Text / deepseek-ai

DeepSeek V4 Flash 0731

DeepSeek V4 Flash 0731 is a text-generation model released under the MIT license on the deepseek-ai repository. It is described as the official release of DeepSeek-V4-Flash, superseding a preview version, with enhanced agentic capabilities. It shares the same model structure as DeepSeek-V4-Flash-DSpark and includes a speculative decoding module. The release does not include a Jinja-format chat template; instead, an encoding folder provides Python scripts and test cases for converting OpenAI-compatible messages to model inputs and parsing outputs. The reasoning_effort parameter supports three levels. Benchmark tables compare its reported scores with other models.

EstimatedRepository opened Jul 31, 2026Source checked 9/23/2026Version: 7872f01b
LOCALRENTED GPUOPEN WEIGHTS
Freshness 75 out of 100. Repository age: 76 days old, Jul 31, 2026. Measures age, not quality.
Popularity 90 out of 100. 4,416,319 downloads in 30 days; 4,083 lifetime likes. Checked Oct 15, 2026. Measures adoption, not quality.
What do these scores mean?

Two ways to compare models. Neither score measures answer quality, benchmark performance, or whether a model fits your machine.

Freshness · release age

A new release starts at 100. The score halves every 180 days: 100 × 2^(−age in days / 180). When the release date is unavailable, a repository date is a fallback—not proof of when the model was released. Missing dates stay unscored.

Check the date source
Popularity · adoption

Downloads over the past 30 days carry 80% of the score; lifetime likes carry 20%. Both use a logarithmic scale, capped at 10 million downloads and 100,000 likes. If likes are unavailable, downloads carry all the weight. Missing download statistics stay unscored.

round(80 × min(1, log10(1 + downloads) / 7) + 20 × min(1, log10(1 + likes) / 5))

Check the statistics source
Scores as of Oct 15, 2026. Statistics checked Oct 15, 2026. Statistics are marked for refresh after 72 hours.Full scoring method

At a glance

Parameters
304.18B
Architecture
deepseek_v4
Context length
1,048,576
License
mit
Disk space
155.4 GB
Software
SGLang, vLLM, Publisher PyTorch reference
View the model source
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Best for

  • Text generation
  • Agentic tasks
  • Code agent tasks
  • Tool-use benchmarks
  • Full-stack development tasks (internal DSBench)

Tradeoffs

  • Does not include a Jinja-format chat template
  • Requires the dedicated encoding folder for message encoding and output parsing
  • Speculative decoding module is included but no compatibility or performance details are provided
  • Numerical hardware requirements are not stated
  • Benchmark results are reported under specific evaluation settings and may not reflect other conditions

Ways to run it

SGLang · Linux

Repository-specific command in publisher documentation; confirm dependencies and hardware in the source.

sglang serve --trust-remote-code --model-path deepseek-ai/DeepSeek-V4-Flash-0731 --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
Official instructions

vLLM · Linux

Repository-specific command in publisher documentation; confirm dependencies and hardware in the source.

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"}'
Official instructions

Publisher PyTorch reference · Linux / rented GPU

Repository-specific command in publisher documentation; confirm dependencies and hardware in the source.

torchrun --nproc-per-node ${MP} generate.py --ckpt-path ${SAVE_PATH} --config ${CONFIG} --interactive
Official instructions

Explore its uses

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DeepSeek V4 Flash 0731: hardware, VRAM & setup | YouRunAI