# OpenCode VS Gemini: Which One To Choose? AI has transformed how I work as a technical writer. Large tech companies are developing and sharpening these tools, but some of them are better than others depending on what you want to accomplish. This article will describe the differences I've experienced when using OpenCode vs Gemini. ## OpenCode OpenCode is an AI agent that you can run from your terminal or preferred IDE. OpenCode is model agnostic, meaning that you can choose which model to execute on. The power of OpenCode lies in the ability to connect to an MCP. MCP stands for Model Context Protocol. To put it simply, an MCP is a bridge between your coding agent and the platform you want to access. For example, you can have a Salesforce MCP, a Greenhouse MCP, a Workday MCP, and so on. When OpenCode connects to an MCP, it allows you to search for information available in that platform. For example, if you connect Opencode to a Salesforce MCP, you can research customer escalations, email campaigns, and more. Since OpenCode is model-agnostic, you can connect it to the most powerful Anthropic models (Opus 4.6+). The ability to choose a powerful model with the addition of being able to connect to an MCP is what makes OpenCode an extremely helpful coging agent. ### When to use OpenCode? From my experience, OpenCode is useful for: * Conducting internal research: If you want to gather information such as internal notes, PRDs, customer escalations and more, OpenCode will scrape the internal knowledge base and compile information it finds in a document. * Troubleshooting: You can use OpenCode to quickly fix errors, resolve merge conflicts, and more. * Compare content between files: Since OpenCode can access an internal knowledge base, you can use it to compare information between files and identify any gap. # Gemini Gemini is the AI assistant developed by Google. Gemini also offers an [MCP](https://ai.google.dev/gemini-api/docs/coding-agents#mcp-setup) which you can connect to your chosen coding agent. ### When to use Gemini? From my personal experience, Gemini is better used for tasks that require external search, collaboration, producing visual items and generating new ideas. Gemini allows you to quickly create Excel sheets or Google Docs that can be easily shared with stakeholders for visibility and collaboration. Gemini is great at thinking through difficult tasks that require researching content on the web and giving you a detailed answer based on its findings. I find Gemini to be better at producing visual content too. You can give Gemini a page and it will create a rough draft which you can then modify to achieve your desired goal. For example, let's take the Gemini the [Coding agent setup](https://ai.google.dev/gemini-api/docs/coding-agents) page and ask Gemini to produce a visual diagram of the content in the page: > Give me a visual diagram of the content in this page: [https://ai.google.dev/gemini-api/docs/coding-agents](https://ai.google.dev/gemini-api/docs/coding-agents) Gemini will produce the following result: ```plaintext ┌────────────────────────────────────────────────────────────────────────┐ │ AI CODING ASSISTANT │ │ (Claude Code, Cursor, Antigravity, Gemini CLI, Copilot) │ └───────────────────────────────────┬────────────────────────────────────┘ │ ┌─────────────────────────┴─────────────────────────┐ ▼ ▼ ┌───────────────────────────────────┐ ┌───────────────────────────────────┐ │ GEMINI DOCS MCP │ │ API DEVELOPMENT SKILLS │ │ (Model Context Protocol) │ │ (Context Rules & Patterns) │ ├───────────────────────────────────┤ ├───────────────────────────────────┤ │ Server: │ │ Methods: │ │ gemini-api-docs-mcp.dev │ │ • skills.sh (Recommended) │ │ │ │ • Context7 │ │ Installation: │ │ │ │ npx add-mcp [URL] │ │ Fallback: │ │ │ │ Fetches llms.txt from Google │ │ Feature Provided: │ │ │ │ Adds 'search_documentation' │ │ Available Modules: │ │ function for real-time API query. │ │ • gemini-api-dev (Foundational) │ │ │ │ • gemini-live-api-dev (Live/WebS) │ │ │ │ • gemini-interactions-api (Apps) │ └───────────────────────────────────┘ └───────────────────────────────────┘ ``` You can use Gemini to modify existing content. For example, if you want to modify the content in a table, you can: 1. Screenshot the table 2. Copy and paste the screenshot in the Gemini chat 3. Propose the changes you wish to make Gemini will generate a new Excel sheet with your proposed changes. Unless you mention the file where you want the changes to be made, Gemini will create a new Excel sheet. Here's an example: ![](https://cdn.hashnode.com/uploads/covers/611692ae16b6034ad04b0440/ac75b8ff-ee96-46b2-b1ed-24e72a448427.png align="center") Content in the Gemini\_example V2: ![](https://cdn.hashnode.com/uploads/covers/611692ae16b6034ad04b0440/aafb5d5a-255a-41ec-9055-e5db1dd01c73.png align="center") While the strengths of OpenCode and Gemini are distinct, I enjoy using both of them as they can greatly complement each other. ## Use case example If you want to develop a new technical document, you can: 1. Start by using Gemini to conduct competitive analysis, research industry best practices, and generate initial ideas based on public web knowledge. 2. Use OpenCode to search internal wikis, previous design documents, and incorporate lessons learned from past projects. 3. Use Geminit to synthesize the findings from the previous steps into a structured document that is ready to share with relevant stakeholders. ## Summary This table summarizes the differences between OpenCode and Gemini: | Task category | Gemini | OpenCode | | --- | --- | --- | | **Data source** | Web/External (public data) | Internal MCPs (Confluence, JIRA, etc.) | | **Research** | Competitive research, industry trends, general knowledge | Internal file search, internal reports | | **Output type** | Shareable documents, diagrams, visual analysis | Troubleshooting, summaries of internal knowledge, gap analysis | | **Collaboration** | Excellent for creating shared, collaborative documents | Excellent for aggregating proprietary, internal data | | **Key strength** | Ideation, external synthesis, visual processing | Internal search power, troubleshooting, security-aware data retrieval |