# DeepSeek V4.1 Flash 1017

DeepSeek V4.1 Flash 1017 is now available on Cloudflare Workers AI. The multimodal Mixture-of-Experts model has a 552B-parameter backbone but activates only 8B parameters per token during prefill and 16B during decode, which keeps input-heavy agentic workloads fast and low-cost. The 1017 release improves reasoning, coding, agentic tasks and visual benchmarks over the prior Flash version and completes tasks with shorter outputs, with image-plus-text requests, reasoning on by default, multi-turn function calling and a 1,048,576-token context window.

DeepSeek V4.1 Flash 1017 is now available on Cloudflare Workers AI. The multimodal Mixture-of-Experts model has a 552B-parameter backbone but activates only 8B parameters per token during prefill and 16B during decode, which keeps input-heavy agentic workloads fast and low-cost. The 1017 release improves reasoning, coding, agentic tasks and visual benchmarks over the prior Flash version and completes tasks with shorter outputs, with image-plus-text requests, reasoning on by default, multi-turn function calling and a 1,048,576-token context window.

DeepSeek V4.1 Flash 1017 is now available on AI Gateway as a minor update to DeepSeek V4.1 Flash, improving reasoning, coding, agentic tasks and image understanding. Coding gains appear in terminal work and in building and editing whole repositories, agentic gains in multi-step automation, and with images the model reads charts and handles visual reasoning more accurately when it can call tools. It also finishes tasks faster and with shorter outputs than the previous snapshot, so agent runs tend to take less time and use fewer output tokens.

DeepSeek V4.1 Flash 1017 is now available on AI Gateway as a minor update to DeepSeek V4.1 Flash, improving reasoning, coding, agentic tasks and image understanding. Coding gains appear in terminal work and in building and editing whole repositories, agentic gains in multi-step automation, and with images the model reads charts and handles visual reasoning more accurately when it can call tools. It also finishes tasks faster and with shorter outputs than the previous snapshot, so agent runs tend to take less time and use fewer output tokens.