希望整合这些新模型的工程团队可以通过 Google AI Studio、Android Studio 或 Gemini 企业代理平台访问 Gemini API 来使用它们。消费者也可以在 Gemini 应用中访问这些新模型，同时 3.5 Flash-Lite 也正在 Google 搜索中推出。
另请参见：Bristol Myers Squibb 购买 Nvidia AI 系统用于药物发现：https://www.artificialintelligence-news.com/news/bristol-myers-squibb-nvidia-ai-system-drug-discovery/
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Google：https://blog.google/ has released Gemini 3.6 Flash and 3.5 Flash-Lite as new workhorses designed to cut latency and token costs for enterprise AI agents.
The economics of running autonomous software agents inside a production environment come down to a fixed equation few vendors advertise directly. A model needs to reason through a multi-step task competently, but every extra token it generates while doing so adds cost and delay to a workflow that might run thousands of times an hour.
Teams building background agents rather than chat interfaces need throughput first and parameter count second. Google’s answer, announced this week, splits that trade-off across three models: Gemini 3.6 Flash for coding and multimodal reasoning, Gemini 3.5 Flash-Lite for high-volume, low-latency work, and a restricted Gemini 3.5 Flash Cyber variant built for vulnerability remediation.
Google’s developer documentation for 3.6 Flash centres on one figure: 17 percent fewer output tokens than the prior 3.5 Flash version, based on measurements from the Artificial Analysis Index.
In specific synthetic tests, including the Datacurve DeepSWE benchmark, Google reports drops in token usage of up to 65 percent. Pricing sits at $1.50/1M input tokens and $7.50/1M output tokens, positioning the model for reasoning loops that run continuously rather than on-demand.
On DeepSWE, the company records a 49 percent success rate for 3.6 Flash against 37 percent for its predecessor. On MLE Bench, the score moves from 49.7 percent to 63.9 percent, and on Google’s GDPval-AA v2 test – which attempts to measure real-world knowledge work rather than coding puzzles – 3.6 Flash scores 1421 against 1349 for the older model.
Figma has integrated 3.6 Flash into its prototyping infrastructure, and according to Matt Colyer, the company’s Director of Product Engineering, the model gives developers a faster route through design iterations without a drop in output quality.
Legal technology platform Harvey and research tool Hebbia route data through the model for multimodal document work: ingesting raw financial filings, parsing document structure, reading embedded charts, and producing draft reports for review.
Google also folded a client-side computer-use tool directly into the Gemini API and Gemini Enterprise platforms, removing the custom intermediary software engineers previously built to let models operate on top of an operating system.
The company reports an OSWorld-Verified score of 83.0 percent, up from 78.4 percent, and says updated safeguards against chemical, biological, radiological, and nuclear misuse improve resistance to jailbreaking without raising refusal rates for benign requests.
Gemini 3.5 Flash-Lite targets a different job: document processing and agentic search running at volume rather than reasoning depth. The Artificial Analysis Index measured the model at 350 output tokens per second, the fastest in the 3.5 series according to Google.
Pricing runs at $0.3/1M input tokens and $2.5/1M output tokens, cheap enough that engineering teams can route simple, high-volume subagent requests to a minimal thinking level and reserve higher thinking levels for multi-step work.
On Google’s GDM-MRCR v2 long-context test, Gemini 3.5 Flash-Lite recorded a 72.2 percent success rate against 60.1 percent for its predecessor, and its GDPval-AA v2 score nearly doubled, from 642 to 1140. The model carries the same native computer-use tool as 3.6 Flash.
Separately, Google says Gemini 3.5 Pro remains in partner testing ahead of a full release, and pre-training for the next Gemini 4 architecture is already underway.
Automated vulnerability scanners now surface flaws faster than most security teams can patch them, and that gap is where Google positions Gemini 3.5 Flash Cyber.
The model is built to validate and remediate code vulnerabilities, and Google reports performance on the CyberGym benchmark competitive with frontier models (though it hasn’t made those figures public in the same detail as its consumer-facing releases.)
Distribution stays restricted to governments and vetted partners through a pilot programme, a limitation Google frames as a safeguard against the model generating exploit code for offensive use.
Inside Google’s CodeMender security agent, multiple instances of 3.5 Flash Cyber run in parallel, cross-checking one another’s findings before producing a single remediation report a human reviewer signs off on.
Engineering teams seeking to integrate these new models can access them through the Gemini API via Google AI Studio, Android Studio, or the Gemini Enterprise Agent Platform. Consumers can also access the new models in the Gemini app and 3.5 Flash-Lite is also rolling out in Google Search.
See also: Bristol Myers Squibb buys Nvidia AI system for drug discovery ：https://www.artificialintelligence-news.com/news/bristol-myers-squibb-nvidia-ai-system-drug-discovery/
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Ryan Daws：https://www.artificialintelligence-news.com/news/author/ryan/
AI Business Strategy：https://www.artificialintelligence-news.com/categories/inside-ai/ai-business-strategy/, Artificial Intelligence：https://www.artificialintelligence-news.com/categories/artificial-intelligence/, Features：https://www.artificialintelligence-news.com/categories/features/, Finance AI：https://www.artificialintelligence-news.com/categories/ai-in-action/finance-ai/, World of Work：https://www.artificialintelligence-news.com/categories/ai-and-us/world-of-work/
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