HomeStepZen Unveils Step 3.7 Flash Large Model, Twice as Fast
China’s large model landscape has reached a new milestone with Step Zenith’s official release and open-sourcing of Step 3.7 Flash. Purpose-built for agent industrialization, this model features deep system-level optimizations for coding, web search, and multimodal workflows.
Architecturally, the model utilizes an advanced sparse Mixture-of-Experts (MoE) structure with 196 billion parameters. This design enables a generation speed of up to 400 tokens per second, drastically cutting latency in high-frequency, multi-turn interactions.
Native Multimodal and Search Enhancement
Step 3.7 Flash offers robust native multimodal comprehension, directly interpreting complex visuals like UI layouts, charts, and documents. This capability allows the model to swiftly transform visual inputs into structured data and generate executable code on the fly.
Furthermore, the model significantly boosts online retrieval and image search functionalities. It actively gathers multi-source evidence from text and images across open networks, ensuring information accuracy through rigorous cross-verification.
Highly Reliable Orchestration and Ecosystem Compatibility
Designed for complex tasks, the model exhibits exceptional stability in long-chain, multi-turn agent workflows. It seamlessly integrates with APIs, browsers, terminals, and Office tools, effectively minimizing execution errors or deviations.
Additionally, the model is deeply optimized for compatibility with mainstream agent development frameworks and tool invocation protocols. This optimization lowers the barrier to entry for developers, streamlining workflow orchestration and ecosystem deployment.
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China’s large model landscape has reached a new milestone with Step Zenith’s official release and open-sourcing of Step 3.7 Flash. Purpose-built for agent industrialization, this model features deep system-level optimizations for coding, web search, and multimodal workflows.
Architecturally, the model utilizes an advanced sparse Mixture-of-Experts (MoE) structure with 196 billion parameters. This design enables a generation speed of up to 400 tokens per second, drastically cutting latency in high-frequency, multi-turn interactions.
Native Multimodal and Search Enhancement
Step 3.7 Flash offers robust native multimodal comprehension, directly interpreting complex visuals like UI layouts, charts, and documents. This capability allows the model to swiftly transform visual inputs into structured data and generate executable code on the fly.
Furthermore, the model significantly boosts online retrieval and image search functionalities. It actively gathers multi-source evidence from text and images across open networks, ensuring information accuracy through rigorous cross-verification.
Highly Reliable Orchestration and Ecosystem Compatibility
Designed for complex tasks, the model exhibits exceptional stability in long-chain, multi-turn agent workflows. It seamlessly integrates with APIs, browsers, terminals, and Office tools, effectively minimizing execution errors or deviations.
Additionally, the model is deeply optimized for compatibility with mainstream agent development frameworks and tool invocation protocols. This optimization lowers the barrier to entry for developers, streamlining workflow orchestration and ecosystem deployment.
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