in terms of features, pricing, performance and integration with their respective ecosystems.

Microsoft Copilot vs. Google Gemini: Core features

After the release of OpenAI's ChatGPT in November 2022, Microsoft initially previewed Copilot as a separate service -- called Bing Chat -- and released it to the general public in May 2023. Thanks to Microsoft's strategic partnership with OpenAI, Copilot uses the same large language model (LLM) as ChatGPT, while integrating Bing Search for real-time information access.

Google entered the AI race even earlier with the release of Bard in February 2023, rebranded a year later as Gemini. Throughout 2024 and 2025, Google has made significant improvements to its language models, with the latest release of its newest model, Gemini 3, in November 2025. 

Each tool platform has undergone significant improvements to its foundation models, resulting in new features that businesses must evaluate. 

• Context size. A model's context window describes the amount of information it can process at once, serving as the model's memory. Developers using the Gemini API have access to a context window of up to 2 million tokens, while Gemini Advanced for end users can handle up to 1 million. In comparison, Copilot's LLM -- OpenAI's GPT-5.1 -- supports 400,000 tokens, equivalent to about 350,000 words. 
• Integration. Gemini integrates with Google Search, which is much more widely used and more reliable than Microsoft's Bing. Both Google and Microsoft have also added support for the Model Context Protocol (MCP), which enables easier integration with MCP servers -- an increasingly de facto standard for generative AI (GenAI) integrations. 
• Multimodality. Gemini and Copilot offer image generation capabilities. Gemini uses Imagen 3, recently made generally available, while Copilot uses OpenAI's Dall-E 3. Both vendors also offer video content creation capabilities -- Google's Veo 3.1 and OpenAI's Sora-2.

• Performance. Gemini 2.5 Pro currently outperforms Copilot on needle in a haystack (NIHS) benchmarks that test how well an LLM can retrieve and use small, hard-to-find pieces of information from a large pool of text. But these benchmarks can quickly become outdated due to the rapid pace of model development.
• Plugins. Both Gemini and Copilot provide various plugin options, and both companies now support the MCP protocol, which has become the universal standard for integrating third-party tools and data with GenAI. However, Microsoft's regular Copilot does not yet offer MCP support; it is currently restricted to Copilot Studio.

Table 1. Comparison of Microsoft Copilot and Google Gemini's core features

Feature	Microsoft Copilot	Google Gemini
LLM	OpenAI GPT-5.1	Gemini 3 Pro
Price	$19.99 per user, per month for Microsoft 365 Premium $30 per user, per month for Microsoft 365 Copilot	$19.99 per user, per month for Gemini Advanced $30 per user, per month for Gemini Enterprise
Context size	400,000 tokens, which is about 350,000 words	1 to 2 million tokens, which is approximately 750,000 to 1.5 million words
Internet search integration	Bing Search	Google Search
API support	Yes, with Copilot 365 only	Yes, with Gemini Enterprise only
Customizability	Yes, Copilot AI Agents; part of Copilot Studio, aimed at businesses	Yes, Gems, and agent support as part of Gemini Enterprise
Integration with collaboration suite	Yes, integrated in Office apps	Yes, integrated with Google Workspace with Gemini Enterprise
Text-to-image	Yes	Yes
Text-to-voice	Yes	Yes
Voice-to-text	Yes	Yes
Text-to-video	Yes, with Sora-2	Yes, with Veo 3.1
Notebook feature	Yes, Copilot Notebook -- part of Business edition	Yes, Notebook LLM
GPQA Diamond	88.1% (GPT 5.1)	91.9% (Gemini 2.5 Pro)
Code assist service	Separate (GitHub Copilot)	Google Code Assist Standard

Features and products on the horizon

Google is continually enhancing Gemini and has recently introduced a new set of agent capabilities within Gemini that enable users to create customized agents within Google's ecosystem. These agents can integrate with third-party services using standardized protocols, such as MCP.

Microsoft and Google have also integrated AI capabilities into their web browsers -- Microsoft Edge and Google Chrome -- offering real-time assistants with video and audio support. Furthermore, Google has extended the native Gemini experience within the Android ecosystem by adding new application extensions, particularly on Google Pixel phones.

Microsoft's strategy for Copilot is a bit broader. They're building a custom Copilot feature included as part of Windows that's powered by local LLMs. This ecosystem is designed to let developers build applications and features using various LLMs on a Windows machine. Furthermore, Copilot and Copilot Studio have also been extended to incorporate other LLMs, such as models from Anthropic, to create a more "open" ecosystem and offer customers the flexibility to select their preferred foundational LLM. 

Yet Google currently appears to have the upper hand in the AI space, because it has all the capabilities in place to train and build its own LLMs, such as with Gemini. Google is using custom-built tensor processing unit (TPU) chips for LLM inference and training, while Microsoft relies on OpenAI and other vendors, such as Anthropic, to perform these tasks. 

GenAI tools for coding

Microsoft and Google each offer GenAI tools specifically for software development. Through its partnership with GitHub, Microsoft provides GitHub Copilot, which is widely recognized for its code suggestions, completions and generation across multiple programming languages. The tool was also recognized in August 2024 as a leader in Gartner's first-ever Magic Quadrant for AI code assistants.

Google offers Gemini Code Assist, which uses Google's language models to provide real-time code suggestions and completions. In terms of monthly pricing, GitHub Copilot Pro costs $10 per user per month, and Gemini Code Assist offers various cost tiers, as well as being available as an add-on to Gemini Enterprise and regular Google AI.

Copilot vs. Gemini: Which is best?

Choosing between Copilot and Gemini is challenging because GenAI tools are continuously evolving. LLMs are constantly improving in performance and context size, and vendors regularly release new features to remain competitive.

For buyers evaluating Copilot and Gemini for enterprise-wide adoption, a strategic decision framework based on core business factors is essential. The "best" choice should align most closely with the organization's existing technological landscape, operational needs and future AI vision.

Existing ecosystem considerations

For businesses primarily working within the Microsoft ecosystem, Copilot is the natural and often simplest choice. It has native integration with Microsoft 365 (Word, Excel, PowerPoint, Outlook and Teams), minimizing deployment friction, training overhead and compatibility issues.

For businesses with an investment in Microsoft Azure services, Copilot is a seamless extension. Microsoft is closing the gap between Copilot and Azure, enabling businesses to more easily integrate Copilot into Azure services, so users can connect Copilot agents to data sources in Azure, as well as to integrations available through standards like MCP.

But for teams invested in Google's suite of products, Gemini is typically the natural choice. Currently, Gemini 3 is the best model overall for performance in coding, agents, multimodality and voice. It can handle larger amounts of information compared to OpenAI's GPTs.

Gemini's tight integration with Google Workspace and Google Cloud services also takes advantage of the existing Google ecosystem investment. Google is also currently the only cloud provider with the entire value chain -- data, training and inference LLMs -- on its own platform. 

Employee workflows and use cases

Businesses must also consider which tool more closely aligns with the organization's unique operational needs and use cases.

• Productivity and collaboration. For document creation, email management and meeting summarization within established productivity suites, the tool that integrates most seamlessly with the organization's daily work environment will yield the highest ROI -- most likely Copilot for Microsoft 365 users and Gemini for Google Workspace users.
• Software development and DevOps. While Copilot and Gemini are strong coding assistants, evaluate which model performs best for the organization's primary programming languages, code repository and development pipeline. However, most businesses use GitHub Copilot, which is not limited to any language model and can therefore use OpenAI GPT models and Gemini 3.
• Line of Business (LOB) application integration. Determine which software vendor offers superior out-of-the-box connectors for critical LOB systems like ERP, CRM and HR platforms -- for example, SAP, Salesforce, ServiceNow or Workday. Microsoft currently offers the widest range of available options, with many ready-made connectors for enterprise agents.

Long-term AI strategy

Creating an enterprise AI strategy requires forward thinking. Therefore, businesses must also consider their future goals when deciding between Copilot and Gemini.

• Agent development and customization. Assess the ease of building custom GenAI agents specific to internal processes, such as an internal knowledge chatbot or procurement automation agent. The Azure and Copilot stack offers strong tooling for those types of applications, while Gemini's core model capabilities provide a powerful foundation for developers.
• Multimodality and future capabilities. Consider the organization's future need for AI to process more than just text. Gemini 3 currently excels in multimodal capabilities -- processing images, video and audio -- and has a very large context window. If the long-term vision includes advanced computer vision or voice-enabled applications, this capability becomes a key differentiator.
• Vendor lock-in and openness. Microsoft is betting on supporting multiple LLMs on its Copilot ecosystem, while Google currently only supports its own. Google currently has the best model, but that might not always be the case, so businesses must consider the consequences of selecting a service that locks them into a single set of models.

Marius Sandbu is a cloud evangelist for Sopra Steria in Norway who mainly focuses on end-user computing and cloud-native technology.