DeepSeek

DeepSeek has exposed the search, file parsing, link reading, safety checking and voice capabilities behind the DeepSeek App through the DeepSeek API, so developers can switch on built-in features with a few parameters or call standalone APIs such as OCR, ASR and TTS. V4.1-Flash (deepseek-flash) supports these capabilities across Messages, Responses and Chat Completions, with tool calls and context assembly handled server-side. Extended capabilities incur tool usage fees on top of standard token charges, and public-facing apps must enable safety checks.

DeepSeek has exposed the search, file parsing, link reading, safety checking and voice capabilities behind the DeepSeek App through the DeepSeek API, so developers can switch on built-in features with a few parameters or call standalone APIs such as OCR, ASR and TTS. V4.1-Flash (deepseek-flash) supports these capabilities across Messages, Responses and Chat Completions, with tool calls and context assembly handled server-side. Extended capabilities incur tool usage fees on top of standard token charges, and public-facing apps must enable safety checks.

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 released DeepSeek-V4.1-Flash-1017, which it says delivers stronger benchmark performance, faster task completion and shorter outputs than the previous version, a Pareto improvement in performance and cost, now live on its website, app and API. The model uses reinforcement learning with self-feedback to improve end-to-end project development, with demos spanning 3D modeling, a web racing game and a Godot-engine recreation. The Search and Fetch APIs also opened to everyone at $0.75 per 1,000 calls per endpoint.

DeepSeek released DeepSeek-V4.1-Flash-1017, which it says delivers stronger benchmark performance, faster task completion and shorter outputs than the previous version, a Pareto improvement in performance and cost, now live on its website, app and API. The model uses reinforcement learning with self-feedback to improve end-to-end project development, with demos spanning 3D modeling, a web racing game and a Godot-engine recreation. The Search and Fetch APIs also opened to everyone at $0.75 per 1,000 calls per endpoint.

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.