In terms of performance, the V4 is, perhaps surprisingly, a huge leap forward from the R1 – and it appears to be a strong alternative to almost all the latest large AI models. According to results shared by the company, on key benchmarks, DeepSeek v4-Pro competes with leading closed-source models, matching the performance of Anthropic’s Cloud-Opus-4.6, OpenAI’s GPT-5.4, and Google’s Gemini-3.1. And compared to other open-source models, such as Alibaba’s QUEN-3.5 or Z.AI’s GLM-5.1, DeepSeek v4 outperforms them all in coding, math, and STEM problems, making it one of the most robust open-source models ever released.
DeepSeek also says that V4-Pro now ranks among the strongest open-source models on benchmarks for agentic coding tasks and performs well on other tests that measure the ability to complete multistep problems. According to benchmarking results shared by the company, its writing capabilities and world knowledge are also leading in the field.
In a technical report released alongside the model, DeepSeek shared the results of an internal survey of 85 experienced developers: more than 90% included the V4-Pro among their top model choices for coding tasks.
DeepSeek says it has optimized V4 specifically for popular agent frameworks like Cloudflare, OpenClaw, and CodeBuddy.
2. It provides a new approach to memory efficiency.
One of the key innovations of V4 is its longer context window – the amount of text the model can process at once. Both versions can handle 1 million tokens, which is enough to fit all three versions lord of the rings And the hobbit Joint. The company says this reference window size is now the default across all DeepSeek services and matches the size offered by state-of-the-art versions of models like Gemini and Cloud.
But it’s important to know that not only has DeepSeek made this leap, but How It did just that. V4 makes significant architectural changes to the company’s prior models – particularly in the attention mechanism, which features AI models that help them understand each part of the prompt in relation to the rest. As the instantiated text becomes longer, these comparisons become much more expensive, making attention one of the main bottlenecks for long-context models.
