# Google 发布 Gemini 3.6 Flash 与 3.5 Flash-Lite，降低企业 AI 智能体成本

- 来源：Artificial Intelligence News（RSS）
- 作者：Ryan Daws
- 发布时间：2026-07-22 00:06
- AIHOT 分数：65
- AIHOT 链接：https://aihot.virxact.com/items/cmruv44fg008zbii0eui6tcvw
- 原文链接：https://www.artificialintelligence-news.com/news/googles-gemini-3-6-flash-targets-enterprise-agent-token-costs

## AI 摘要

Google 发布 Gemini 3.6 Flash 与 3.5 Flash-Lite，旨在降低企业 AI 智能体的延迟与 token 成本。3.6 Flash 输出 token 比前代减少 17%，在 DeepSWE 上成功率达 49%（前代 37%），定价 $1.50/1M 输入 token。

## 正文

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.

The math behind Gemini 3.6 Flash

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, Hebbia, and Harvey put the model to work

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.

A cheaper tier for high-volume background agents

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.

Gemini 3.5 Flash Cyber: A restricted model for patching code

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.