DeepSeek Pro
A flagship large language model from DeepSeek, featuring strong planning and reasoning capabilities. It is suited for planning and review stages within multi-agent workflows.
Timeline (last 90 days)
GLM 5.3 ranked third, and the engineer alternates between DeepSeek Pro and GLM 5.3 for the planner and reviewer roles.
DeepSeek Pro ranked fourth and is one of the engineer's daily workhorses for agentic workflows, commonly used in the planning and review phases.
In the elemental sandbox test, MiMo-V2.6 Pro scored 8 points thanks to its visual expressiveness and complete translation, while DeepSeek Pro scored only 6 points due to untranslated menus.
DeepSeek's Pro model has an activation ratio of approximately 3% (49B activated / 1.6T total parameters), which is a typical characteristic of MoE architecture.
DeepSeek Pro's 1.6T total parameters with 49B activated parameters result in inference costs comparable to a roughly 49B dense model, while expanding knowledge capacity by approximately 32 times.
Pro still consistently outperforms Flash on complex tasks, with a particularly notable gap in Chinese writing.
This transition from Pro to Flash allowed only a four-day buffer period.
Flash is better suited for front-end tasks and real-image capabilities that Pro itself does not support.
After the 14th, requests sent to Pro models will be automatically routed to the new 4.1 Flash and billed at the Flash unit price, with the Pro version set to be taken offline soon.
All Facts (6)
DeepSeek Pro ranked fourth and is one of the engineer's daily workhorses for agentic workflows, commonly used in the planning and review phases.
50%UnverifiedGLM 5.3 ranked third, and the engineer alternates between DeepSeek Pro and GLM 5.3 for the planner and reviewer roles.
50%UnverifiedDeepSeek's Pro model has an activation ratio of approximately 3% (49B activated / 1.6T total parameters), which is a typical characteristic of MoE architecture.
50%UnverifiedDeepSeek Pro's 1.6T total parameters with 49B activated parameters result in inference costs comparable to a roughly 49B dense model, while expanding knowledge capacity by approximately 32 times.
50%UnverifiedPro still consistently outperforms Flash on complex tasks, with a particularly notable gap in Chinese writing.
50%UnverifiedFlash is better suited for front-end tasks and real-image capabilities that Pro itself does not support.
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