DeepMind reorganised its leadership after Demis Hassabis stepped from CEO into a chair role, while chief scientist Jeff Dean also left. Gemini 3.5 Pro has missed three release dates since its May I/O reveal, and independent tests say Gemini 3.6 Flash trails multiple major rivals in raw intelligence. DeepMind is shifting day-to-day control to the CTO, losing key talent, and trying to regain momentum without its main scientific leaders while power concentrates closer to Google headquarters.
Google launched Gemini 3.7 Flash, its most capable entry-level model for coding and AI agent projects. It is rolling out 3 weeks after Gemini 3.6 Flash and is priced at half the cost of Gemini 3.6 Flash through the end of the year. Developers get improved coding and UI generation plus added ability to power AI agent ensembles and to browse/use Google services via Gemini Spark.
Gemini 3.7 Flash launched Thursday as Google’s new coding-agent and workflow workhorse instead of Gemini 3.5 Pro. DeepSWE v1.1 rose from 49% on Gemini 3.6 Flash to 65.3% on Gemini 3.7 Flash. Developers can use the new thinking_level settings and face API pricing that doubles on January 1, 2027, plus migration steps if moving from older Gemini models.
Judge James Donato ordered Google to make it easier to install rival Android app stores, restarting proceedings with Epic Games in San Francisco court.
Google released Gemini 3.7 Flash, a refined update in its Flash tier, for multi-modal input and agent-style use via hosted APIs. It costs $0.75 per 1M input tokens (and $3.75 per 1M output tokens). Pricing drops and deployability remains API/enterprise-only, with reported gains on coding and document-work benchmarks alongside some reasoning regressions.
Google announced Gemini 3.7 Flash, replacing Gemini 3.6 Flash just three weeks after the prior Flash release rather than delivering the expected 3.5 Pro.
Google reorganized DeepMind’s leadership, with Jeff Dean leaving to start a new lab and Demis Hassabis stepping aside, prompting debate about whether Google is slipping in frontier AI. The interview focuses on Gemini work that emphasizes “3.5” class models that have fallen on leaderboards. As researchers shift away from long-term work toward product-focused efforts, the story suggests Google may keep catching up rather than repeatedly taking the lead in state-of-the-art models.
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