DeepSeek が DeepSeek-V4 を発表
DeepSeek から DeepSeek-V4 がリリースされました。
▸何が変わったのか
DeepSeek-V4-Flash-0731
Technical Report👁️
Introduction
DeepSeek-V4-Flash-0731 is the official release of DeepSeek-V4-Flash, superseding the preview version, with substantially enhanced agentic capabilities. It has the same model structure as DeepSeek-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.
| Benchmark | DeepSeek-V4-Flash-0731 | DeepSeek-V4-Flash (Preview) | DeepSeek-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 |
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 the max reasoning effort level with temperature = 1.0, topp = 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.
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 reasoningeffort parameter now supports three levels — low, high, and max — which control how much deliberation the model spends before answering.
A brief example:
python
from encodingdsv4 import encodemessages, parsemessagefromcompletiontext
messages = [
{“role”: “user”, “content”: “hello”},
{“role”: “assistant”, “content”: “Hello! I am DeepSeek.”, “reasoningcontent”: “thinking…”},
{“role”: “user”, “content”: “1+1=?”}
]
messages -> string
prompt = encodemessages(messages, thinkingmode=”thinking”, reasoningeffort=”max”)
string -> tokens
import transformers
tokenizer = transformers.AutoTokenizer.frompretrained(“deepseek-ai/DeepSeek-V4-Flash-0731”)
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”,”numspeculativetokens”:7,”draftsamplemethod”:”greedy”}’
For example, the command below serves the model with vLLM on a single 4×GB300 node.
See the v
SOURCE: DeepSeek (2026-07-31)
