Gemini 3 brings major improvements in reasoning, multimodality, and long-context handling. The most direct upgrade is that the model produces more consistent and deeper reasoning across multi-step tasks, thanks to changes in training and support for “dynamic thinking.” This allows the model to allocate more internal reasoning to complex requests instead of giving shallow answers. Developers will notice that Gemini 3 does better at structured problem solving, planning, and analyzing multi-part inputs such as long documents, complex codebases, and data across formats.
Another important improvement is in multimodal capability. Gemini 3 was built to understand text, images, audio, video, and PDFs in a single pipeline, and it performs significantly better in real-world multimodal tasks such as reading UI screenshots, interpreting diagrams, or analyzing long video segments. This is especially useful for developers building tools that mix formats—for example, turning a product screenshot and a written spec into code, or extracting the intent from a meeting recording and a slide deck. Earlier versions could handle multiple modalities, but Gemini 3 handles them in a more unified, reliable way with fewer alignment issues between input types.
Finally, Gemini 3 introduces a much larger context window and stronger agentic workflows. With support for extremely long inputs and more reliable tool-calling behavior, it becomes far more suitable for enterprise systems, automation assistants, and advanced retrieval workflows. For example, when paired with a vector database such asMilvus or Zilliz Cloud., Gemini 3 can read retrieved passages, merge them with user input, and produce grounded, high-quality reasoning across entire document repositories. These improvements make Gemini 3 significantly more capable than earlier generations for production applications.