Introducing Gemini 4 Hydrogen

Hydrogen brings cutting-edge intelligence to everyone.

Today, we’re introducing Gemini 4 Hydrogen, bringing cutting-edge intelligence to everyone — with speed and efficiency for everyday coding and agentic workflows.

While our frontier models, Gemini 4 Carbon and Gemini 4 Argon, excel at deep, long-horizon reasoning on your hardest problems, Hydrogen is built for rapid execution across diverse agentic workflows. With Hydrogen, we’re also bringing Gemini 4 to everyone — for all tiers of Google AI Studio, the Gemini API, Gemini for business, the Gemini app, Google Search, and paid Google AI Pro and Ultra subscribers in Google Antigravity.

Hydrogen surpasses Argon on the Artificial Analysis Intelligence Index at its default high thinking level (51 vs. 50, and reaching 54 at its highest xhigh thinking setting), while often completing tasks faster than models in its class. Gemini 4 Hydrogen is priced flat across its entire 1 million-token context window at $1.50 per million input tokens and $7.50 per million output tokens.

Unlocking efficiencies in multi-model workloads

In real-world software development, while teams rely on frontier models like Carbon and Argon for deep architectural judgment when designing systems or untangling subtle bugs, they still need a fast, disciplined execution model for writing code, running tests, and verifying changes. Hydrogen is built to stand on its own as a model for everyday work — or to pair with Carbon and Argon as the high-efficiency execution engine in multi-model workflows.

To help make this multi-model collaboration automatic, we’re introducing Orchestrator in Google Antigravity. Rather than using a single model from start to finish, Orchestrator dynamically switches between models like Carbon and Hydrogen within the same session. On Terminal-Bench 4.0, Orchestrator reached near-Carbon task success rates while, on average, consuming 24% less quota and completing 19% faster. For workflows that require parallel agents, Carbon can act as the coordinator while Hydrogen can power worker and verifier agents to build and test changes simultaneously across isolated workspaces — helping teams ship complex projects faster without driving up token costs.

Scaling developer and enterprise workflows

Benchmark chart comparing Gemini 4 Hydrogen to other models

Beyond multi-model workflows, Hydrogen is a major standalone upgrade for teams migrating from Gemini 3.8 Flash. It delivers significant leaps in both speed and intelligence — finishing tasks 2.5 times faster at its high default setting while more than tripling Terminal-Bench 4.1 scores (57% vs. 18.2%), cutting fabricated claims by more than 5x, and adapting cleanly when a tool call fails or a search returns empty. It also matches or outperforms competing models in its class across core developer and enterprise workflows:

Iterative repository coding and debugging: Hydrogen handles issue resolution, debugging, rapid iteration, and codebase Q&A with focused, surgical edits — delivering cleaner, production-ready code without rewriting unrelated files. It outperforms similar-tier models on SWE-sweep where it has to discover and repair bugs without breaking existing code. To explore large repositories efficiently, the model excels at natively batching file-read and search calls in parallel rather than inspecting files one by one; in early testing across complex graphs, SQL, and data-heavy enterprise workflows, our partners have seen Hydrogen run 2x to 2.8x faster while reducing token usage by 12% to 23%, compared to 3.8 Flash.
Open-ended agentic execution: On multi-step tasks like deep research, data analysis, and broad codebase navigation, Hydrogen ranks #5 on Agent Arena, arena.ai’s live benchmark. For deep domain reasoning and analysis, it leads its class on HLE-Diamond (58.8%), ranks #1 overall on Vals Tax Agent Bench (52.3%), ties for #1 on the Vals Legal Research Bench (55.8%), and leads its class on AutomationBench-AA.

The new Gemini 4 Hydrogen model is thorough and skeptical in a way I haven’t seen from past Gemini models. It is really pleasant and surprising! We have been working on a thorny problem for 10 days with frontier models from competitors and the Hydrogen model was able to catch 2 issues with our approach and proposed a fix. Really amazing and efficient.

By Andrew CarrChief Scientist and Co-Founder, Cartwheel

Building with Hydrogen made it very easy to build new tools — it combines the performance of a frontier model with speed that makes iteration on tools feel incredibly lightweight.

By Tris WarkentinFounding Member of Technical Staff, Discovery Loop

Gemini 4 Hydrogen was the most accurate model on the toxicology and safety section of our Antibody Development benchmark, passing 67% of attempts against 37% for the best non-Gemini model, and the fastest Gemini model at about 13 minutes per task. It reasoned like a cautious safety reviewer, weighing the whole clinical picture rather than single data points and choosing conservative assumptions where patient risk was high.

By Joshua Badshah and Dillon FloodBiologics Development, LatchBio

Across our reasoning and creative evaluation suite, Gemini 4 Hydrogen matched accuracy with 100% format compliance while slashing end-to-end latency by 28% and cutting total token usage by 23%. From solving complex graph and SQL problems up to 2.8× faster to generating rule-compliant voice ad scripts for StanShop, Hydrogen gives our streaming agents the speed, step-by-step verification, and efficiency we need at scale.

By Rahul SinghCo-Founder & CTO, STAN

Multimodal knowledge work: Hydrogen pairs native 1 million-token input with up to 1 million output tokens to synthesize dense PDFs, spreadsheets, charts, audio, and recorded video into polished, cited reports and structured deliverables. It outperforms other models on the new Multimodal Knowledge Agent (MKA) benchmark (53.4%) for end-to-end agentic knowledge work over complex visual files, and on Agents’ Last Exam v1.1 (71.6%) for end-to-end professional workflows.
Web development: On tasks where speed matters to developers, like live UI prototyping, design-to-code iteration, and interactive app building, Hydrogen generates more functional, feature-complete layouts in fewer prompts.

Our goal, at Astrocade, is to let anyone’s creativity grow into games that people enjoy playing and sharing. Serving our creators the best frontier models is central to that experience. In early testing, Gemini 4 Hydrogen blew past our expectations in game generation performance. We’re optimistic that Gemini 4 Hydrogen will be key in helping creators take their games to the next level.

By Ali SadeghianCo-Founder, Astrocade

In Box’s evaluations, Gemini 4 Hydrogen’s accuracy significantly improved when compared to Gemini 3.8 Flash, while running about twice as fast and using 12% fewer tokens. We saw the strongest gains in data-heavy workflows, where accurately reading tables and performing complex calculations is essential to producing reliable reports. These results demonstrate meaningful progress in the model’s ability to handle complex enterprise content with greater accuracy and efficiency.

By Yashodha BhavnaniVP of AI Products, Box

We evaluated Gemini 4 Hydrogen over 276 runs. It swept every single design brief, ran 1.8× faster with 30% cleaner code, and eliminated the text clipping and hallucinated claims we saw in previous models.

By Dominique YahyaviCo-Founder & CEO, Pixelcut

We tested Gemini 4 Hydrogen across demanding video-comprehension workflows — from tracking characters and physical action to breaking down complex cinematography and directing downstream video models. Hydrogen consistently nailed major scene progressions, camera angles, and visual aesthetics, while anchoring on real visual evidence rather than hallucinating details. It’s proving to be an exceptionally capable multimodal foundation for end-to-end cinematic generation.

By Harshit YadavFounder & CEO, Zingroll