待翻译：DeepSeek-AI/DeepSeek-V4-Pro-0813

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It is built on the DeepSeek-V4-Pro (Preview) model structure, with a DSpark speculative decoding module attached. DeepSeek-V4-Pro-0813 outperforms DeepSeek-V4-Pro (Preview) on the benchmarks listed below, and is broadly competitive with the strongest proprietary models available. Benchmark DeepSeek-V4-Pro-0813 DeepSeek-V4-Flash-0731 DeepSeek-V4-Pro (Preview) DeepSeek-V4-Flash (Preview) GLM-5.2 Kimi K3 Opus-4.8 Fable-5 (w/ fallback) HLE (wo / w tools) 42.7 / 60.0 37.8 / 51.5 37.7 / 48.2 34.8 / 45.1 40.5 / 54.7 43.5 / 56.0 49.8 / 57.9 53.3 / 63.0 Terminal Bench 2.1 87.9 82.7 72.1 61.8 81.0 88.3 85.0 88.0 NL2Repo 61.5 54.2 38.5 39.4 48.9 - 69.7 - Cybergym 83.3 76.7 52.7 38.7 - 80.0 78.3 83.1 DeepSWE 62.7 54.4 12.8 7.3 46.2 67.5 58.0 70.0 Toolathlon-Verified 74.1 70.3 55.9 49.7 59.9 76.5 76.2 77.9 Agents' Last Exam 25.7 25.2 16.5 15.8 23.8 27.6 25.7 - AutomationBench (Public) 31.8 25.1 12.8 10.8 12.9 30.8 27.2 29.1 DSBench-FullStack † 71.1 68.7 41.8 37.0 61.8 73.7 71.6 77.2 DSBench-Hard † 67.2 59.6 31.1 25.8 54.5 63.0 71.7 68.3 Notes: For the code-agent tasks among the public benchmarks above, DeepSeek-V4-Pro-0813 is evaluated with the minimal mode of DeepSeek Harness as the agent framework, using the max reasoning effort level with temperature = 1.0, top_p = 0.95. † DSBench-FullStack is an internal full-stack development test set; DSBench-Hard is an internal test set of difficult coding-agent problems. Chat Template This release does not include a Jinja-format chat template. Instead, we provide a dedicated encoding folder with Python scripts and test cases demonstrating how to encode messages in OpenAI-compatible format into input strings for the model, and how to parse the model's text output. Please refer to the encoding folder for full documentation. The reasoning_effort parameter now supports three levels — low, high, and max — which control how much deliberation the model spends before answering. A brief example: from encoding_dsv4 import encode_messages, parse_message_from_completion_text messages = [ {"role": "user", "content": "hello"}, {"role": "assistant", "content": "Hello! I am DeepSeek.", "reasoning_content": "thinking..."}, {"role": "user", "content": "1+1=?"} ] # messages -> string prompt = encode_messages(messages, thinking_mode="thinking", reasoning_effort="max") # string -> tokens import transformers tokenizer = transformers.AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-V4-Pro-0813") tokens = tokenizer.encode(prompt) How to Run with vLLM DSpark speculative decoding is enabled with a single flag — add --speculative-config with method: dspark to your vLLM launch command: --speculative-config '{"method":"dspark","num_speculative_tokens":7,"draft_sample_method":"greedy"}' For example, the command below serves the model with vLLM on a single 4×GB300 node. See the vLLM recipe for detailed instructions and other hardware configurations. vllm serve deepseek-ai/DeepSeek-V4-Pro-0813 \ --trust-remote-code --kv-cache-dtype fp8 --block-size 256 \ --data-parallel-size 4 --enable-expert-parallel \ --moe-backend deep_gemm_mega_moe \ --attention-config '{"use_fp4_indexer_cache": true}' \ --speculative-config '{"method":"dspark","num_speculative_tokens":7,"draft_sample_method":"greedy"}' How to Run with SGLang Enable DSpark with --speculative-algorithm DSPARK and do not set a separate --speculative-draft-model-path as the target and draft weights therefore come from the same checkpoint. See the SGLang cookbook for detailed instructions, benchmarks and other hardwares configurations. sglang serve \ --trust-remote-code \ --model-path deepseek-ai/DeepSeek-V4-Pro-0813 \ --tp 4 \ --moe-runner-backend flashinfer_mxfp4 \ --speculative-algorithm DSPARK \ --mem-fraction-static 0.90 \ --chunked-prefill-size 4096 \ --swa-full-tokens-ratio 0.1 \ How to Run Locally Please refer to the inference folder for detailed instructions on running DeepSeek-V4 locally, including model weight conversion and interactive chat demos. For local deployment, we recommend setting the sampling parameters to temperature = 1.0, with top_p = 0.95 for agentic scenarios and top_p = 1.0 otherwise. For the high and max reasoning effort levels, we recommend a maximum output length of 384K tokens. License This repository and the model weights are licensed under the MIT License. Citation @misc{deepseekai2026deepseekv4, title={DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence}, author={DeepSeek-AI}, year={2026}, } Contact If you have any questions, please raise an issue or contact us at [email protected]. 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