1. Summary

DeepSeek-V4-Flash-0731 is the official release replacing the preview version of DeepSeek-V4-Flash. Its agent capabilities have been significantly enhanced.

Despite having fewer active parameters, it demonstrates performance exceeding that of DeepSeek-V4-Pro (Preview). It is reported to have performance that competes broadly with commercial models.

2. Bunrin Bench (BUNRIN Lab Original Test)

No data (scheduled). For the methodology, refer to About Bunrin Bench.

3. Benchmarks

The following benchmark results are listed on the Model Card. Code Agent tasks were evaluated using DeepSeek Harness (minimal mode) as the agent framework, with reasoning effort set to max, temperature = 1.0, and top_p = 0.95 (Model Card Note 1).

Benchmark V4-Flash-0731 V4-Flash (Preview) V4-Pro (Preview) GLM-5.2 Opus-4.8
Terminal Bench 2.1 82.7 61.8 72.1 81.0 85.0
NL2Repo 54.2 39.4 38.5 48.9 69.7
Cybergym 76.7 38.7 52.7 - 83.1
DeepSWE 54.4 7.3 12.8 46.2 58.0
Toolathlon-Verified 70.3 49.7 55.9 59.9 76.2
Agents' Last Exam 25.2 15.8 16.5 23.8 25.7
AutomationBench Public 25.1 10.8 12.8 12.9 27.2
DSBench-FullStack † 68.7 37.0 41.8 61.8 71.6
DSBench-Hard † 59.6 25.8 31.1 54.5 71.7

† DSBench-FullStack is an internal full-stack development test set, and DSBench-Hard is an internal high-difficulty coding agent problem test set (Model Card Note 2).

4. Official announcements

DeepSeek-V4-Flash-0731 was released on 2026-07-31. According to the Model Card, this model shares the same structure as DeepSeek-V4-Flash-DSpark and includes a speculative decoding module.

A reasoning_effort parameter has been introduced. The level of deliberation before generating a response can be controlled in three stages: low, high, and max. This allows for precise control.

An encoding folder is included for performing encoding and decoding in an OpenAI-compatible format.

5. Real-world performance (Community reception)

Reddit user /u/Fluffy-Ad-889 stated that local models are reaching performance levels close to those of models from major developers.

User /u/deathcom65 reported that it excels in fast token generation and research.

In llama.cpp, support for quantization type concat was added as an update related to DeepSeek-V4-Flash-0731. This is a significant update.

6. Recommended parameters

The parameters recommended by the Model Card are as follows:

  • temperature: 1.0
  • top_p: 0.95 (for agent-based scenarios)
  • top_p: 1.0 (for other scenarios)
  • reasoning_effort: low / high / max

For high and max settings, the maximum output length is 384K tokens.

7. Sources