Gemini Code Assist Review

The free tier offers 6,000 code-related requests and 240 chat requests per day-more than enough for full-time individual development. You get access to Gemini 2.5 models, support for 20+ programming languages, and IDE plugins for VS Code and JetBrains. However, advanced features like Agent mode, MCP support, and unlimited completions require the $19/month Standard tier.

Gemini Code Assist can process up to 1 million tokens of context-equivalent to roughly 750,000 words or several medium-sized codebases. This means it understands your entire repository structure, cross-file dependencies, and architectural patterns when making suggestions. The massive context window is available on all tiers, including the free plan, making it ideal for large monorepos and microservices architectures.

Yes, the core coding features work independently of Google Cloud. You can use code completion, Agent mode, custom commands, and GitHub PR reviews without any GCP dependencies. However, you'll miss out on the deep integrations with Apigee, BigQuery, Firebase, and other Google Cloud services that make it particularly powerful for GCP-based teams. The tool is still competitive for general development work.

Agent mode (Standard tier and above) makes coordinated changes across multiple files simultaneously, understanding how modifications in one file affect others. Regular code completion suggests individual lines or functions at your cursor. Agent mode can refactor entire features, update API contracts across services, or migrate dependencies throughout a codebase. It also supports MCP (Model Context Protocol) for connecting external tools and databases.

Enterprise customers can train Gemini Code Assist on their private codebase, teaching it organization-specific libraries, frameworks, and architectural patterns. The AI learns your team's coding conventions, internal APIs, and preferred design patterns. This dramatically improves suggestion relevance for companies with extensive proprietary code. The tuning process is managed by Google Cloud and requires the $45/month Enterprise tier.

It depends on your needs. Gemini Code Assist wins on context window (1M vs 100K tokens), free tier generosity (6K vs 2K monthly completions for Copilot Individual), and Google Cloud integrations. GitHub Copilot wins on price ($10 vs $19), maturity (more refined suggestions), multi-model support (10+ models), and compliance controls. For teams already on Google Cloud, Gemini is compelling. For general development, Copilot's track record and lower cost make it safer.

Gemini Code Assist supports 20+ languages including Java, JavaScript, TypeScript, Python, C, C++, C#, Go, PHP, Ruby, Kotlin, Swift, Rust, SQL, and more. The quality is consistent across major languages, though Java and Python have the most training data. It works in VS Code, JetBrains IDEs (IntelliJ, PyCharm, GoLand, WebStorm, Android Studio), Cloud Workstations, and Cloud Shell Editor. No Vim/Neovim support currently.

Gemini Code Assist requires an internet connection as the AI models run on Google Cloud infrastructure. Unlike Tabnine or some self-hosted options, there's no offline mode. This is standard for most AI coding assistants (including GitHub Copilot and Cursor). If your team requires air-gapped development or complete data isolation, consider Tabnine's self-hosted deployment or train your own models with tools like Continue.dev.

Some users report slow response times and occasional accuracy issues, particularly compared to GitHub Copilot's more mature system. DORA research also found that 25% increases in AI coding tool adoption correlated with 1.5% reductions in delivery throughput-suggesting that individual productivity gains don't always translate to team-level improvements. The tool is less proven than Copilot, though Google continues rapid iteration with Gemini 2.5 Pro improvements.

Custom Commands create personalized shortcuts for repetitive tasks (e.g., '/test-all' runs your entire test suite, '/deploy-staging' triggers deployment). Rules enforce project-specific conventions automatically (e.g., 'always use async/await instead of promises', 'enforce camelCase for variables'). Together, they let teams standardize workflows while automating boilerplate. Custom Commands are available on Standard tier; Rules engine requires Enterprise.