Gemini 3.5 Flash might be fast enough for gen AI to make sense

2026-05-19 · Source: AI - Ars Technica · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Intermediate, short

Summary

Google has launched Gemini 3.5 Flash, an efficient AI model designed to make complex agentic tasks viable at scale. This new model outputs nearly 300 tokens per second, achieving benchmark scores comparable to larger frontier models like Gemini 3.1 Pro, which operates at a quarter of the speed. Its API pricing is significantly lower than 3.1 Pro, at \$1.50 per 1M input tokens and \$9 per 1M output tokens, potentially saving large AI users a billion dollars annually. Gemini 3.5 Flash shows substantial improvements in code generation benchmarks (Terminal Bench, SWE-Bench Pro) and OSWorld-Verified tasks, even slightly surpassing Gemini 3.1 Pro and matching OpenAI's GPT 5.5. It is rolling out across Google products, including an upgraded Antigravity IDE 2.0. Concurrently, Google introduced Gemini Spark, a dedicated 24/7 cloud-based AI agent utilizing 3.5 Flash to manage tasks across a user's Google ecosystem, available to AI Ultra subscribers. Additionally, Gemini Omni Flash, a new multimodal model, is replacing Veo for video generation, aiming for a unified input/output experience across various data types.

Key takeaway

For AI Engineers and ML Directors evaluating models for agentic workflows, Gemini 3.5 Flash presents a compelling option. Its high token output rate and competitive benchmarks, coupled with significantly lower API costs, make complex, long-running AI tasks more economically viable. You should consider integrating 3.5 Flash for applications requiring efficient code generation, tool use, or cross-platform automation, potentially utilizing Gemini Spark for dedicated agentic solutions.

Key insights

Gemini 3.5 Flash demonstrates that high intelligence and efficiency can enable scalable agentic AI.

Principles

• User feedback is crucial for post-training model refinement.
• Balancing AI quality and cost expands viable use cases.
• Multimodal models simplify diverse data processing.

Method

Model improvements stem from pre-training advancements combined with post-training insights gleaned from developer usage feedback.

In practice

• Deploy efficient models for complex agentic tasks.
• Utilize AI agents for cross-platform task automation.
• Experiment with multimodal models for varied content generation.

Topics

• Gemini 3.5 Flash
• AI Agents
• Multimodal AI
• LLM Efficiency
• Code Generation
• Google Gemini

Best for: CTO, VP of Engineering/Data, AI Architect, AI Engineer, Machine Learning Engineer, Director of AI/ML

Related on AIssential

• Gemini 3.5 Flash: Frontier Intelligence with Speed — Gemini 3.5 Flash delivers rapid, low-latency AI for agentic workflows, coding, and multimodal reasoning.
• Gemini 3.5 Flash: Google's Most Powerful Model Ever! Beats Opus 4.7 & GPT 5.5? (Fully Tested) — Gemini 3.5 Flash offers superior coding and creative generation at a higher cost and token usage than previous flash models.
• Gemini 3.5 Flash Looks Good For How Fast It Is — Gemini 3.5 Flash offers high speed for agentic tasks, but its intelligence and cost-effectiveness are debated.
• Gemini 3.5: frontier intelligence with action — Gemini 3.5 Flash delivers frontier agentic and coding performance at high speed and reduced cost.
• Google launches Gemini 3.5 Flash as its default AI model — Google's Gemini 3.5 Flash offers high-speed, cost-effective AI for agentic tasks, becoming the default model.
• Gemini 3.1 Flash Lite in 14 mins! — Gemini 3.1 Flash Lite offers high-speed, cost-effective multimodal AI capabilities for diverse applications.

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI - Ars Technica.