Gemini 3 Flash Preview takes 40–50 seconds to respond

What is happening with latency in Google AI Studio

For at least some Google AI Studio users, Gemini 3 Flash Preview suddenly no longer feels very flash. Since midday on September 23, 2026, they have reported response times of 40–50 seconds instead of the usual 2–3 seconds. The available information includes no official Google confirmation of a distinct incident, so it is too early to call this a global outage.

The contrast is particularly striking given Google’s official documentation for Gemini 3 Flash Preview. The model remains in public preview and is positioned as an option focused on low latency and efficiency rather than maximum reasoning depth. Delays measured in tens of seconds directly undermine its main use case: rapid cycles of prompting, checking, and refining.

Independent benchmarks previously showed a very different picture. Some measurements put time to first token at roughly 0.91–0.95 seconds, with generation speeds reaching 195.8–218 tokens per second. Another comparison for Google AI Studio reported 1.17 seconds of latency and throughput of 74 tokens per second, showing that variation existed even before the current complaints.

The Google developer forum also contains more severe examples: a paid Tier 2 user reported responses taking 80–356 seconds along with frequent 503 errors. According to that user, Google support acknowledged that latency was exceeding target levels. In another thread, sharp slowdowns were linked to context sizes of around 50,000 tokens, interface rendering, and token-count requests.

For now, I would not conclude that the model itself has degraded. Routing, quotas, infrastructure, or the AI Studio interface could all be responsible, and externally these issues can look nearly identical.

Why this breaks the rapid development loop

For prototyping, this level of delay turns an interactive tool into a task queue. When every short request takes 40–50 seconds, prompt debugging, structured-output checks, and variant comparisons quickly lose their value.

The first step should be to separate time to first token from total generation time, then compare short and long contexts. Error 503 points toward the infrastructure layer, while degradation only near 50,000 tokens looks more like context scaling or an interface issue.

The main uncertainty is not whether AI Studio is slow for some users; the reports already make that clear. The question is where the delay originates: inside Gemini 3 Flash Preview itself or somewhere around it.

We previously compared Gemini with dedicated meeting-summary tools for Google Meet, including accuracy and operational limitations. That comparison adds context for teams assessing how Gemini service instability can affect day-to-day workflows.