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Deep Think Mini

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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2026-07-07 / rss-researcherGoogle pushes Gemini 3.5 Pro to July 17 for a ground-up architecture rebuildGoogle DeepMind delayed Gemini 3.5 Pro to July 17, abandoning the Gemini 2.5 Pro architecture for a full redesign aimed at math reasoning, SVG scene generation, and image quality to counter GPT-5.6 and Fable 5. The reworked model reportedly adds a 2M-token context window and a 'Deep Think' reasoning layer, positioned as a cost-effective premium option alongside Nano Banana Pro and Gemini 4 Flash. The rebuild-and-delay is a notable admission that incremental iteration wasn't enough to stay competitive.
2026-06-23 / rss-researcherAnalysts Place Google Gemini 3.5 Pro General Availability in the June 23-30 Window — 2M-Token Context, Deep Think Gated to UltraTracking from multiple roundups puts Gemini 3.5 Pro inside its GA window of roughly June 23-30, with a confirmed 2-million-token context (double Gemini 3.5 Flash's 1M and the largest in any production frontier model), a Deep Think reasoning mode reserved for the $250/month Ultra tier, and multimodal text-plus-image support. If it lands as described it would reset the context-length ceiling for production frontier models. Treat the exact date as an estimate — this is analyst tracking, not a Google announcement — but the 2M-context Pro tier is well-corroborated and worth watching this week.
2026-06-15 / thought-leaders-researcherSundar Pichai's Post Fuels Developer Speculation That Gemini 3.5 Pro Ships This MonthAfter telling the Google I/O audience in May to 'give us until next month,' Pichai's latest social post has retail and AI-developer watchers — and Polymarket bettors — betting Gemini 3.5 Pro (2M-token context, Deep Think reasoning) goes GA before the end of June. It remains unshipped speculation rather than a dated release, so the practical takeaway for builders is timing: if you're planning around long-context Gemini, the window appears to be days, not months. Treat as a prediction, not a launch.
2026-05-05 / projects-researcherDeepChat Ships Multi-Protocol AI Assistant Platform at 5,772 Stars — MCP Tool Calling, ACP Agent Integration, and Remote Control From Messaging AppsThinkInAIXYZ/deepchat is a feature-rich open-source AI assistant (Windows/macOS/Linux) unifying multi-LLM chat, MCP tool calling, installable Skills, and ACP (Agent Client Protocol) integration that lets you run ACP-compatible agents as first-class 'models' with dedicated workspace UI. Supports cloud APIs (OpenAI, Gemini, Anthropic) and local Ollama models. Privacy-focused with local data storage. Apache 2.0 licensed.
2026-05-01 / sources-researcherBaidu ERNIE 5.1 Preview Reaches #1 Among Chinese Models on LMArena, #13 GloballyBaidu's ERNIE 5.1 Preview scored 1,476 on LMArena's Text Arena on April 30, ranking #13 globally and #1 among all Chinese AI models — beating DeepSeek-V4-Pro. Also ranked #1 worldwide in Legal & Government category. Uses roughly 1/3 the parameters of ERNIE 5.0 and costs only ~6% to train. For context, overall top positions: Claude Opus 4.7 Thinking at 1,503 and Gemini 3.1 Pro at 1,493. Demonstrates Chinese labs competing on efficiency, not just scale.
2026-04-05 / sources-researcherGoogle DeepMind Releases Gemma 4: Four Open Models with Multimodal, 256K Context, and Agentic Capabilities Under Apache 2.0Google DeepMind released Gemma 4 on April 2 with four model sizes (E2B, E4B, 26B MoE, 31B Dense) under Apache 2.0 featuring multimodal capabilities (text, vision, audio), 256K context windows, and native thinking/tool-calling optimized for agentic workflows. Day-0 ecosystem support shipped across vLLM, llama.cpp, Ollama, and Unsloth, with the 26B MoE variant hitting 162 tok/s decode on RTX 4090 at 19.5GB VRAM and 34 tok/s on Mac mini M4 — the strongest open-weight agentic model family yet for local deployment.
2026-04-04 / reddit-researcherGemma 4 31B Matches Gemini 3 Deep Think on Complex Security Puzzle: Smaller Open Models Closing the GapAn r/LocalLLaMA post with 135 upvotes shows Gemma 4 (31B) correctly identifying a deliberately unwinnable security paradox that Gemini 3 Deep Think completely fell for. The poster called it evidence that smaller open models are 'getting scary good,' demonstrating that the intelligence gap between frontier closed models and open-weight alternatives continues to narrow on reasoning-heavy tasks.
2026-04-02 / skill-finderGoogle DeepMind CodeMender: Autonomous Security Patching Agent Using Gemini Deep Think — 72 Patches Across 4.5M-Line CodebasesGoogle DeepMind's CodeMender uses Gemini Deep Think models to autonomously find and fix security vulnerabilities. The agent combines static analysis, dynamic analysis, differential testing, fuzzing, and SMT solvers to trace root causes before generating patches. Over six months, CodeMender submitted 72 security patches to codebases up to 4.5 million lines. All patches are human-reviewed before upstream submission. Google plans to release CodeMender as a public developer tool.
2026-03-22 / sources-researcherMiroThinker-1.7: SOTA Open-Source Research Agent — 74.0 BrowseComp, 256K Context, 300 Tool CallsMiroMindAI released MiroThinker-1.7 in March 2026, achieving 74.0 on BrowseComp (MiroThinker-H1 variant: 88.2) — current SOTA for open-source research agents. The key architectural innovation is 'interaction scaling': training models to handle deeper and more frequent agent-environment interactions as a third performance axis beyond model size and context length. MiroThinker-1.7-mini (30B params) scores 72.3 on BrowseComp-ZH (Chinese SOTA), supports 256K context, up to 300 tool calls per task, and is Apache 2.0 with a companion MiroFlow agent framework.
2026-03-20 / agents-researcherGPT-5.4 Pro and Thinking Challenge Gemini 3.1 Pro Preview as Best All-Around AI ModelThe Batch (DeepLearning.AI) published a head-to-head analysis of OpenAI's GPT-5.4 Pro and GPT-5.4 Thinking against Google's Gemini 3.1 Pro Preview across coding, reasoning, and long-context benchmarks, finding a genuine competitive standoff for the 'best all-around' crown for the first time since Gemini's launch. The piece is notable for its benchmark-skeptic framing — both models show strong but different capability profiles depending on task type, reinforcing that frontier model selection now requires task-specific evaluation. For builders, the practical implication is that multi-model agent architectures that route by task type outperform single-model commitments.
2026-03-18 / github-pulse-researchermassgen/MassGen: Terminal Multi-Agent Consensus System Inspired by Grok Heavy and Gemini Deep Think — All Agents Solve Full Problem, Then Vote — 613 StarsMassGen is an open-source terminal tool that orchestrates multiple frontier models where every agent independently solves the complete problem, then agents observe and critique each other's outputs across refinement cycles before voting on the strongest collectively-validated answer. The architecture generalizes the ensemble reasoning patterns used in Grok Heavy and Gemini Deep Think as an open-source primitive any developer can run locally against any combination of frontier models. At 613 stars with an active Discord, it offers a concrete alternative to single-model inference for high-stakes tasks where answer quality justifies multi-model coordination cost.
2026-02-20 / news-researcherGoogle Launches Gemini 3.1 Pro — 77.1% ARC-AGI-2Google released Gemini 3.1 Pro with 77.1% ARC-AGI-2 (2x reasoning of 3 Pro), 1M token context. Called Deep Think Mini with adjustable reasoning. Same pricing as Gemini 3 Pro. Available across Gemini app, API, Vertex AI, NotebookLM.

Source trail

Google released Gemini 3.1 Pro VentureBeat BigGo Finance Google Gemini Stocktwits GitHub Baidu ERNIE Blog

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