Google I/O 2026：Gemini 3.5 Flash、Omni、および Google のエージェントスタックを発表

Google is so back!

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Top Story: Google I/O recap and new Gemini technical details

What happened

Google used I/O to reposition Gemini as both a consumer AI surface and a developer/agent platform, with three core technical announcements: Gemini 3.5 Flash for fast agentic/coding workloads, Gemini Omni for multimodal generation/editing starting with video, and a broader Antigravity agent stack spanning desktop/CLI/SDK/API. Official posts emphasized scale — Google says it now processes over 3.2 quadrillion tokens/month, up 7x YoY from 480T/month, while the Gemini app has 900M+ monthly users and is available in 230+ countries and 70+ languages (Google, Google, GeminiApp). The most technically substantive release was Gemini 3.5 Flash, framed by Google as its strongest agentic/coding model yet, GA immediately, with 1M-token context, 65k max output, 4 thinking levels (“minimal/low/medium/high”), and “thought preservation” across turns (GoogleDeepMind, Google, _philschmid). Google paired that with Gemini Omni, a new family combining Gemini reasoning with generative media, initially via Omni Flash, capable of taking text/image/video/audio inputs and producing video edits/generation in Gemini, Flow, Shorts, and later APIs (GoogleDeepMind, Google, GeminiApp). Around those models, Google launched or expanded Antigravity 2.0 desktop, CLI, SDK, Managed Agents in the Gemini API, Search-native generative UI/coding, Gemini Spark background agents on cloud VMs, and a long list of Gemini-app/Workspace/commerce/media integrations (Google, Google, Google).

Facts vs. opinions

Facts / directly claimed by official or third-party benchmark sources

• Google says it now processes 3.2 quadrillion tokens/month, up from 480 trillion a year earlier (Google).

• Google says Gemini has 900M+ monthly users (Google).

• Google says Gemini 3.5 Flash is GA today across Gemini app, Search AI Mode, Gemini API, AI Studio, Antigravity, Android Studio, and enterprise surfaces (Google, GeminiApp).

• Google says Gemini 3.5 Flash has 1M context, 65k max output, 4 thinking levels, and “thought preservation” across turns ( _philschmid).

• Google says 3.5 Flash beats Gemini 3.1 Pro on Terminal-Bench 2.1, GDPval-AA, and MCP Atlas (GoogleDeepMind, Google).

• Google says 3.5 Flash runs 4x faster than comparable frontier models, and up to 12x faster in Antigravity (Google, JeffDean).

• Independent benchmarker Artificial Analysis reports Gemini 3.5 Flash scores 55 on its Intelligence Index, +9 vs Gemini 3 Flash, at >280 output tok/s, with MMMU-Pro 84%, GDPval-AA Elo 1656, and pricing of $1.50 / $9.00 per 1M input/output tokens; it also reports the model is 5.5x costlier to run than Gemini 3 Flash on its suite and 75% costlier than Gemini 3.1 Pro (ArtificialAnlys).

• Arena reports Gemini 3.5 Flash reached #9 overall in Text Arena and #9 in Code Arena: Frontend, scoring 1507, a +70 jump over Gemini 3 Flash, and becoming the top score in its price tier (arena).

• Google says Gemini Omni Flash is available in Gemini/Flow today for paid users, in Shorts/Create starting this week for free, and via APIs in coming weeks (Google).

• Google says Spark runs on dedicated Google Cloud virtual machines, allowing long-running tasks while user devices are closed (Google).

• Google claims an Antigravity + Gemini 3.5 Flash demo built a functioning OS in 12 hours using 93 parallel sub-agents, 15k+ model requests, 2.6B tokens, and < $1K API credits (Google).

• Google says Search will use Antigravity + 3.5 Flash to generate custom visual tools/simulations on the fly (Google).

Opinions / interpretations / skepticism

• Positive takes: “Google is back,” “insane evals for a Flash model,” “world model towards AGI,” “mind blowing” for Search + Antigravity, etc. (kimmonismus, Kseniase_, demishassabis).

• Neutral caution: some posters explicitly avoided overhyping due to self-reported benchmarks and noted pricing/perf concerns (scaling01, simonw).

• Negative/skeptical takes focused on:

Price inflation relative to earlier Flash models (enricoros).

• Comparisons where GPT-5.5-medium may be smarter/cheaper/faster end-to-end (scaling01, scaling01).

• Benchmark caveats such as weak TerminalBench-Hard, mediocre MRCR / ARC-AGI-2, or not clearly beating Kimi/GLM on some slices (scaling01, teortaxesTex, scaling01).

• Product naming/UX confusion around Gemini CLI vs Antigravity CLI and broader interface design criticism (zachtratar, kchonyc, teortaxesTex).

Gemini 3.5 Flash: the main technical release

Official positioning

Google/DeepMind repeatedly described Gemini 3.5 Flash as the company’s strongest model yet for agents and coding, not its absolute flagship intelligence model. It’s meant to sit on the high-speed, high-utility part of the Pareto frontier, powering both Google products and developer workloads (GoogleDeepMind, Google, SundarPichai).

Technical details and metrics

From Google and affiliated posts:

• GA availability now (Google)

• 1M token context window

• 65k max output tokens

• Thinking levels: minimal, low, medium (new default), high

• Thought preservation across multi-turn conversations

• Text output

• Input modalities: text, image, video, speech per Artificial Analysis ( _philschmid, ArtificialAnlys)

• Pricing: $1.50 / 1M input, $9.00 / 1M output, 90% discount on cached input (scaling01, ArtificialAnlys)

Official benchmark claims:

• Terminal-Bench 2.1: 76.2%

• GDPval-AA: 1656 Elo

• MCP Atlas: 83.6%

• Google-quoted multimodal result: MMMU-Pro 83.6% in one engineer post; Artificial Analysis reports 84%, highest recorded on its setup (koraykv, ArtificialAnlys)

Speed claims:

• Google marketing claim: 4x faster than comparable frontier models (Google)

• In Antigravity, Google says it is up to 12x faster (JeffDean, scaling01)

• Artificial Analysis observed >280 output tok/s

• Some discussion cited ~867 tok/s in Antigravity-specific optimized serving (scaling01, scaling01)

Third-party evaluation:

• Artificial Analysis says 3.5 Flash is the leader on the intelligence-vs-speed Pareto frontier, but the economics are notably worse than prior Flash:

Intelligence Index 55

• +9 over Gemini 3 Flash

• Hallucination rate reduced to 61%, a 31-point drop vs Gemini 3 Flash on its omniscience setup

• GDPval-AA 1656 Elo

• 5.5x costlier than Gemini 3 Flash to run on its benchmark suite

• 75% costlier than Gemini 3.1 Pro on the same suite (ArtificialAnlys)

Arena:

• #9 Text Arena

• #9 Code Arena: Frontend

• 1507 score, +70 over Gemini-3 Flash

• Better than Gemini 3.1 Pro across categories in its frontend coding eval (arena, arena)

Implications

The notable shift is that Google appears to be using a “Flash” label for a model that, in prior cycles, would have been described more like a high-end product model optimized for deployment rather than simply a cheap lightweight tier. Several posters called this out directly, arguing Flash is becoming more expensive and possibly absorbing former Pro territory (enricoros, simonw).

The strongest technical signal is not “best absolute benchmark model,” but:

• material agentic gains

• extreme serving speed

• deep integration into product surfaces

• tooling built around subagents and long-horizon execution

That makes 3.5 Flash strategically important even if some competitors still win on raw price-adjusted intelligence in certain third-party comparisons.

Gemini Omni: multimodal generation/editing as “create anything from any input”

What Google announced

Google introduced Gemini Omni as a new family merging Gemini reasoning/world knowledge with Google’s generative media stack, starting with video creation and editing. Official messaging described it as “create anything from any input,” but current rollout is narrower:

• Inputs: text, images, audio, video

• Initial output emphasis: video

• Product availability: Gemini app, Flow, YouTube Shorts/Create, later APIs

• Current shipping model: Gemini Omni Flash (GoogleDeepMind, Google, Google)

Google/DeepMind claims:

• Better world understanding

• More robust physics

• Multi-turn editing where scene/character consistency is retained

• Ability to “reimagine” user video footage with conversational edits (Google, Google)

Rollout specifics:

• Paid Gemini users globally in app/Flow “today”

• YouTube Shorts/Create rolling out “starting this week” at no cost

• APIs for developers/enterprise in coming weeks (Google, GeminiApp)

Perspectives

• Supportive: users and Google employees described Omni as a major quality step, especially for video editing and consistency (joshwoodward, fofrAI, osanseviero).

• Strategic interpretation: several posters framed Omni as evidence Google is investing in world models and embodied/physical priors, not just text/code competition (demishassabis, jparkerholder, kimmonismus).

• Skepticism: some UI/output examples drew criticism for looking like “B-tier video game interface” or too polished/template-like (teortaxesTex, shlomifruchter).

Context

Omni matters less as “yet another video model” and more as Google’s attempt to unify:

• multimodal understanding,

• media editing,

• world grounding,

• agent interfaces,

• and eventually any-input/any-output generation.

This aligns with DeepMind’s long-running world-model agenda and Google’s product distribution advantage.

Antigravity: Google’s agent OS, not just a coding assistant

A major underappreciated I/O theme was that Google is no longer presenting agents as a thin wrapper around a chat model. Antigravity is becoming the execution substrate.

What launched / expanded

• Antigravity 2.0 desktop app: agent-first desktop with core conversations, artifacts, multi-agent orchestration (Google, Google)

• Antigravity CLI (Google, Google)

• Antigravity SDK (Google)

• Managed Agents in Gemini API: single API call gives an agent plus hosted Linux sandbox; supports Bash/Python/Node, files, browsing, custom markdown-defined skills, repo/GCS mounts (Google, GoogleAIStudio, _philschmid)

• Integrations with AI Studio, Android, Firebase, Workspace, web (Google, Google)

• One-click export from AI Studio to Antigravity (Google)

• Native Android app generation in AI Studio / Android support in Antigravity (Google, AndroidDev)

Technical signaling

Google’s own demos centered on parallel sub-agents, hosted execution, high-frequency iterative loops, and artifact-oriented workflows. Jeff Dean explicitly described 3.5 Flash as a strong engine for “deploy sub-agents that collaborate, run high-frequency iterative loops, and solve real-world problems at scale” (<a href="https://x.com/JeffDean/