DeepSeek-V4.1-Flash-1017 is a multimodal, image-text-to-text model release focused on tighter reasoning, coding, agentic behavior, and visual task performance while lowering runtime and output length to improve cost-efficiency. The update claims measurable gains across many benchmarks versus prior variants and competitors: representative scores include K3 (~42.6), Terminal-Bench 4.0 (~42.3), DeepSWE v1.1 (77.0), ProgramBench (29.1), NL2Repo-Bench (68.2), Chartography with tools (83.0) and BabyVision with tools (92.5). The release emphasizes advancing the performance-cost Pareto frontier for agent harnesses and reports both text-only and tool-enabled evaluations.
Practical details cover end-to-end usage and productionization: ready-to-use examples for Transformers pipelines, AutoModelForCausalLM, vLLM and SGLang servers, and Docker deployment; quantized builds and compatibility with llama.cpp/Ollama/LM Studio are available. Prompt encoding is provided as a Python reference (encoding.py) plus deepseek-recipe Rust libraries with Python bindings to encode conversations, tool calls, thinking mode, interleaved images, and to parse streamed outputs. Weights are distributed as sharded safetensors (large total size, with quantization options), licensed under MIT. Recommended sampling settings: temperature 1.0, top_p 1.0, context window up to 1M tokens and max_tokens ≥256K. Contact for support: [email protected].
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DeepSeek V4.1 Flash 1017 is a sparse mixture-of-experts model built on the Causal Encoder-Decoder architecture, designed for coding, long-horizon tasks, and visual-text understanding. It offers improved performance and cost efficiency, with reduced cache memory and faster task completion compared to previous versions. (openrouter.ai)
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