Gemini API Features

Happy Gemini 3 Launch Day! After weeks of Twitter vague-posting, the curtain is finally up. And for once, the show lived up to the suspense. Gemini 3 has burst through the gates, crushing nearly every benchmark in sight. The surprise isn’t that it won, it’s just how far ahead it finished. Let’s get intros out of the way. Gemini 3 Pro is a Mixture-of-Experts transformer built from scratch - not a fine-tune or continuation of Gemini 2.5. It supports 1M-token inputs, 64K outputs, and native multimodality across text, code, images, audio, and video. The key trick - sparse activation - decouples capacity from cost. Instead of running all parameters for every token, Gemini activates only the subset most relevant to the task. That efficiency lets Google run frontier-level reasoning for mid-tier prices ($2 in / $12 out <200K tokens). The benchmark table reads like a paradigm shift: ▪️ ARC-AGI-2 (31 %) → 6x over Gemini 2.5, ~2x over GPT 5.1. Deep Think is even higher ~45%. No one else had cracked 20% before Gemini 3 blew past it. ▪️ScreenSpot-Pro (73 %) → first model to understand real app UIs - Photoshop, AutoCAD, spreadsheets. +2x over prior SOTA: 36% ▪️ t2-Bench & Vending-Bench 2 → best-ever scores in multi-step planning and resource management - the markers of real “agentic” behavior. ▪️LiveCodeBench (2,439 Elo) → elite coding precision and control, trailing Claude Sonnet 4.5 on SWE-Bench by just one pt (76.2 vs 77.2). Props to Anthropic for holding the slimmest of leads in coding - a small win amid Gemini’s full-court press. ▪️Humanity’s Last Exam (37.5%) → +10pts vs GPT-5.1 on judgment under ambiguity - early signs of reasoning, not recall. Together they mark a shift from language competence to tool-based agency: Gemini 3 can read, reason, plan, and act. One line from Sundar’s launch post stands out: “Gemini 3 is also much better at figuring out the context and intent behind your request, so you get what you need with less prompting." If Gemini 2 was reactive, Gemini 3 is anticipatory - and that change could transform user experience more than any performance metric. Gemini 3 launches everywhere simultaneously: Gemini App, AI Studio, Vertex AI, Gemini API, Google AI Mode, and even the new “Antigravity” platform, the quiet star of today’s launch. Antigravity is Google’s new agent-first IDE. It allows multiple agents to operate an editor, terminal, and browser in parallel while producing Artifacts - self-verifying logs of what they did and why. Think mission control for autonomous dev agents, complete with feedback loops and memory. It’s almost certainly the culmination of Google’s Windsurf acquisition and a formidable competitor for Cursor, Replit Agents, and every coding-copilot startup in the stack. America’s Next Top Model has arrived - and this one might just keep its crown through year-end.

For the first time, Google has a leading language model - Gemini 3;  the best in the planet so far. I will share some new, practical use cases you can actually leverage (especially the previous language models struggled with) But first, the two biggest improvements that are driving such strong industry feedback:   1- It’s significantly stronger on reasoning + multimodal understanding, meaning it handles text, images, video, and more, with much more nuance and depth. 2- It’s built for agent-style tasks: built-in tool use, multi-step workflows, planning + execution 𝗡𝗲𝘄 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀:   1) Run real multi-step tasks automatically It can actually do it: organise emails, summarise meetings, update tools, follow multi-step instructions without breaking. Executes workflows end-to-end. 2) Turn rough ideas into working apps You can sketch (even hand-draw) a UI or just describe a concept in plain English, and Gemini 3 Pro generates HTML/CSS/JS to build a working prototype. 3) Understand complex visuals Feed it a mix of lectures (video), hand-written notes, images — Gemini 3 can synthesise everything, create interactive flashcards or visualisations, and help you master complex topics. 4) Make sense of messy documents Beyond simple OCR: understands complex documents (contracts, blueprints, diagrams), reasons over their content, and extracts structured data or insights. 5) Plan and execute long projects Holds and executes multi-step plans (tens of steps) with fewer collapses or resets. For the tasks like writing emails or polishing text, you won’t feel a huge difference - older models already handled that well. The leap is in reasoning, multimodality, and agent-style execution. 𝗜𝘀 𝗶𝘁 𝗼𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲? No, not the model itself. But Google has released some open-source tools around it: - Through the Gemini app / Google AI Studio, there is a free tier (not Deep Think yet) - For developers, the Gemini API also has a free tier Will drop references in the post. 📍Also, there’s an awesome 2‑day AI live course is happening this weekend, hosted by Outskill. Upskilling‑oriented | 16 hours | free to join: 🔗https://lnkd.in/gBuXuqJ7 __________ For more on AI and learning materials, plz check my previous posts. I share my journey here. Join me and let's grow together. Alex Wang #artificialintelligence #technology #llms #aiagents #generativeai

The release of Google's Gemini Pro 1.5 is, IMO, the biggest piece of A.I. news yet this year. The LLM has a gigantic million-token context window, multimodal inputs (text, code, image, audio, video) and GPT-4-like capabilities despite being much smaller and faster. Key Features 1. Despite being a mid-size model (so much faster and cheaper), its capabilities rival the full-size models Gemini Ultra 1.0 and GPT-4, which are the two most capable LLMs available today. 2. At a million tokens, its context window demolishes Claude 2, the foundation LLM with the next longest context window (Claude 2's is only a fifth of the size at 200k). A million tokens corresponds to 700,000 words (seven lengthy novels) and Gemini Pro 1.5 accurately retrieves needles from this vast haystack 99% of the time! 3. Accepts text, code, images, audio (a million tokens corresponds to 11 hours of audio), and video (1MM tokens = an hour of video). Today's episode contains an example of Gemini Pro 1.5 answering my questions about a 54-minute-long video with astounding accuracy and grace. How did Google pull this off? • Gemini Pro 1.5 is a Mixture-of-Experts (MoE) architecture, routing your input to specialized submodels (e.g., one for math, one for code, etc.), depending on the broad topic of your input. This allows for focused processing and explains both the speed gains and high capability level despite being a mid-size model. • While OpenAI also uses the MoE approach in GPT-4, Google seems to have achieved greater efficiency with the approach. This edge may stem from Google's pioneering work on MoE (Google were the first to publish on MoE, way back in 2017) and their resultant deep in-house expertise on the topic. • Training-data quality is also a likely factor in Google's success. What's next? • Google has 10-million-token context-windows in testing. That order-of-magnitude jump would correspond to future Gemini releases being able to handle ~70 novels, 100 hours of audio or 10 hours of video. • If Gemini Pro 1.5 can achieve GPT-4-like capabilities, the Gemini Ultra 1.5 release I imagine is in the works may allow Google to leapfrog OpenAI and reclaim their crown as the world's undisputed A.I. champions (unless OpenAI gets GPT-5 out first)! Want access? • Gemini Pro 1.5 is available with a 128k context window through Google AI Studio and (for enterprise customers) through Google Cloud's Vertex AI. • There's a waitlist for access to the million-token version (I had access through the early-tester program). Check out today's episode (#762) for more detail on all of the above (including Gemini 1.5 Pro access/waitlist links). The Super Data Science Podcast is available on all major podcasting platforms and a video version is on YouTube. #superdatascience #machinelearning #ai #llms #geminipro #geminiultra

The global Multimodal AI market is projected to exceed $10 billion by 2030, reflecting the overwhelming industry demand for systems that can fluently process text, images, video, and audio together. Yet, most current Large Language Models (LLMs) still hit a wall when faced with deep, cross-application reasoning. 𝐒𝐭𝐚𝐭𝐢𝐜 𝐎𝐮𝐭𝐩𝐮𝐭 𝐚𝐧𝐝 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐁𝐥𝐢𝐧𝐝𝐧𝐞𝐬𝐬: For years, AI has excelled at single-turn tasks: generating a piece of text or answering a factual question. However, the modern professional's workflow is rarely a single step. We live across emails, documents, spreadsheets, and calendar invites. Asking a typical AI model to "Analyze the Q3 budget report (PDF) and schedule a follow-up meeting with the top three regional managers mentioned" often results in disjointed, static text that requires you to manually perform the next two or three critical steps. This constant need for context switching and manual orchestration kills productivity. 😩 The core limitation lies in agentic reasoning—the ability to not just plan a task but execute it across different tools and applications, making necessary real-time decisions along the way. Existing AI systems often fail at complex, multi-step tasks because they lack the deep, native understanding to connect disparate data points, such as correlating a flight reservation in an email with local rental car availability and a personal budget constraint. 𝐔𝐩𝐠𝐫𝐚𝐝𝐞𝐝 𝐑𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐈𝐧𝐭𝐞𝐫𝐟𝐚𝐜𝐞𝐬 Google’s latest major update, powered by the Gemini 3 model, is engineered to overcome these challenges through sharper reasoning and unparalleled multimodal understanding. Gemini 3 moves beyond static text generation by introducing Generative Interfaces—user experiences custom-designed by the model itself, in real-time. This includes: 👉 Visual Layout: Creating magazine-style, immersive views for complex outputs, making consumption easier. 👉 Dynamic View: Generating and coding a custom, interactive UI that lets users tap, scroll, and engage with the response (e.g., an interactive gallery guide) in ways static text never could. 🎨 𝐊𝐞𝐲 𝐁𝐞𝐧𝐞𝐟𝐢𝐭𝐬 𝐟𝐨𝐫 𝐏𝐫𝐨𝐟𝐞𝐬𝐬𝐢𝐨𝐧𝐚𝐥𝐬 & 𝐓𝐞𝐚𝐦𝐬: 👉 Unrivaled Productivity: Automate workflows that require connecting multiple tools and data sources. 👉 Multimodal Fluency: Seamlessly process complex inputs like photos of homework, transcribed lectures, or detailed reports alongside text prompts. 👉 Intuitive Interaction: Experience AI that adapts its output format to your request, moving beyond bullet points to interactive, visually rich layouts. Discover the full depth of the Gemini 3 model, the new Generative Interfaces, and the powerful Gemini Agent: https://lnkd.in/daWHiQFH #Gemini3 #AI #GenerativeAI #MultimodalAI #AIagent #Productivity #Innovation 

Excited to introduce our research and novel implementation of 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗨𝗜, coming to life today with the release of Gemini 3! Now rolling out in the Gemini app and in Google Search, starting with AI Mode. Generative UI is a new capability in which an AI model generates not only content but an entire user experience. ✨ New Design Paradigm: We introduce a novel implementation of generative UI which dynamically creates immersive, visual experiences and interactive interfaces—such as web pages, games, tools and applications—that are automatically designed and fully customized in response to any question, instruction, or prompt. 🛠️ How the generative UI implementation works  Our generative UI implementation, described in a paper we made public today, uses Google’s Gemini 3 Pro model with three important additions: tool access (e.g., image generation, web search), carefully crafted system instructions, and post-processing to manage potential common issues. 💡 Example scenarios: Generative UI is useful across a wide range of scenarios. From learning about fractals or any topic, to teaching mathematics, to getting tailored fashion advice - see more examples in the project page.  The user prompt can be as simple as a single word, or as long as needed for detailed instructions. 🚀 Our research on generative UI, comes to life today in the Gemini app through an experiment called dynamic view and in AI Mode in Search. Gemini App: In dynamic view, Gemini designs and codes a fully customized interactive response for each prompt, using Gemini’s agentic coding capabilities. It understands that explaining a complex topic like the microbiome to a child requires a different interface than explaining it to an adult. Google Search: We are integrating generative UI capabilities starting with AI Mode (for Pro and Ultra subscribers in the U.S.) unlocking dynamic visual experiences with interactive tools and simulations that are generated specifically for a user’s question. 📈 The magic cycle of generative UI research: Excited to see our foundational research on generative UI coming to life in product innovation in Search AI Mode and Gemini dynamic view.  We are still in the early days of generative UI and important opportunities for improvement remain, like improving efficiency and accuracy, extending generative UI to access a wider set of services, adapt to additional context and human feedback, and deliver increasingly more helpful visual and interactive interfaces. Read more about the research in our blog: https://goo.gle/47FdGcR  Read the new paper: https://lnkd.in/d-PxE5nG See the project page with more examples: https://lnkd.in/dzWejH2B

🔥 Inside Gemini’s AI Image Tool — Nano Banana How Multimodal Intelligence Creates Visual Precision AI image generation has evolved far beyond making “beautiful pictures.” Today, the most advanced systems understand context — across text, images, video, and even sensor data — to produce photorealistic, intention-aligned results. Gemini’s Nano Banana is a perfect example of that leap. Here’s how its end-to-end multimodal image generation pipeline works 👇 🧩 1. Input Stage Accepts multimodal inputs — text, images, video, and even real-time contextual sensor signals. 📝 2. Text Processing Multilingual datasets transform raw text into dynamic embeddings rich with nuance and context. 🖼️ 3. Image Pre-Processing Extracts lighting, materials, 3D structure, and composition to build layered feature maps. 🔗 4. Multimodal Alignment Aligns text and visual signals, learning cross-modal relationships with high efficiency. 🧠 5. Concept Understanding Builds a semantic plan and adapts to historical user preferences for personalized generation. 🌫️ 6. Noise Initialization Creates structured noise from learned distributions — forming early shapes, edges, and colors. 🔄 7. Guided Transformation Removes noise in stages, guided by real-world transformation datasets that anchor realism. 🎯 8. Attention Mechanism Focuses computation on the most relevant tokens and visual features for fine-grained accuracy. 🪄 9. Iterative Refinement Adds texture, depth, shadows, and environmental cues that mimic real-world physics. ✨ 10. Final Polishing Enhances reflections, sharpness, and micro-details using calibrated visual data. 🔐 11. Safety & Consistency Check Evaluates harmful content, style mismatches, and semantic coherence. 📤 12. Output Delivery Applies secure AI watermarks and exports multiple high-resolution formats. 🌟 Why this matters Each layer in the Nano Banana workflow represents a leap toward trustworthy, multimodal creativity — a world where AI doesn’t just render images, but truly understands them. This deep alignment between text, vision, and user intent is redefining how creators, engineers, and designers interact with AI. How close are we to achieving human-level intuition in visual AI systems? Would it change how we think about creativity, authorship, and imagination? #AI #GeminiAI #ImageGeneration #MultimodalAI #GenAI #ArtificialIntelligence #VisualComputing #Innovation #AIDesign

BREAKING: Google Gemini 3 might be the most powerful AI model yet! Top 5 upgrades you should know. 👇 Upgrade #1 – State-of-the-art reasoning & benchmark domination Gemini 3 Pro smashes major AI benchmarks: Tops the LMArena leaderboard with an Elo score of 1501. PhD-level reasoning: e.g., 37.5% on “Humanity’s Last Exam” without tool use; 91.9% on GPQA Diamond. Multimodal understanding: 81% on MMMU-Pro, 87.6% on Video-MMMU. Upgrade #2 – Multimodal superpowers + mega context window. Gemini 3 isn’t just text-smart. It reads images, video, audio, code. It now supports a 1 million-token context window. Example use-cases: deciphering handwritten recipes in different languages, analyzing your pickleball match video and generating training plans. If you’ve got weird formats or long-form data, this opens big new doors. Upgrade #3 – Build anything: agentic dev & vibe-coding For developers & creators: Gemini 3 is described as “the best vibe coding and agentic coding model we’ve ever built.” It achieved top marks on WebDev Arena (1487 Elo) and Terminal-Bench 2.0 (tool use via terminal) plus SWE-bench Verified. Launching with new platform: Google Antigravity — an agentic dev environment where the AI assists the editor, terminal, browser. Upgrade #4 – Plan anything: long-horizon workflows Gemini 3 isn’t just reactive — it can plan and execute complex, multi-step workflows. Example: It topped the “Vending-Bench 2” which tests managing a simulated vending machine business over a year. Google frames it as “it can take action on your behalf by navigating more complex, multi-step workflows … while under your control and guidance.” So if you’ve got big workflows (marketing campaigns, product launches, business ops) this is a serious leap. Upgrade #5 – Safety, security & responsible rollout Every power boost comes with risk. Google says Gemini 3 “is our most secure model yet,” with enhanced resistance to prompt injection, sycophancy (yes, AI flattery), and misuse. Also, roll-out is phased: Gemini 3 is available now in the Gemini app, Search (AI mode), AI Studio, Vertex AI; the “Deep Think” mode (even more capable) is coming later after extra safety review.

🚀 Google DeepMind just dropped Gemini 3, and it feels like we’re in a new era! I don’t say this lightly: what Google released today is the biggest leap forward in the Gemini lineage since the original “native multimodality” moment. Gemini 3 isn’t just a bigger model. It’s a different species of model. Here are the 6 things that blew my mind 👇 1. The model can finally “read the room”, not just the prompt Sundar Pichai, Demis Hassabis, and koray kavukcuoglu said it clearly: Gemini 3 understands intent, not just text. It scores: 1501 Elo on LMArena (new #1 after xAI's Grok 4.1 leading yesterday) This is the first Google model that feels like a thought partner, not just an autocomplete engine. 2. Deep Think mode is… wild Gemini 3 Deep Think is essentially “AGI mode on training wheels.” This is Google admitting: ➡️ We now have frontier-grade reasoning that must go through safety review before exposure. That alone is a signal. 3. Search with Gemini 3 is the biggest upgrade since PageRank For the first time ever, a Gemini model ships in Search on day one. AI Mode now gives: ✅ Dynamic visual layouts ✅ Interactive tools & simulations generated in real time ✅ A massively upgraded query fan-out engine ✅ Automatic routing to Gemini 3 for harder queries The “three-body problem → auto-generated physics simulator” example is the future of learning. Not search results, search experiences. 4. Google Antigravity might redefine how software is built This deserves its own post. Antigravity is a new agentic development platform where: ✅ Agents have direct access to editor, terminal, and browser ✅ They can plan + execute full features end-to-end ✅ Multiple agents run in parallel ✅ The developer becomes the architect, not the typist 5. Multimodality is no longer a “feature”, it’s the foundation Gemini 3 can: ✅ Parse handwritten recipes → generate a family cookbook ✅ Analyze your pickleball game from video → build a training plan ✅ Turn a single image into an interactive web app ✅ Understand OS screens, cursor movements, gestures, and intent ✅ Translate academic papers + hour-long lectures → interactive flashcards, visualizations, or full learning paths This isn’t multimodal “input.” This is multimodal thinking. 6. Developers just got a completely new toolbox Gemini 3 is now available with client-side + server-side bash tools, new “thinking level” (and thought-signature validation), and configurable multimodal fidelity (finally!) The bigger picture: Gemini 1 gave us multimodality. Gemini 2 unlocked agents. Gemini 3 combines everything into coherent intelligence. AI isn’t just answering questions anymore. It’s learning what you mean, building what you imagine, and planning what you’d do next. (official release posts are linked in comments). This is the closest Google has ever been to saying the quiet part out loud: ➡️ We’re on the AGI path, and it’s accelerating.