DeepSeek: DeepSeek V4.1 Flash 1017在线50% OFF

DeepSeek V4.1 Flash is a multimodal Mixture-of-Experts (MoE) model released by DeepSeek on September 10, 2026. It combines text and image understanding with reasoning, coding, tool use, and long-context processing. The model is designed to deliver higher throughput and lower inference costs for AI agents and other input-heavy workloads.

DeepSeek V4.1 Flash uses a 40-layer Causal Encoder-Decoder architecture consisting of a 20-layer causal encoder and a 20-layer decoder. Its backbone contains 552 billion parameters, while approximately 8 billion parameters are activated per token during prefill and 16 billion during decoding. It also uses Compressed Sparse Attention 2, Engram conditional memory, and DSpark speculative decoding to improve inference efficiency.

DeepSeek V4.1 Flash supports a context window of up to 1 million tokens and a maximum output of up to 384K tokens. The large context window is useful for long documents, code repositories, extended conversations, retrieval-augmented generation, and AI agent workflows with extensive tool histories.

Yes. DeepSeek V4.1 Flash natively accepts both text and image input and produces text output. It can be used for image understanding, document analysis, chart interpretation, visual question answering, and other multimodal tasks. It is not a text-to-image, text-to-video, text-to-speech, or automatic speech recognition model.

DeepSeek V4.1 Flash is well suited to:

• Coding, debugging, code review, and repository analysis
• AI agents and tool-based workflows
• Long-context document analysis and RAG
• Image and document understanding
• Structured data extraction and JSON generation
• Math, reasoning, and quantitative analysis
• High-throughput applications where latency and cost matter

Yes. The model supports thinking and non-thinking modes, tool calling, JSON output, and multi-turn conversations. Its reasoning effort can be adjusted to balance accuracy, speed, and token cost, making it suitable for both lightweight API calls and more complex problem-solving workflows.

In DeepSeek’s published evaluations at maximum reasoning effort, DeepSeek V4.1 Flash scored 74.2% resolved on DeepSWE v1.1, 90.6% on Terminal-Bench 2.1, and 88.1% on CyberGym. It also received a Codeforces rating of 3471. These are vendor-reported benchmark results obtained under specific evaluation settings, so production performance may vary with prompts, tools, agent scaffolds, and reasoning configuration.

开桥 pricing can vary by provider, time window, and active promotion. Check the live provider table on this page for current input, output, and cache rates before estimating production costs.

Create a 开桥 API key, use the OpenAI-compatible base URL https://openqiao.com/api/v1, and set the model field to deepseek/deepseek-v4.1-flash. 开桥 supports REST API requests, streaming, SDK-based integrations, provider routing, and usage monitoring for production applications. You can also explore other DeepSeek models available on 开桥 before choosing a model for your workload.

Yes. DeepSeek has published the DeepSeek V4.1 Flash model weights under the MIT License. Developers who have sufficient GPU, memory, and storage resources can deploy the model themselves using supported inference frameworks. Developers who prefer managed inference can access it through 开桥 without operating the underlying hardware.