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.

https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-3-5/#frontier-intelligence

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.