Performance-wise, the V4 is, perhaps unsurprisingly, a huge leap forward from the R1 and looks to be a strong alternative to almost all the latest big AI models. On major benchmarks, DeepSeek V4-Pro competes with leading closed-source models, matching the performance of Anthropic’s Claude-Opus-4.6, OpenAI’s GPT-5.4, and Google’s Gemini-3.1, according to results shared by the company. And compared to other open source models, such as Alibaba’s Qwen-3.5 or Z.ai’s GLM-5.1, DeepSeek V4 outperforms them all on coding, math and STEM problems, making it one of the most robust open source models ever released.
DeepSeek also says that V4-Pro is now one of the strongest open-source models on benchmarks for agent coding tasks and performs well on other tests that measure its ability to execute multistep problems. According to benchmarking results shared by the company, its writing capabilities and global knowledge also lead the sector.
In a technical report released with the model, DeepSec shared the results of an internal survey of 85 experienced developers: more than 90% included the V4-Pro as their top model choice for coding tasks.
DeepSec says it has optimized V4 specifically for popular agent frameworks such as CloudCode, OpenClaw, and CodeBuddy.
2. It provides a new approach to memory efficiency.
One of V4’s key innovations is its long context window—the amount of text that the model can process at once. Both versions can handle 1 million tokens, which is large enough to fit three volumes. Lord of colors And The Hobbit The joint company says this context window size is now the default across all DeepSec services and matches what modern versions of models like Gemini and Cloud offer.
But it’s important to know that not only has DeepSec made this leap, but how He did so. V4 makes significant architectural changes to the company’s previous models—particularly in the attention mechanism, which is a feature of AI models that helps them understand each part of a prompt in relation to the rest. As the immediate text becomes longer, these comparisons become more expensive, making attention a significant constraint for long-context models.