An analysis of Gemini 4 Hydrogen (High) compares its intelligence, performance and price, using capability indexes that measure model performance on specific capabilities and industries. The comparison covers token use, cost and context window, alongside performance metrics including time per task in minutes, output speed in tokens per second, and time to first answer token and first token in seconds.
Google released Gemini 4 Hydrogen, a closed-weights model from Google DeepMind positioned below Gemini 4 Carbon and Argon as a daily driver for coding and agentic workflows. It offers a 1,048,576-token unified window, four thinking levels with high as the default, and API pricing of $1.50/$7.50 per 1M input/output tokens, flat across the full window. Google reports 57% on Terminal-Bench 4.1 versus 18.2% for Gemini 3.8 Flash and 51 on the Artificial Analysis Intelligence Index, but the model ranks only #5 on Agent Arena; pairing it with Carbon in Antigravity used 24% less quota and finished 19% faster than Carbon alone.
Same event; the pick carries“Google DeepMind releases Gemini 4 Hydrogen, a faster Gemini 4 model for coding and agentic workflows”
Google released Gemini 4 Hydrogen, the lowest-cost model in the Gemini 4 family, priced at $1.50 per million input tokens, $0.075 for cached input and $7.50 per million output tokens. Simon Willison notes that Carbon and Argon both cost $2.00/$0.10/$10.00 until Nov 30, 2026 and double from Dec 1, 2026, so Hydrogen is currently only about 25% cheaper than its larger siblings, a gap that widens to roughly 75% after the increase. Hydrogen is also the new free tier model and will power AI features in Google Search.
Same event; the pick carries“Google DeepMind releases Gemini 4 Hydrogen, a faster Gemini 4 model for coding and agentic workflows”
Google introduced Gemini 4 Hydrogen, a model built for rapid execution in everyday coding and agentic workflows, rolling out globally across the Gemini API, AI Studio, Gemini for business, the Gemini app, Google Search, and Google Antigravity.
Same event; the pick carries“Google DeepMind releases Gemini 4 Hydrogen, a faster Gemini 4 model for coding and agentic workflows”
An analysis of Gemini 4 Hydrogen (Xhigh) covers its intelligence and capability indexes across specific capabilities and industries, along with token use cost and context window. Performance is measured by time per task in minutes, output speed in tokens per second, and time to first answer token and first token in seconds.
Google released Gemini 4 Hydrogen, a new model it says gives more helpful answers to complex questions, including questions with multiple parts, hard-to-understand typos, or highly specific requests. It is available starting today in Search, the Gemini app, all tiers of Google AI Studio, the Gemini API, Gemini for business, and Google Antigravity for AI Pro and Ultra subscribers, and Google Search VP Robby Stein says it is now the default model in AI Mode and is used for the hardest questions in AI Overviews.
Same event; the pick carries“Google DeepMind releases Gemini 4 Hydrogen, a faster Gemini 4 model for coding and agentic workflows”
Google has released Gemini 4 Hydrogen, a fast general-purpose model for developers and everyday users, priced at $1.50 per million input tokens and $7.50 per million output tokens with a one-million-token context window. Google says Hydrogen scores 51 on the Artificial Analysis Intelligence Index at its default high thinking level and 54 at the new xhigh level, versus 50 for the larger Gemini 4 Argon. Independent testing by Artificial Analysis puts Hydrogen roughly on par with Claude Sonnet 5.5 at maximum thinking, but weaker on agentic terminal work, scoring 54 percent on Terminal-Bench 4.0 against 64 percent for Claude Sonnet 5.5.
Same event; the pick carries“Google DeepMind releases Gemini 4 Hydrogen, a faster Gemini 4 model for coding and agentic workflows”
Google has released Gemini 4 Hydrogen, which replaces Gemini 3.8 Flash across the company's products with immediate access rather than the staged, paid-tier-only rollout used for its recent Argon and Carbon releases. Google says Hydrogen runs at one of four thinking levels (low, medium, high, and a new xhigh) and is 2.5 times faster than Flash 3.8 even in high mode, while cutting fabricated claims by more than 5x. It also more than triples 3.8 Flash's Terminal-Bench score and reaches 54 on the Artificial Analysis Intelligence Index in xhigh mode, versus 50 for Argon.
Same event; the pick carries“Google DeepMind releases Gemini 4 Hydrogen, a faster Gemini 4 model for coding and agentic workflows”
Google released Gemini 4 Hydrogen, a fast-execution model it calls its biggest Search improvement yet, and is rolling the update out to all Search users globally. Google says Hydrogen completes tasks 2.5 times faster than Gemini 3.8 Flash and beats the more powerful Gemini 4 Argon on the Artificial Analysis Intelligence Index at its default high thinking level. In Search it improves intent understanding, typo handling, fan-out subqueries and visual responses in AI Mode, and it is also coming to AI Studio, the Gemini API, the Gemini App and Antigravity.
Same event; the pick carries“Google DeepMind releases Gemini 4 Hydrogen, a faster Gemini 4 model for coding and agentic workflows”
Google DeepMind introduced Gemini 4 Hydrogen, a Gemini 4 model built for fast everyday coding and agentic execution rather than the long-horizon reasoning of Gemini 4 Carbon and Argon. It scores 51 on the Artificial Analysis Intelligence Index at its default high thinking level versus 50 for Argon, reaches 54 at a new xhigh setting, and is priced flat at $1.50 per million input tokens and $7.50 per million output tokens across its 1 million-token context window. Google also launched Orchestrator in Google Antigravity, which switches between models like Carbon and Hydrogen within a session and reached near-Carbon task success on Terminal-Bench 4.0 while using 24% less quota and finishing 19% faster.
Why it matters: Google's Gemini 4 Hydrogen targets fast everyday coding and agentic work, with flat $1.50/$7.50 per million token pricing across a 1M context window and a new Orchestrator that switches between Gemini 4 models.
Google DeepMind has released AlphaGenome 2, a genomics model that predicts how DNA variants affect specific human cell types and finds nearly twice as many relevant gene changes as its predecessor. Trained on more than 107 million individual cell profiles from 68 tissues in humans and mice, it takes a cell's biological state as an extra input instead of predicting broadly across many tissues at once.
Google has released Gemini Decision 1, a decision model API that supports yes/no, choice and rubric-scoring questions, joining Jev and OpenAI in the decision model category. It is priced at 10 cents per million input tokens with free output, and unlike Jev's text-only input it accepts text, images, audio, video and PDFs. In a test on a robot video, Gemini returned probabilities such as 0.9234 for robot and 0.0498 for dragon instead of the 1.0 or 0.0 that Jev and OpenAI return.
Google DeepMind released AlphaGenome 2, a conditional sequence-to-function DNA model that reads a 1 Mb DNA window plus a target cell state and predicts variant effects for one cell type per call. On TenK10K single-cell eQTLs across 28 immune cell types, causal variant recall at 90% precision rose from 16% for fine-tuned AlphaGenome 1 to 30%, and direction-of-effect recall rose from 23% to 43%. The model is available through the AlphaGenome API for non-commercial research, with weights promised upon final publication and commercial use in private preview.
Google DeepMind introduced the Gemini Decisions API, built for closed-ended decisions such as yes/no questions, multiple choice, and scale-based ratings, with the underlying Gemini Decision 1 model processing up to 1,000 independent questions in a single pass across text, images, audio, video, and PDFs. It returns probabilities with confidence values in 150 to 250 milliseconds and costs $0.10 per million input tokens with no output-token charge, which Google says is about five times cheaper than Gemini 3.5 Flash-Lite. The API is available now through Google AI Studio, with integrations for OpenRouter, LangChain, and Vercel rolling out, and Google says it already uses it internally for model routing in Gemini Notebook, design prototyping in Stitch, and safety checks in Google Search.
Same event; the pick carries“Google launches Gemini Decisions API and Gemini Decision 1 model for fast closed-set decisions”
Google DeepMind announced AlphaGenome 2, a next-generation DNA sequence model that predicts how DNA sequences and their variants affect specific human cell types. Trained on more than 107 million individual cell profiles across 68 tissues, the conditional model identified nearly twice as many expression-changing variants as the previous model, and on the TenK10K immune cell dataset it recovered 30% of causal variants versus 16% for the original AlphaGenome fine-tuned on single-cell data. It is available for non-commercial research via the AlphaGenome API and in private preview for commercial research.
Why it matters: AlphaGenome 2 shifts from bulk-tissue averages to single-cell conditioning, roughly doubling causal variant recall on immune cell data, which matters for linking GWAS signals to specific cell types.
Google DeepMind has unveiled AlphaGenome 2, a successor to last year's AlphaGenome that predicts how genetic variants affect individual cell types and states rather than broad tissue averages. Trained on more than 100 million individual cell profiles across 68 tissues, the model identifies nearly twice as many variants that can change gene expression as the previous state-of-the-art model, and can make predictions about new cell types and biological states without retraining. Google is offering it for non-commercial research via the AlphaGenome API from today, with a limited private preview for commercial use; DeepMind's Žiga Avsec said its finer predictions are more computationally expensive, so it will not replace AlphaGenome soon.
Gemini 4 Carbon, announced two weeks ago for a subset of cyber defenders, is now available to everyone at $2/million input and $10/million output, with prices scheduled to double to $4/$20 on December 1st. Simon Willison tested it in Google's Antigravity IDE, generating a Three.js animation of a mech stomping through New York City using OpenStreetMap data, and noted that Google CEO Sundar Pichai highlighted Antigravity's /swarm feature building an open-world 3D game with Blender assets, WebGL shaders and Lyria music.
Same event; the pick carries“Google DeepMind releases Gemini 4 Carbon for agentic coding, cybersecurity and scientific reasoning”
Google DeepMind made Gemini 4 Carbon available to developers, enterprises, scientists and consumers, positioning it above Gemini 4 Argon for tasks that need deeper reasoning and more tokens. Carbon ranks #1 on CursorBench 4.0, Perplexity's WANDR and Zapier's AutomationBench at more than 2X lower cost per task than the next best external model, and is priced at $2 per million input tokens and $10 per million output tokens, the same introductory price as Argon. Google also previewed /swarm in Google Antigravity, which turns a single prompt into a coordinated team of agents, and reported that Carbon blocked 99% of attack trajectories in simulated cyberattack testing.
Why it matters: Google's release notes detail how Gemini 4 Carbon trades higher token usage for deeper reasoning, ranking first on CursorBench 4.0, WANDR and AutomationBench at under half the per-task cost of the next external model.
Google DeepMind revealed Gemini Robotics 2.5, a new version of its vision-language-action model that generates robot actions, saying it is much better at tasks requiring high dexterity. The model was evaluated across more than 200 activities, including serving food with tongs and using a pipette, and Apptronik's Apollo 2 humanoid robot running it can now tie a bow with its five-fingered hands. DeepMind also introduced an open source safety benchmark, ASIMOV-jailbreak, as it prepares for robots to operate alongside humans, and said some Gemini robots did actual work in Mercedes-Benz factories.
Google DeepMind introduced Gemini Robotics 2.5, its most capable vision-language-action model to date, which expands the range of skills a robot can learn and speeds up how fast it acquires them. Evaluated across 200 distinct tasks, the model achieves a 30% to 100% higher success rate than previous baselines while completing tasks 20% to 30% faster, and in a Mercedes-Benz production-line proof-of-concept, Apptronik's Apollo 2 humanoid picked and packed automotive parts with a 96% success rate. The release also adds inference-time reasoning guidance for physical safety, which raised safe task completion by 33% around obstacles and 43% around nearby people, plus ASIMOV-Jailbreak, an open-source benchmark on which Gemini 4 Argon reaches 99.5% jailbreak resistance.
Why it matters: Gemini Robotics 2.5 improved its success rate on 200 tasks by 30% to 100%, and achieved a 96% pick-and-place success rate in a pilot on the Mercedes-Benz production line, providing reference data for evaluating the real-world deployment of VLA models.
Every's hands-on review finds Gemini 4 Carbon, released today by Google DeepMind, is a much stronger coder than Gemini 4 Argon and can build better 3D objects and work for hours with little supervision. Testers disagree on how much supervision it needs, and the review says Anthropic's Fable 5.2, released a day earlier, has raised expectations that a flagship agent also pick the right work, respect limits and know when it is finished. Google gave Every early access and had no input on the review.
Google DeepMind rolled out Gemini Omni 1.2 Flash, now generally available across the Gemini API and Gemini for business, with native 15-second video generation, multi-turn conversational editing and stronger reference following. The price drops 30% from Gemini Omni 1.1 to $0.07 per second for 720p video, and users can draft at 360p or 720p before upscaling to 1080p or 4K. Clips can be set from 3 to 15 seconds in one-second increments, and outputs carry an imperceptible SynthID watermark.
Why it matters: Gemini Omni 1.2 Flash raises native clip length to 15 seconds and cuts the price 30% to $0.07 per second of 720p video, with multi-turn conversational editing now generally available.
Google DeepMind rolled out Gemini Omni 1.2 Flash and made it generally available across the Gemini API and Gemini for business. The model generates and edits native clips up to 15 seconds, up from 10 seconds in Omni 1.1, supports multi-turn conversational editing, and holds character, product, and style references more consistently; 720p video now costs $0.07 per second, 30% less than Omni 1.1. Outputs carry a SynthID watermark, and the model is rolling out in Flow, Google Vids, the Gemini app, and Google Ads.
Why it matters: Gemini Omni 1.2 Flash cuts the price 30% to $0.07 per second for 720p video while extending native clips to 15 seconds, giving teams producing video at scale a cheaper option to compare against prior versions.
Google DeepMind announced Gemini 4 Carbon, its strongest model yet, with state-of-the-art cybersecurity and long-horizon capabilities, ranking #1 on the v5.0 Artificial Analysis Intelligence Index at 59. It scores 88.2% on SEC-bench Pro for finding vulnerabilities, 54.8% on ExploitGym for exploitation and 71.0% on CWE-bench for patching, and reaches #4 (1768) on WebDev Arena. Carbon launches at the same introductory price as Argon, $2 per million input tokens and $10 per million output tokens, and is first available to a subset of cyber defenders through the Fairwind Program before wider release.
Why it matters: Gemini 4 Carbon's benchmark results in vulnerability discovery and patching show how frontier models are being positioned for cybersecurity defense work.
Google's Gemini 4 Carbon is the new No. 5 on the Artificial Analysis Coding Agent Index. 5 (max, 62), and it leads 4 of the 9 Index evaluations including HLE-Diamond and Terminal-Bench-Science. 1 rises from 4% to 11%; the model is being rolled out to selected users and is not yet publicly available.
Why it matters: Gemini 4 Carbon tops Artificial Analysis Intelligence Index v5 at 59 and trails Claude Opus 5.5 by 3 points on the Coding Agent Index, with per-task cost and hallucination figures for comparison.
Google DeepMind released Gemini 4 Argon today, marking Google's first major flagship model upgrade since Gemini 3 Pro last November and a minor 3.1 Pro bump in February. The release follows a summer in which Google shipped four rapid updates to its faster, lower-cost Flash line while the promised Gemini 3.5 Pro stayed in testing, and Every frames Argon as putting Google back in the frontier conversation.
Why it matters: Google DeepMind releases Gemini 4 Argon, its first flagship upgrade since Gemini 3 Pro, returning Google to the frontier model conversation.