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MarkTechPost · 2026/7/21 17:45:08

Google Releases Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber: A Cheaper, More Token-Efficient Flash Tier Built for Agentic Workloads

AI 中文解读
Google一口气发布了三款新AI模型,全是主打性价比的“Flash”系列。最核心的亮点是:新一代模型不仅更聪明,还更便宜、说话更简练,特别适合那些需要AI反复调用工具、多步骤推理的复杂任务,比如自动写代码、分析文档。 简单来说,这就像给AI“减肥”又“增肌”。新发布的Gemini 3.6 Flash在回答问题时用的字数比上一代少了近20%,有时候甚至能少65%,但推理能力反而更强了——在编程和知识类测试中得分大幅提升。同时,输出价格从每百万token9美元降到7.5美元,开发者做同一个任务的成本直接缩水。另一款“Flash Lite”则主打极速,每秒能吐出350个token,适合做实时搜索和文档处理,价格更是低到每百万输入token只要0.3美元。 对普通人来说,这些升级意味着用AI工具的成本会越来越低,反应速度也会更快。比如你让AI帮你整理一份长报告,它现在不仅理解更准,还不会啰里啰嗦写一堆废话,而且后台成本下降,最终可能反映到咱们用的APP或服务上——功能更强、价格更亲民。对于企业而言,部署AI助手的门槛进一步降低,从客服到数据分析,更多智能化场景将加速落地。
Developers building production agents need higher token efficiency, lower latency, and more reliable performance. Today, Google has released three new Gemini models. The lineup is Gemini 3.6 Flash, Gemini 3.5 Flash-Lite, and Gemini 3.5 Flash Cyber. All three sit in the Flash tier, which Google tunes for speed, cost, and high-volume agentic work rather than maximum reasoning depth. Gemini 3.6 Flash: better quality, fewer tokens, lower price Gemini 3.6 Flash is the new default workhorse. It builds on 3.5 Flash and targets coding, knowledge work, and multimodal tasks. The main point is efficiency. On the Artificial Analysis Index, 3.6 Flash uses 17% fewer output tokens than 3.5 Flash. On the DeepSWE benchmark by Datacurve, Google reports up to a 65% reduction. The model also takes fewer reasoning steps and tool calls per multi-step workflow. Pricing moves down alongside efficiency. Gemini 3.6 Flash is priced at $1.50 per 1M input tokens and $7.50 per 1M output tokens. The output rate drops from the previous $9.00 on 3.5 Flash. Lower verbosity and a lower output price reduces the total cost per agentic task. Quality gains accompany the efficiency gains. On DeepSWE, 3.6 Flash scores 49% versus 37% for 3.5 Flash. On MLE Bench, it reaches 63.9% versus 49.7%. On OSWorld-Verified, it hits 83.0% versus 78.4%. On GDPval-AA v2, a knowledge-work benchmark, it scores 1421 versus 1349. Computer use is now a built-in client-side tool through the Gemini API and Gemini Enterprise. Early customers including Hebbia and Harvey cite gains in document parsing, chart and data analysis, and report drafting. Google is shipping 3.6 Flash with enhanced Frontier Safety safeguards. These cover Chemical, Biological, Radiological, and Nuclear (CBRN) and cyber-offense misuse. Full details are in the 3.6 Flash model card. The interactive explainer below lets you compare each model against its predecessor and estimate token cost at your own volume. (function(){ window.addEventListener('message', function(e){ var d = e.data || {}; if(d && d.mtpEmbed === 'explorer' && typeof d.height === 'number'){ var f = document.getElementById('mtp-gemini-explorer'); if(f){ f.style.height = Math.max(320, d.height) + 'px'; } } }); })(); Gemini 3.5 Flash-Lite: the fastest model in the 3.5 line Gemini 3.5 Flash-Lite is highlighted for low-latency and high-throughput jobs. Target use cases include agentic search and document processing. As measured by Artificial Analysis, it runs at 350 output tokens per second. Pricing is $0.30 per 1M input tokens and $2.50 per 1M output tokens. The model clears the prior 3.1 Flash-Lite by wide margins. On Terminal-Bench 2.1, it scores 54% versus 31%. On GDM-MRCR v2, a long-context benchmark, it reaches 72.2% versus 60.1%. On GDPval-AA v2, it scores 1140 versus 642. Notably, Flash-Lite also beats the older 3 Flash on some evals. It leads on SWE-Bench Pro at 54.2% versus 49.6% and on OSWorld-Verified at 74.0% versus 65.1%. Flash-Lite exposes configurable thinking levels: minimal, low, and higher. Developers can prioritize low-cost, low-latency execution for high-volume tasks. They can also engage higher thinking levels for multi-step subagent workloads. Computer use is a built-in tool here too. Gemini 3.5 Flash Cyber in CodeMender: cheap agents that find and patch bugs Gemini 3.5 Flash Cyber is the most specialized release. It is built on 3.5 Flash and fine-tuned to find, validate, and patch software vulnerabilities. The design premise is the search-space problem. Finding deep flaws means exploring an immense execution search space. A single call to one massive model becomes a bottleneck. The answer is a cheap model called many times. Inside CodeMender, Google’s code-security agent, multiple 3.5 Flash Cyber agents run in parallel. CodeMender invokes the model up to five times, then merges the sub-agent findings into one report. On the CyberGym benchmark, this setu
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