Gemini in your AI agents: what it does better (and worse) than competitors

Gemini is often underestimated in LLM discussions. After seriously testing it on several types of projects, here’s what I take away.

Long context: the killer argument

Gemini 1.5 Pro supports up to 1 million context tokens. To put that in perspective: that’s about 750,000 words, equivalent to several novels.

In practice, what does this change? For agents that need to analyze entire codebases, large document corpora, or very long conversation histories, Gemini 1.5 Pro is in a class of its own.

I tested it on a technical documentation analysis project: ingesting 400 pages of specs and answering cross-referenced questions. Gemini 1.5 Pro handled it without truncating. GPT-4o and Claude had to work in chunks.

Native multimodality

Gemini is multimodal by design, not by addition. It processes text, images, audio, and video in the same model.

For a PO agent, this opens interesting use cases:

• Analyzing wireframes or mockups directly
• Processing meeting recordings
• Extracting data from tables in images

In practice, quality on pure text remains slightly below Claude or GPT-4o on complex reasoning tasks. But on multimodal tasks, the advantage is real.

Google Workspace integration

If your client is in the Google ecosystem, this is the decisive argument. Gemini integrates natively with Google Docs, Sheets, Drive, Gmail. For agents that need to interact with these tools, no need for MCP servers or third-party APIs.

What disappointed me

Consistency on complex instructions. On highly constrained system prompts with many rules, Gemini tends to “drift” more than Claude. You need to pay more attention to prompt structure.

The API. The Google AI API is less mature than those from Anthropic or OpenAI. Documentation is less clear, SDKs less stable. It’s improving, but still a friction point.

Pricing. Gemini 1.5 Pro with long context gets expensive. At 1M context tokens, each request costs significantly more than a standard GPT-4o call.

When I recommend Gemini

• Project requiring analysis of very large document volumes
• Client in the Google Workspace ecosystem
• Multimodal use cases (images + text + audio)
• Budget available for long context

For everything else, Claude and GPT-4o remain my first choices.