Multimodal AI

Google has introduced Gemini 3.5 at Google I/O 2026 with a clear change in direction. The model is no longer framed as a chatbot alone, but as an AI system built to carry out agentic workflows more independently. That shift matters because Gemini 3.5 is aimed at tasks that require long context, multiple steps, and tool access. Google is positioning it as a platform for work that can be decomposed, assigned, and completed with far less manual supervision. Agentic coding takes center stage One of the most notable features is agentic coding. Gemini 3.5 is designed to break complex work into multi-step plans, delegate sub-tasks to sub-agents, use prior context, and then apply available tools to finish the job. This approach separates it from traditional chatbots that mainly respond to single prompts one at a time. In software development scenarios, it moves the model closer to how real technical teams organize and execute work. Google is also backing that direction with Antigravity, a development platform built for an agent-first workflow. The platform includes a desktop app, SDK, CLI, and voice support for rapid prototyping, complex task handling, and multi-agent orchestration. Long context is a major advantage Gemini 3.5 supports a context window of up to 1 million tokens and output of up to 65,000 tokens. That scale is intended for long conversations, large codebases, and massive documents without losing track of important details too quickly. For enterprise users and developers, that capacity is important because many AI workflows fail when a model cannot retain enough information from long inputs. With this range, Google is targeting use cases that feel closer to production workloads. The model also uses a thinking or reasoning system with multiple levels of capability, from minimal to high. Each level carries its own cost, allowing users to balance response quality and efficiency depending on the task. Multimodal performance is built in Gemini 3.5 is trained with native understanding across multiple input types. It can process text, images, video, audio, charts, and other formats within a single reasoning framework. Google says the model delivered 84.2 percent on the CharXiv benchmark for reasoning and interpretation, and 83.6 percent on MMMU-Pro for advanced multimodal understanding. Those figures suggest that multimodal capability is a core design goal rather than an added feature. In practical terms, that means Gemini 3.5 is meant to handle data across formats more naturally in both analysis and content-generation tasks. The model is being built to move fluidly between media types instead of treating them as separate problems. Speed, integrations, and broader use cases Google also highlighted Gemini 3.5 Flash, which aims to deliver intelligence close to the Pro version at a much lower cost. Even with that efficiency focus, the model still includes multimodal capability, context awareness, and platform integration. That makes the Flash variant relevant for larger-scale deployment, especially in applications where fast responses matter. A lower-cost model with broad capability could expand adoption across business environments. Gemini 3.5 is also connected to Google Search grounding, Google Maps, code execution, and URL context. Google says those links are meant to improve completeness and accuracy in generated responses. Support for third-party platforms such as Shopify, Box, and Databricks widens the automation potential further. The result is a system that can fit into workflows involving data, documents, online stores, and analytics. Translation and video generation add more reach Beyond coding and enterprise work, Google showed real-time voice translation in more than 70 languages. In the Google I/O 2026 demonstration, the system also tried to preserve the speaker’s tone, pace, and pitch in a natural way. That feature points to a broader focus on communication that feels more fluid and less mechanical. The emphasis is not only on language accuracy, but also on the quality of delivery. Google also introduced a video-generation model called Omni within the Gemini 3.5 ecosystem. The model is said to turn both simple and complex prompts into cinematic video output. With these additions, Gemini 3.5 is shaping up as a broader AI package centered on autonomy, long-context reasoning, and multimodal capability. Google says more features are still to come as the platform continues to develop. Source: tech.sportskeeda.com

Gemini 3.5 Shifts Beyond Chat, Google Pushes AI Toward Autonomous Workflows