Windsurf’s SWE-1 comprises a frontier model, a lightweight model, and a low-latency model.
Windsurf said the SWE-1 was designed to perform the full complexity of the engineering process
Photo Credit: Windsurf
Windsurf, an artificial intelligence (AI) no-code or 'vibe coding' platform, released a series of AI models on Thursday. Dubbed SWE-1, these models are focused on complex software engineering tasks, and not just writing code. The series comprises SWE-1, SWE-1-lite, and SWE-1-mini, where each model is designed for specific use cases. The lite and mini versions are available to all Windsurf users, whereas the frontier SWE-1 model is only available to subscribers. The company has yet to reveal pricing for and availability for the frontier coding model.
The California-based AI firm detailed the new series of AI models in a blog post. SWE-1 is designed to tackle the complex tasks and responsibilities of a human, and not just write and edit code. The company says that while coding models have improved, their scope has not increased significantly.
Most models are still trained to produce code that compiles and passes unit tests, but that represents only a small part of what software engineers do. The company added that the next generation of coding models should be able to work across terminals, interpret user feedback, and handle tasks over a longer period.
Windsurf claims that the SWE-1 series is a step in that direction. The frontier model is said to perform at the level of Claude 3.5 Sonnet and comes with features such as tool-calling and reasoning. Windsurf claimed that the AI model would be priced cheaper than the Anthropic model.
On the other hand, the SWE-1-lite is a lightweight model which can perform everyday coding tasks. The company said it is available for unlimited use. Similarly, SWE-1-mini is a low-latency model which is best suited for real-time tasks. Both of them are available to all users, including those on the free tier.
Windsurf said that SWE-1 is the company's first attempt at a frontier model that can be compared to a human software engineer. As a result, the benchmark scores of the model were not as high as the company expected. However, it now plans to develop the SWE family further.
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