Google Gemini

Gemini is a family of multimodal large language models from Google DeepMind, accessed via API through Google AI Studio and Vertex AI. The core workflow is straightforward: you send requests — text, image, video, audio, or combinations — and receive generated text, code, or structured output. The API supports function calling and tool use, so agents running multi-step tasks can invoke external systems between reasoning steps without the model losing thread.

The differentiating feature is context depth. The vendor states that select Pro models support up to 2M tokens, which means a 100,000-line codebase, a multi-year document archive, or hours of video transcript can be analyzed in a single context without retrieval chunking. For tasks where chunking introduces retrieval errors — long-form document analysis, cross-file debugging, end-to-end research synthesis — this is the architectural gap that separates Gemini Pro from shorter-context competitors.

The model family is tiered by cost and capability. Flash and Flash-Lite variants retain a free tier with rate limits, making them usable for prototyping and low-volume applications. Pro models are paid-only. Teams building cost-sensitive production workloads at high volume frequently route simpler queries to Flash and reserve Pro for tasks that require the full context window or complex reasoning — maintaining that routing logic becomes an ongoing engineering commitment. The mixture-of-experts architecture supports this split, but the two-model strategy means you are debugging two latency profiles and two failure modes.