OpenAI models deeply into the Microsoft ecosystem for some time

When Google unveiled Gemini 3 on 18 November 2025, most headlines focused on its leaps in reasoning, multimodality, and performance. But executives should look past the model-to-model comparisons. Gemini 3’s real significance is structural.

Part of a Broader Trend — Accelerated by Gemini 3

AI assistants have already begun moving inside enterprise workflows.

Microsoft Copilot, for example, has embedded OpenAI models deeply into the Microsoft ecosystem for some time, enabling users to query documents from office.com, summarize inboxes, act on Teams content, and automate tasks across Office 365.

Gemini 3 continues — and accelerates — this trend in Google’s environment.It comes natively with a broad set of Workspace integrations and early agentic capabilities, pushing Google toward a more unified enterprise AI mesh, where AI mediates interactions across email, documents, storage, collaboration tools, and automation.

Core Model Advances Explained

At its heart, the model is fully Multimodal, fusing five data types in one context window. Therefore, users can upload a video frame, supply transcripts, and request code or narrative summaries. Google touts a one-million-token context window, dwarfing earlier limits. In contrast, rival models still struggle beyond 200,000 tokens. Such scale supports extended Reasoning over legal filings, medical research, or entire repositories.

Deep Think mode extends this capability, trading speed for deeper analytical depth and higher benchmark scores. Furthermore, the company reports 81% on MMMU-Pro and 87.6% on Video-MMMU. Agentic features also mature, allowing the system to plan multi-step tasks and invoke external tools. Gemini 3 also introduces improved vibe coding for visual code prompts.

These technical gains reinforce Google's claim of frontier performance. However, specifications matter little without practical deployment, which we explore next.

The vulnerability presents the following risk characteristics:

Severity Level: LOW

CVSS Score: 5.0

Current Status: Unpatched

Detection Confidence: 80%

Affected Tools:

GitHub Copilot (GitHub/Microsoft)

Improved tool calling for automation

With refined interaction capabilities, Gemini 3 Pro can reliably call APIs, complete actions in apps, and integrate into enterprise systems such as Jira, GitHub, Confluence, Notion, Zendesk, and Slack.

These strengths make Gemini 3 Pro one of the most capable Gemini enterprise AI models available today.

Available in GoSearch on Release Day

GoSearch is one of the first enterprise platforms to offer Gemini 3 Pro immediately. As soon as Google DeepMind released the model, we integrated it into our multi-model AI ecosystem.

When Google unveiled Gemini 3 on 18 November 2025, most headlines focused on its leaps in reasoning, multimodality, and performance. But executives should look past the model-to-model comparisons. Gemini 3’s real significance is structural.

Part of a Broader Trend — Accelerated by Gemini 3

AI assistants have already begun moving inside enterprise workflows.

Microsoft Copilot, for example, has embedded OpenAI models deeply into the Microsoft ecosystem for some time, enabling users to query documents from office.com, summarize inboxes, act on Teams content, and automate tasks across Office 365.

Gemini 3 continues — and accelerates — this trend in Google’s environment.It comes natively with a broad set of Workspace integrations and early agentic capabilities, pushing Google toward a more unified enterprise AI mesh, where AI mediates interactions across email, documents, storage, collaboration tools, and automation.

Core Model Advances Explained

At its heart, the model is fully Multimodal, fusing five data types in one context window. Therefore, users can upload a video frame, supply transcripts, and request code or narrative summaries. Google touts a one-million-token context window, dwarfing earlier limits. In contrast, rival models still struggle beyond 200,000 tokens. Such scale supports extended Reasoning over legal filings, medical research, or entire repositories.

Deep Think mode extends this capability, trading speed for deeper analytical depth and higher benchmark scores. Furthermore, the company reports 81% on MMMU-Pro and 87.6% on Video-MMMU. Agentic features also mature, allowing the system to plan multi-step tasks and invoke external tools. Gemini 3 also introduces improved vibe coding for visual code prompts.

These technical gains reinforce Google's claim of frontier performance. However, specifications matter little without practical deployment, which we explore next.

The vulnerability presents the following risk characteristics:

Severity Level: LOW

CVSS Score: 5.0

Current Status: Unpatched

Detection Confidence: 80%

Affected Tools:

GitHub Copilot (GitHub/Microsoft)

Improved tool calling for automation

With refined interaction capabilities, Gemini 3 Pro can reliably call APIs, complete actions in apps, and integrate into enterprise systems such as Jira, GitHub, Confluence, Notion, Zendesk, and Slack.

These strengths make Gemini 3 Pro one of the most capable Gemini enterprise AI models available today.

Available in GoSearch on Release Day

GoSearch is one of the first enterprise platforms to offer Gemini 3 Pro immediately. As soon as Google DeepMind released the model, we integrated it into our multi-model AI ecosystem.

 By - Aaradhay Sharma