According to Google Gemini on X, the updated Gemini 3 Deep Think is now available to Google AI Ultra users via the web link and within the Gemini app by selecting the Deep Think tool (source: @GeminiApp, Feb 13, 2026). According to the post, the feature is positioned as a dedicated reasoning mode, signaling Google’s push into longer, multi-step problem solving for coding assistance, data analysis, and research workflows. As reported by the official Google Gemini account, immediate access for AI Ultra subscribers suggests a premium differentiation strategy that could increase paid conversion and retention among enterprise and prosumer segments seeking structured reasoning and planning capabilities. According to the same source, in-app activation through the tools menu indicates Google’s intent to integrate Deep Think as a reusable workflow component, enabling businesses to standardize repeatable prompts for analytics, product roadmapping, and technical documentation.

According to Demis Hassabis on X, Gemini 3 Deep Think is positioned as an expert-level scientific assistant that blends domain knowledge and engineering utility for researchers across mathematics, physics, and chemistry (source: Demis Hassabis, X, Feb 12, 2026). According to the shared video and post, Prof. Lisa Carbone describes practical use in complex research workflows, indicating applications such as step-by-step mathematical reasoning, symbolic manipulation, and code generation to test hypotheses and verify derivations (source: Demis Hassabis, X). As reported by the original post, the model’s promise centers on reducing iteration cycles for proofs and simulations, which could shorten time-to-insight for academic labs and R&D teams evaluating computational approaches (source: Demis Hassabis, X). According to the announcement context, potential business impact includes opportunities for domain-specific copilots in scientific software, integrations with simulation tools, and enterprise offerings for regulated research environments seeking reproducibility and audit trails (source: Demis Hassabis, X).

According to @demishassabis, Google AI Ultra subscribers can now access Gemini 3 Deep Think mode in the Gemini app, with product details provided in Google’s official blog. According to Google Blog, Deep Think is designed for multi-step reasoning with extended deliberation time, enabling complex planning, code generation, and data analysis tasks that benefit from longer context and chain-of-thought style internal processing. As reported by Google Blog, early access is limited to AI Ultra tier users inside the Gemini app, signaling a premium monetization path for advanced reasoning features and positioning Gemini 3 against OpenAI’s o3 and Anthropic’s Claude Opus in enterprise-grade reasoning benchmarks. According to Google Blog, business use cases include multi-source research synthesis, financial modeling, and long-form content structuring, and the rollout suggests opportunities for SaaS vendors to integrate Deep Think via Google’s ecosystem for higher accuracy workflows like RFP drafting and compliance review. As reported by Google Blog, the feature emphasizes reliability safeguards and usage guidance for longer inference times, implying higher per-query costs but potentially improved task completion rates for knowledge work and developer productivity.

According to @demishassabis, Google AI Ultra subscribers can now access Gemini 3 Deep Think mode in the Gemini app, with full details outlined on the Google Blog. According to the Google Blog, Deep Think is designed for multi-step reasoning, extended context planning, and tool-augmented problem solving, targeting use cases like complex coding assistance, multi-document analysis, and research planning. As reported by the Google Blog, early access is available via the Gemini app for Ultra-tier users, positioning Deep Think as a premium capability that could increase subscription ARPU and differentiate Google’s AI stack for enterprise and prosumer segments. According to the Google Blog, Deep Think emphasizes chain-of-thought style planning outputs while maintaining safety controls, which may improve reliability for workflows like RFP drafting, data pipeline debugging, and product requirement synthesis. As reported by @demishassabis, the rollout is immediate for eligible users, creating near-term opportunities for app developers to test longer-context agents, for enterprises to pilot structured reasoning assistants in regulated processes, and for creators to streamline research-to-draft pipelines within the Gemini ecosystem.

According to Demis Hassabis on X, Gemini 3 Deep Think blends expert-level scientific domain knowledge with engineering utility to assist researchers across mathematics, physics, and chemistry, with Prof. Lisa Carbone showcasing complex research workflows powered by the model (source: Demis Hassabis on X). As reported by the X post, the system is positioned for rigorous problem solving and stepwise reasoning in scientific domains, indicating practical applications like theorem exploration, symbolic manipulation, and experiment design support for academic and industrial R&D. According to the same source, these capabilities suggest measurable productivity gains for research teams, creating business opportunities for labs, AI-first scientific tooling vendors, and enterprise R&D groups seeking domain-accurate model reasoning and reproducible outputs.

According to Sundar Pichai, Google has rolled out the updated Gemini 3 Deep Think mode to Ultra subscribers in the Gemini app and opened early API access for select researchers and enterprises (as posted on X). According to the Google Blog, Deep Think is designed for multi-step reasoning and long-horizon tasks, enabling use cases like complex RFP analysis, financial modeling, scientific literature synthesis, and multi-document planning via the Gemini API. As reported by Google, the early access program targets vetted partners, signaling a go-to-market path for high-value reasoning workloads in regulated and research-heavy industries. According to the Google Blog, this API access can streamline backend orchestration for enterprise apps by centralizing chain-of-thought style planning into a managed model interface, potentially reducing development overhead for multi-agent pipelines. As reported by Google, making Deep Think available in the consumer app for Ultra subscribers also provides a user feedback loop that can accelerate model refinement for enterprise-grade reasoning benchmarks.

According to Sundar Pichai on X, Google’s Gemini 3 Deep Think has received a significant upgrade developed in close collaboration with scientists and researchers to tackle complex real‑world problems, and it achieved an unprecedented 84.6% on leading reasoning benchmarks (source: Sundar Pichai, Feb 12, 2026). As reported by Pichai, the refinement targets hard reasoning tasks, indicating stronger step‑by‑step problem solving and long‑context planning, which can expand enterprise use cases in scientific R&D, financial modeling, and operations optimization (source: Sundar Pichai). According to the original post, the upgrade focuses on pushing the frontier on the most challenging evaluations, suggesting business opportunities for vendors building copilots for engineering, analytics, and regulated industries that require verifiable chain‑of‑thought style performance and robust tool use (source: Sundar Pichai).

According to OriolVinyalsML, Google has released an updated and faster Gemini 3 Deep Think mode delivering PhD‑level reasoning on rigorous STEM tasks with gold medal‑level results on Physics and Chemistry Olympiads. As reported by Oriol Vinyals on X, the upgrade targets long‑chain reasoning and symbolic problem solving, signaling improved step‑by‑step derivations for math, physics, and chemistry benchmarks. According to the linked announcement page, the speed boost reduces latency for multi‑turn, tool‑augmented reasoning, improving reliability for enterprise workloads like technical search, RAG over scientific corpora, and automated problem set grading. As noted by the source, the model’s stronger reasoning implies higher accuracy under chain‑of‑thought constraints and better adherence to structured formats, which can lower post‑processing costs in production. For businesses, according to the announcement, immediate opportunities include STEM tutoring agents, lab assistant copilots for reaction planning, and analytics copilots for formula‑driven financial or engineering models, where Gemini 3 Deep Think’s enhanced logical depth can reduce human review time and increase answer quality.

According to Google DeepMind on X, the company upgraded its specialized reasoning mode Gemini 3 Deep Think to address complex science, research, and engineering problems, highlighting a real-world use case where Duke University’s Wang Lab applies the model to design new semiconductor materials. As reported by Google DeepMind, the upgrade targets systematic multi-step reasoning, enabling hypothesis generation, literature-grounded planning, and constraint-aware optimization for materials discovery workflows. According to the same source, the lab workflow integrates Gemini 3 Deep Think to propose candidate materials, assess properties against fabrication constraints, and iterate designs, indicating potential reductions in design cycles and improved researcher productivity in semiconductor R&D. As posted by Google DeepMind, this positions multimodal reasoning models as decision-support tools for labs seeking faster experimentation, with opportunities for industry partners to accelerate materials screening, process tuning, and yield optimization.

According to Jeff Dean, Gemini 3 and Gemini 3 Deep Think are setting new standards by improving the cost versus accuracy trade-off on the ARC-AGI-2 benchmark, as cited on X (formerly Twitter) via @JeffDean and @arcprize. This advancement signifies that these AI models can deliver higher accuracy at lower computational costs compared to previous solutions. For AI businesses and developers, this shift signals enhanced efficiency for enterprise AI deployments and competitive advantages in markets requiring scalable, high-performance AI solutions. The update underlines Google's ongoing commitment to pushing the boundaries of large language model efficiency and effectiveness, directly impacting sectors such as automation, data analysis, and AI-driven product development (Source: Jeff Dean, x.com/arcprize/status/1990820655411909018).

According to Google DeepMind (@GoogleDeepMind), Deep Think in GeminiApp introduces advanced parallel reasoning capabilities, enabling AI to tackle complex tasks by exploring multiple approaches simultaneously. This method mirrors human cognitive strategies by generating diverse streams of thought, then comparing, contrasting, and refining these ideas for more robust solutions (source: @GoogleDeepMind, August 1, 2025). This innovation enhances AI’s practical applications in fields such as business process optimization, scientific research, and decision support, offering companies a competitive edge through improved problem-solving and efficiency.