网页 综合 更新时间 2026-11-01 00:00:00

Gemini — Google DeepMind

自动蜘蛛 deepmind.google 正文约 6000 字
Gemini — Google DeepMind
Our next era of frontier intelligence, built to solve your hardest problems.
Frontier performance in complex workflows
Methodology: deepmind.google/models/evals-methodology/gemini-4-hydrogen
Methodology: deepmind.google/models/evals-methodology/gemini-4-carbon
“On over 200 complex synthetic longitudinal charts, Gemini 4 achieved the highest recall and ICD-10 code specificity among the models we evaluated, producing concise problem lists without external lookups. That combination of medical knowledge and long-context understanding shows promise for clinician-reviewed chart workflows. We're now validating it on real patient records and excited to try it out more broadly.”
SVP Technology, Ambience Healthcare
“Argon is doing a great job at understanding what people actually mean. Customers collect endless signals that they don't know what to do with. Argon consistently scored amongst the top in our evals for building charts that help visualize the solutions for customers. It's also meaningfully persistent about finding those kinds of needle in a haystack answers that are valuable to customers.”
“Gemini 4 represents a step change in what AI can accomplish in real-world science. We've seen it reason through complex experimental data, from cellular imaging to mass spectrometry, and make subtle findings that traditionally require expert scientists to identify. On our Scientific Agent Benchmark, Gemini 4 was at the frontier across most tasks, including our hardest: ranking the clinical risk between two drugs.”
Founding Engineer, Axiom Bio
“Gemini 4 Argon is at the frontier of enterprise knowledge work, achieving a 14% improvement in accuracy compared to Gemini 3.1 Pro. In Box’s evaluation, Argon connected evidence across the full set of documents, including details embedded in figures, and carried multi-step analyses through to complete answers. This is especially valuable in data analysis and in technical fields like healthcare and life sciences, where teams need to find and interpret information across many kinds of source material.”
VP of AI Products at Box
“By integrating Gemini 4 into our 3D spatial reconstruction engine, we turn everyday video into articulated, physics-ready 3D scenes in seconds instead of hours. Across our benchmarks, Gemini outperformed every model we tested — cutting runtime nearly 100× and token usage by 90%, unlocking physics ready and watertight 3D asset creation for our customers.”
Co-founder and Chief Scientist, Cartwheel
“Early impressions from Gemini 4 are very strong: for long-running tasks across these workflows, it completed successfully 100% of the time in our evals, maintaining context and running through multiple tools across impressive spans. Its depth of reasoning delivers significantly higher, clearer output. Performing in-depth reasoning for domain-specific tasks without detailed prior logic or instructions is something we’ve never seen with any model in its class before.”
Global AI and Data Lead, Deloitte
“On our core financial analysis benchmarks, which span building multi-year financial statements straight from SEC filings to valuing companies with complex capital structures and announced acquisitions, Gemini 4 matched our analyst-built reference models on more than 99% of figures. Performance on our most complex workflows was at the frontier in a majority of tasks. We're excited to keep building with Gemini 4 at Kepler.”
Software Engineer, Kepler
“To help legal professionals achieve more with Lexis+ with Protégé, we continuously evaluate AI models as part of the multi-model approach behind the LexisNexis Legal Intelligence Engine. In preliminary evaluations, Gemini 4 Argon demonstrated strong performance across legal knowledge, drafting, and analysis tasks, including identifying relevant citations and improving contract reviews, complaint analyses, and research memos.”
Chief AI Officer, LexisNexis Legal & Professional
“In early testing, Gemini 4 Argon stood out by connecting dates, documents, and financial calculations with legal precedents to develop highly substantive arguments. In one evaluation, it actually surpassed our human expert's answer key by surfacing a legally relevant detail we had missed. This demonstrates AI’s potential to help professionals uncover crucial details and bring a more complete understanding to complex cases.”
“In Qualia, our autonomous AI researcher, Gemini 4 Argon has been a standout in our evaluations for both capability and efficiency. On our internal benchmarks of complex, multi-step research tasks, it performs at the frontier while completing our evals 1.5x faster than peer models at a quarter of the cost per task. It excelled at coming up with novel ideas and identifying non-obvious insights about the problem, helping it maintain a clear research vision over long horizons, something today's models struggle with.”
Chief Scientist, Quadrillion
“Gemini 4 impressed us with the quality of its responses in our early evaluations. On Poe, people are increasingly using AI to build things, and Gemini 4 gives creators a powerful foundation for turning an idea into a tool they can refine and share.”
Head of Product, Quora
“Argon ranked first in our object detection evaluations and led our new text benchmark, which uses real-world images from manufacturing, logistics, and aerial imagery. We’ve fully integrated Argon into Roboflow so developers and enterprises can leverage its high precision detection and text extraction to inspect products, track inventory, and guide machines.”
Co-founder and CTO of Roboflow
Strengthening frontier safeguards before broad availability
Argon is designed to refuse harmful requests while preserving legitimate, dual-use scientific research, as per our Frontier Safety Framework.
Argon is our most resilient model yet against indirect prompt injections.
We are deploying misalignment mitigations that monitor Argon’s chain-of-thought and actions and stop execution when necessary.
We are hardening our san
访问原文 返回搜索 https://deepmind.google/models/gemini/
本页为82搜索生成的网页快照,内容来自站内索引;演示数据的原文链接仅作示例。