deepseek-ai/DeepSeek-V4-Flash-0731 · Hugging Face

Source: https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731

Technical Report👁️

https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731#introductionIntroduction

DeepSeek-V4-Flash-0731is the official release ofDeepSeek-V4-Flash, superseding the preview version, with substantially enhanced agentic capabilities. It has the same model structure asDeepSeek-V4-Flash-DSpark, i.e. it comes with a speculative decoding module attached.

DeepSeek-V4-Flash-0731 outperforms DeepSeek-V4-Pro (Preview) on benchmarks listed below despite its far smaller activated parameter count, and is broadly competitive with the strongest proprietary models available.

BenchmarkDeepSeek-V4-Flash-0731DeepSeek-V4-Flash (Preview)DeepSeek-V4-Pro (Preview)GLM-5.2Opus-4.8Terminal Bench 2.182.761.872.181.085.0NL2Repo54.239.438.548.969.7Cybergym76.738.752.7-83.1DeepSWE54.47.312.846.258.0Toolathlon-Verified70.349.755.959.976.2Agents’ Last Exam25.215.816.523.825.7AutomationBench Public25.110.812.812.927.2DSBench-FullStack †68.737.041.861.871.6DSBench-Hard †59.625.831.154.571.7 Notes:

1. For the Code Agent tasks among the public benchmarks above, DeepSeek-V4-Flash-0731 is evaluated with the minimal mode of DeepSeek Harness (to be released) as the agent framework, using themaxreasoning effort level withtemperature = 1\.0, top\_p = 0\.95.
2. † DSBench-FullStack is an internal full-stack development test set; DSBench-Hard is an internal test set of difficult coding-agent problems.

https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731#chat-templateChat Template

This release does not include a Jinja-format chat template. Instead, we provide a dedicatedencodingfolder 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 theencodingfolder for full documentation.

Thereasoning\_effortparameter now supports three levels —low,high, andmax— 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-Flash-0731")
tokens = tokenizer.encode(prompt)

https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731#how-to-run-with-vllmHow 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 thevLLM recipefor detailed instructions and other hardware configurations.

vllm serve deepseek-ai/DeepSeek-V4-Flash-0731 \
  --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"}'

https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731#how-to-run-locallyHow to Run Locally

Please refer to theinferencefolder 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 totemperature = 1\.0, withtop\_p = 0\.95for agentic scenarios andtop\_p = 1\.0otherwise. For thehighandmaxreasoning effort levels, we recommend a maximum output length of384Ktokens.

https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731#licenseLicense

This repository and the model weights are licensed under theMIT License.

https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731#citationCitation

@misc{deepseekai2026deepseekv4,
      title={DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence},
      author={DeepSeek-AI},
      year={2026},
}

https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731#contactContact

If you have any questions, please raise an issue or contact us at[email protected].