Google just released Gemini 3.5 Flash at Google I/O May, 2026. It is the first Gemini 3.5 model. The series combines frontier intelligence with action. Google calls it a major leap for intelligent agents. The Flash tier has historically been faster and cheaper. 3.5 Flash outperforms Gemini 3.1 Pro on challenging benchmarks. The previous premium tier has now been surpassed.
What the Benchmarks Say
Gemini 3.5 Flash scores 76.2% on Terminal-Bench 2.1. That benchmark tests coding performance. It scores 1656 Elo on GDPval-AA. That measures real-world agentic task performance. It scores 83.6% on MCP Atlas. MCP Atlas measures scaled tool-use reliability. It scores 84.2% on CharXiv Reasoning. That benchmark tests multimodal understanding.
Gemini 3.5 Flash is 4x faster on output tokens. Tasks often complete at less than half the cost. Official pricing is $1.50 per million input tokens. Output tokens cost $9.00 per million. Cached input is priced at $0.15 per million.
The context window is 1,048,576 input tokens. Maximum output is 65,536 tokens. Supported inputs are text, image, audio, and video. The knowledge cutoff is January 2026. Dynamic thinking is on by default. The model auto-allocates more compute for harder problems.
Built for Agentic and Long-Horizon Tasks
Here ‘Agentic’ means the model plans, calls tools, and iterates. It completes multi-step goals, not single questions. ‘Long-horizon’ means that loop runs for extended periods. Google introduced Managed Agents in the Gemini API. One API call spins up a full agent. It reasons, uses tools, and executes code. The environment runs inside an isolated Linux container. Files and state persist across follow-up calls. This enables seamless multi-turn agent sessions.
Previously, managing agent state and environments was manual. The Managed Agents API abstracts that infrastructure entirely.
The Antigravity Ecosystem
Google Antigravity is its agent-first development platform. It takes ideas to production-ready apps. Antigravity 2.0 is a new standalone desktop app. It orchestrates multiple agents running in parallel. Dynamic subagents handle parallelized workflows. Scheduled tasks enable background automation. Integrations cover Google AI Studio, Android, and Firebase.
The Antigravity CLI is for terminal-based developers. It creates agents instantly, without a GUI. Google encourages Gemini CLI users to migrate now. The Antigravity SDK gives programmatic access to the harness. You can define custom agent behaviors with it. Host agents on infrastructure of your choice.
Real-World Enterprise Deployments
According to Google, several enterprise partners are already running 3.5 Flash. Shopify runs subagents in parallel for data analysis. It powers more accurate merchant growth forecasts globally. Macquarie Bank is piloting it for customer onboarding. The model reasons over complex 100+ page documents. It retrieves information and makes reliable recommendations.
Salesforce is integrating 3.5 Flash into Agentforce. It automates enterprise tasks using multiple subagents. Subagents retain context across complex, multi-turn tool calling. Ramp uses it for smarter OCR on invoices. It pairs multimodal understanding with historical pattern reasoning. Xero deploys agents for complex, multi-week workflows. One example is gathering supplier data for 1099 forms. Databricks uses agentic workflows for real-time data monitoring. The model diagnoses issues and proposes fixes for engineers.
Check out the Technical details. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well.
Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? Connect with us
Michal Sutter is a data science professional with a Master of Science in Data Science from the University of Padova. With a solid foundation in statistical analysis, machine learning, and data engineering, Michal excels at transforming complex datasets into actionable insights.
Credit: Source link
