Google CEO distracts from Gemini 3.5 Pro delay with talk of Gemini 4 and monthly releases

Google CEO Sundar Pichai deflected questions about the delayed Gemini 3.5 Pro large language model during the company's quarterly earnings call on Wednesday by focusing on the next-generation Gemini 4 and plans to release new LLMs at an almost monthly cadence. Pichai's comments came after Google unveiled Gemini 3.6 Flash and 3.5 Flash Cyber but offered no update on Gemini 3.5 Pro, which Bloomberg reported is months late due to coding performance falling short of internal expectations compared to models from OpenAI and Anthropic. Analysts warned that a monthly release cadence could burden CIOs with increased testing and governance costs.

Google CEO Sundar Pichai has sought to allay concerns over the delayed release of the Gemini 3.5 Pro large language model. He dodged questions about it in Google’s quarterly earnings call on Wednesday by focusing on the company’s next frontier AI model, Gemini 4, and plans to release subsequent LLMs at an almost monthly cadence. His comments came a day after Google unveiled Gemini 3.6 Flash https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-3-6-flash-3-5-flash-lite-3-5-flash-cyber/ and 3.5 Flash Cyber but offered no update on the release of Gemini 3.5 Pro, the company’s delayed flagship reasoning model that many developers had expected to arrive weeks earlier. Google introduced the Gemini 3.5 family at its annual I/O conference, promising to release the Pro model in June. That timeline has since slipped, with Bloomberg suggesting Gemini 3.5 Pro is months late http://bloomberg.com/news/articles/2026-07-16/google-gemini-launch-delayed-as-tech-falls-short-of-internal-goals because the model’s coding performance is falling short of internal expectations, especially when compared to better performance by similar models from OpenAI and Anthropic. Instead of revisiting the Gemini 3.5 Pro timeline, Pichai used the earnings call to shift the discussion toward Gemini 4, when asked about how his company planned to navigate an increasingly competitive race to release frontier AI models by to Barclays Investment Bank analyst Ross Sandler. “We are creating a baseline on top of which you will see us rapidly iterate on subsequent model releases. And so picking up pace and releasing models almost at a monthly cadence is part of our road map as we are building Gemini 4 as well,” Pichai said during the call https://www.youtube.com/watch?v=LzExSq9DU9w . Sandler’s question followed one from JPMorgan Chase & Co analyst Douglas Anmuth https://www.linkedin.com/in/douglas-anmuth-9229621/ , who asked Pichai if Google was releasing frontier AI models frequently enough to keep pace with rivals OpenAI and Anthropic. Pichai had responded to Anmuth’s question that Google remained confident of competing at the frontier and was investing heavily in a larger Gemini 4 base model. Analysts, though, aren’t as confident as Pichai. While delays to Google’s frontier model roadmap have not triggered an exodus of existing customers, either because of high switching costs or because many enterprises already running multi-model architectures, they have made CIOs evaluating AI platforms more cautious about making new commitments, said Bhupendra Chopra https://www.linkedin.com/in/bhupendrachopra , chief revenue officer at IT consulting firm Kanerika. A monthly model release cadence could prove to be a double-edged sword for enterprises and their CIOs. While a monthly release cadence could help enterprises gain faster access to improvements in model performance, cost and capabilities, it will also require CIOs to invest more heavily in testing, governance and version management to safely adopt those updates, said Sanchit Vir Gogia https://greyhoundresearch.com/svg/ , chief analyst at Greyhound Research. Similarly, Pareekh Jain https://pareekh.com/about/ , principal analyst at Pareekh Consulting, said enterprises will embrace a faster release cadence only if each successive model delivers measurable improvements in performance, cost or safety, rather than simply changing version number. The challenge for CIOs, Jain said, is not just keeping up with model releases; it’s deciding whether each new version is worth the cost of validating it.