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 @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 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 Yann LeCun on X, the post titled “Tired of winning” links to results highlighting Meta AI’s strong performance on recent benchmarks; as reported by LeCun’s tweet and Meta AI’s shared materials, the models demonstrate competitive scores on reasoning and vision-language tasks, indicating continued progress in open AI research. According to Meta AI’s public benchmark summaries cited in the linked post, improved performance on long-context understanding and multi-step reasoning suggests near-term opportunities for enterprises to deploy more accurate retrieval-augmented generation and agentic workflows. As reported by Meta’s AI research updates that LeCun frequently amplifies, these gains can reduce inference costs by enabling smaller models to meet production thresholds, opening pathways for cost-optimized copilots, analytics assistants, and edge inferencing in 2026.

According to DeepLearning.AI, Artificial Analysis has launched version 4.0 of its Intelligence Index, introducing new evaluation tests that focus on economically useful work, factual reliability, and reasoning. This update replaces outdated, saturated benchmarks to more accurately assess how large language models perform in real-world business scenarios. As reported by DeepLearning.AI, the new benchmarks are designed to reflect the models' capabilities in delivering value for enterprises, offering actionable insights for organizations assessing AI integration in business operations.

According to Anthropic on Twitter, their analysis shows that the longer advanced language models such as Claude3 engage in reasoning, the more incoherent their outputs become. This trend was observed consistently across all tested tasks and models, including measurements based on reasoning tokens, agent actions, and optimizer steps. This finding highlights significant challenges for businesses and developers relying on large language models for complex, extended reasoning, suggesting a need for improved coherence management in future AI solutions.

According to @GoogleDeepMind, 2025 marked a breakthrough year for AI agents, advanced reasoning, and scientific discovery, driven largely by innovations at Google and its partners (source: @GoogleDeepMind, Dec 23, 2025). The recap highlights the deployment of next-generation AI agents capable of autonomous problem-solving across diverse sectors such as healthcare, sustainability, and research. These AI systems demonstrated robust reasoning capabilities, enabling more accurate scientific simulations, drug discovery, and real-time data analysis. For businesses, the integration of AI agents opens new opportunities to automate complex workflows, accelerate innovation cycles, and unlock competitive advantages in R&D-intensive industries. The progress signals a shift toward AI-driven enterprises and underscores the market potential for tailored AI solutions in both established and emerging markets.