google gemini 3.5 flash Features

Unlike competitors, google gemini 3.5 flash is natively multimodal, handling text, audio, and video in one stream. Its 1M token context window allows for massive document analysis without losing detail. It is specifically optimized for high-speed inference, providing a much lower latency compared to larger models while maintaining high accuracy in coding and reasoning tasks across various benchmarks like MMLU and HumanEval.

We offer the base google pricing: $0.075 per 1M input tokens and $0.30 per 1M output tokens for smaller prompts. For long-context tasks over 128k tokens, a slight surcharge applies. By using our API, you gain access to context caching which can reduce repetitive query costs by up to 90%, making it one of the most economical high-performance models available for enterprise-scale AI workflows and agentic applications.

Yes, it natively supports video content. You can upload up to an hour of video, and the model reasons across frames without needing a separate transcription or image-extraction layer. This makes it ideal for summarizing meetings, analyzing security footage, or performing sentiment analysis on visual content. It preserves temporal data far better than older models that rely on manual frame sampling or separate encoders.

No. When you access google gemini 3.5 flash through GPTProto's enterprise-grade API, your data is never used for training underlying models. We prioritize security and privacy, ensuring that your prompts and outputs remain confidential. This is critical for businesses handling sensitive customer data or proprietary codebases who want the speed of a 'flash' model without compromising their intellectual property or security.

It is designed for extreme speed. The 'flash' designation refers to its optimized inference engine, which delivers a Time To First Token (TTFT) approximately 40-50% faster than the Gemini 1.5 Pro version. For most standard text prompts, users can expect latencies under 400ms. This speed makes it the perfect engine for real-time customer support bots and interactive tools where user experience depends on instant feedback.

Absolutely. google gemini 3.5 flash supports structured outputs via a dedicated JSON mode. By setting the response_mime_type to application/json or using constrained output parameters, developers can ensure the model returns data in a predictable format. This is vital for integrating the model into existing software stacks, performing automated data extraction, or powering complex multi-step agentic workflows that require precise data.