When it comes to efficiency, V4 is, maybe unsurprisingly, an enormous soar from R1—and it appears to be a powerful various to only about all the newest massive AI fashions. On the foremost benchmarks, based on outcomes shared by the corporate, DeepSeek V4-Professional competes with main closed-source fashions, matching the efficiency of Anthropic’s Claude-Opus-4.6, OpenAI’s GPT-5.4, and Google’s Gemini-3.1. And in comparison with different open-source fashions, resembling Alibaba’s Qwen-3.5 or Z.ai’s GLM-5.1, DeepSeek V4 exceeds all of them on coding, math, and STEM issues, making it one of many strongest open-source fashions ever launched.
DeepSeek additionally says that V4-Professional now ranks among the many strongest open-source fashions on benchmarks for agentic coding duties and performs nicely on different checks that measure capability to hold out multistep issues. Its writing capability and world information additionally lead the sphere, based on benchmarking outcomes shared by the corporate.
In a technical report launched alongside the mannequin, DeepSeek shared outcomes from an inner survey of 85 skilled builders: Greater than 90% included V4-Professional amongst their prime mannequin selections for coding duties.
DeepSeek says it has particularly optimized V4 for fashionable agent frameworks resembling Claude Code, OpenClaw, and CodeBuddy.
2. It delivers on a brand new method to reminiscence effectivity.
One of many key improvements of V4 is its lengthy context window—the quantity of textual content the mannequin can course of without delay. Each variations can deal with 1 million tokens, which is giant sufficient to suit all three volumes of The Lord of the Rings and The Hobbit mixed. The corporate says this context window measurement is now the default throughout all DeepSeek providers and it matches what is obtainable by cutting-edge variations of fashions like Gemini and Claude.
However it’s essential to know not simply that DeepSeek has made this leap, however how it did so. V4 makes vital architectural adjustments to the corporate’s former fashions—particularly within the consideration mechanism, which is the function of AI fashions that helps them perceive every a part of a immediate in relation to the remainder. Because the immediate textual content will get longer, these comparisons develop into far more expensive, making consideration one of many principal bottlenecks for long-context fashions.
