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

"Gemini 3.5 Flash is rolling out across a wide range of Google products starting today, and Google again claims this model is even better than its last-gen Pro model. That has been a trend with Google's tick-tock model updates over the past year, but the team says this release is special. Gemini 3.5 Flash supposedly offers frontier-level intelligence while also being efficient enough that it may finally make complex agentic tasks worth doing at scale."

"The problem is magnified when you start building agentic experiences that are supposed to run for longer to complete complex tasks. Gemini 3.5 Flash may be a big step toward making that viable. The new model can output nearly 300 tokens per second, but its benchmark scores are similar to larger frontier models (like 3.1 Pro) that build outputs at a quarter of that speed."

"According to Doshi, the team made numerous improvements in pre-training with Gemini 3.5 Flash, but insights gleaned from how devs use Gemini models are really paying off. "With post-training, we're really starting to unlock some of the value of the feedback we're getting from users, for example, from Antigravity," said Doshi."

""That's really what you're seeing play out in terms of the code performance and the tool use performance. And then, the hope is that you'll continue to see the step change where 3.5 Pro will be better,"

Gemini 3.5 Flash is rolling out across many Google products and is positioned as more capable than the prior Pro model. The release follows rapid Gemini version updates and emphasizes that the improvements are integrated across multiple products. The model targets efficiency challenges in generative AI, especially for agentic experiences that must run longer to complete complex tasks. Gemini 3.5 Flash can output nearly 300 tokens per second while achieving benchmark results similar to larger frontier models that generate outputs at about a quarter of that speed. Improvements come from pre-training and from post-training that uses feedback from developer usage, improving code performance and tool use performance.

Read at Ars Technica