谷歌发布三款新Gemini Flash模型，但其前沿3.5 Pro仍在训练中

Google is releasing three efficient AI models—Gemini 3.6 Flash, 3.5 Flash-Lite, and the security-focused 3.5 Flash Cyber—while its flagship 3.5 Pro remains delayed. Gemini 4 is already in training. Each model serves a distinct purpose: 3.6 Flash cuts token usage at lower cost, Flash-Lite prioritizes speed, and the Cyber model is restricted to governments and partners due to potential risks. Without the Pro model, Google is losing ground to OpenAI, Anthropic, and even Meta, which are pushing ahead with more capable frontier models. Google is expanding the Gemini lineup with two Flash models and a specialized Cyber version. But the anticipated frontier model, Gemini 3.5 Pro, is still missing. Google has announced three new models in the Gemini Flash family : 3.6 Flash, 3.5 Flash-Lite, and the cybersecurity model 3.5 Flash Cyber. Buried in the announcement is the fact that Google's anticipated flagship, Gemini 3.5 Pro, is still being tested exclusively with partners and will ship "as soon as it is ready." Google says pretraining for Gemini 4 is already underway. The company calls it its "most ambitious training run" yet and says it is "excited by the progress." That reads like damage control, and it makes clear that Google knows what the market expects but can't deliver yet. Ad According to the benchmark aggregator Artificial Analysis Index , Gemini 3.6 Flash is expected to use about 17 percent fewer output tokens than 3.5 Flash. Google says the savings reach 65 percent on specific benchmarks such as DeepSWE. Google has cut the price to $1.50 per million input tokens and $7.50 per million output tokens, making it much cheaper than the earlier 3.1 Pro model, which 3.6 Flash consistently beats in benchmarks. Ad DEC_D_Incontent-1 Google also reports gains over 3.5 Flash. DeepSWE rises from 37 to 49 percent, MLE Bench from 49.7 to 63.9 percent, and OSWorld-Verified from 78.4 to 83 percent. The GDPval-AA v2 knowledge work benchmark improves from 1,349 to 1,421 points. Computer Use is now a built-in client-side tool in the Gemini API and Gemini Enterprise. Google has also added stronger Frontier Safety safeguards against CBRN misuse and cyberattacks. CBRN refers to chemical, biological, radiological, and nuclear threats. Ad Despite gains on multimodal tasks and a one million token context window, Google still trails the best models from competitors in the US and China. Logan Kilpatrick, a member of the technical staff, responded to criticism on X , saying the explicit goal was efficiency, usability, and lower cost, and that performance still improved in the process. The smaller Gemini 3.5 Flash-Lite is tuned for low latency and high throughput. According to Artificial Analysis , it produces 350 output tokens per second. It costs $0.30 per million input tokens and $2.50 per million output tokens. Ad DEC_D_Incontent-2 Google says Flash-Lite beats the older 3 Flash on several agentic and coding benchmarks, including SWE-Bench Pro and OSWorld-Verified. Compared with its direct predecessor, 3.1 Flash-Lite, its Terminal-Bench 2.1 score rises from 31 to 54 percent. Ad