DeepSeek V4 Flash 0731 achieves scores of 89.0% on ARC-AGI-1 Semi-Private at a cost of $0.02 per task and 61.4% on ARC-AGI-2 Semi-Private at $0.04 per task. The model also records scores of 87.0% and 56.0% for the High variant, and 84.0% and 46.0% for the Low variant on the respective benchmarks.

DeepSeek is a platform that requires users to sign up or log in to access its features. By doing so, users consent to DeepSeek's Terms of Service and Privacy Policy.

DeepSeek V4 Flash 0731 offers enhanced intelligence, improved performance metrics, and competitive pricing analysis. The model aims to optimize efficiency and effectiveness in AI applications.

The DeepSeek-V4-Flash API is now in public beta, with no changes to the API calling method; users can access it by setting the model name to deepseek-v4-flash. Benchmark scores for various tools include Terminal Bench 2.1 at 82.7, NL2Repo at 54.2, and Cybergym at 76.7.

DeepSeek, a Hangzhou-based AI startup, is developing its own chip to reduce reliance on Nvidia and Huawei's Ascend processors. This strategic pivot signals significant changes in China's AI landscape.

The US has decided not to blacklist DeepSeek, a Chinese company, along with over 100 other firms identified as security risks. This decision reflects ongoing tensions between the US and China regarding technology and security concerns.

DeepSeek V4 Pro is available at 5% of the cost of Claude and offers a range of features including hash-anchored edits, a sticky prefix cache, and autonomous loops for production code. It has been used for various applications such as training dose-prediction models for radiotherapy and developing a financial research agent, with no associated charges.

The GitHub repository "huggingface/open-r1" provides a fully open reproduction of the DeepSeek-R1 model. It includes scripts for installation, training, evaluation, and data generation, aiming to enable users to reproduce and build upon the R1 pipeline.

Posted by vinhnx. Score: 86 points. Comments: 63.

DeepSeek V4 Pro achieved a precision score of 38.0, outperforming GPT-5.5 Pro, which scored 33.0. DeepSeek excelled in handling overlapping patterns in a python log redactor task by using a single regex and replacer, while GPT-5.5 Pro utilized multiple regexes, leading to less effective results.