Software engineering-native AI models have arrived: What Windsurf’s SWE-1 means for technical decision-makers

Up to now, vibe coding platforms rely heavily on existing large-language models (LLMs), which help with code writing.

Writing code is just one of the many tasks that developers must perform to build an enterprise-grade production platform. To help with other tasks in the software engineering workflow, different tools are needed to review, commit, and maintain code. It’s a challenging task Windsurf (formerly Codeium) has launched a new wave of AI models called SWE-1 (software engineers 1) as part of its Wave 9 update.

This news comes at a time when Windsurf, a leader in AI, is reportedly acquiring OpenAI for up to $3 billion. The deal hasn’t been closed yet, and Windsurf hasn’t made any public comments about it.

SWE-1 family is a frontier-class AI model designed to accelerate entire software engineering process. SWE-1 was designed to tackle the full spectrum of software development activities, unlike general-purpose AI models which have been adapted to coding tasks.

These new models are designed to help developers deal with the multiple surfaces, incomplete states, and long-running tasks of real-world software development. SWE-1 is available immediately to Windsurf customers. It offers performance comparable to foundation models but with a focus software engineering workflows.

Anshul Ramachandran is the head of product and strategic at Windsurf. He told VentureBeat that “our main goal is to accelerate software engineering by 99%”.

Windsurf’s core innovation in SWE-1 is Windsurf’s recognition that software engineers do more than just code.

The approach addressed a critical limitation of current AI coding LLMs. Today, many different models are available for writing application code. These include OpenAI’s GPT-4.1, Anthropic’s Claude 3.7, and Google’s Gemini 2.50 Pro I/O Edition.

Windsurf’s modular interface allows for the use of different models. Ramachandran explained Windsurf users had told the company that existing coding model tends to work well with user guidance but tends to miss things over time.

The limitation is due to a fundamentally different task structure. While code generation can be a one-shot task, software engineering requires navigating multiple tools, working incomplete code, and maintaining context over long-running projects.

The SWE-1 Family: Designed for different engineering tasks.

Instead of creating a solution that fits all, Windsurf developed three specialized models.

1. SWE-1 (19459049]: Full-size, full-featured model for advanced reasoning and tools, available to paid users. SWE-1-lite (19459049) : A powerful, smaller model that replaces Windsurf’s Cascade Base. Available to all users. SWE-1-mini (19459049) : A lightweight model that powers passive code predictions within Windsurf Tab. This model is available to all users, free or paid.

SWE models were created through a thorough in-house training program that focused on software engineering tasks. Ramachandran stated that the company used an updated data model with sequential training steps.

Benchmarks for SWE-1 performance

Although SWE-1 won’t replace foundation models in major labs, Windsurf claims that it is capable of achieving frontier-class performance when used specifically for software engineering tasks. The company claims that it outperforms both mid-sized foundations models and open-weighted models. Windsurf is careful to not oversell its initial results. Ramachandran admitted that “even our benchmark shows that it’s not objectively superior to all the other models.”

The goal is to position SWE-1 in the first step towards purpose-built models which will eventually surpass general-purpose models for specific engineering tasks – and possibly at a lower price.

What makes Windsurf’s approach technically distinctive is its implementation of the flow awareness concept.

The basic idea is that a flow of steps need to happen as part of enterprise development. Rather than just writing code for one specific step, flow awareness is about being aware of the broader context.

Flow awareness is centered on creating a shared timeline of actions between humans and AI in software development. The core idea is to progressively transfer tasks from human to AI by understanding where AI can most effectively assist.

This approach creates a continuous improvement loop for the models.

“As we continue to improve the models, more of the steps in that shared timeline will be flipped from human to AI,” said Ramachandran. “The AI will be able to do more things that the human had to do before because the AI wasn’t right.”

What this means for technical decisions makers

SWE-1 represents a significant evolution in AI-assisted software development. This approach promises to accelerate all phases of development, rather than simply treating AI coding assistances as autocomplete tools.

This approach has a potential impact that goes beyond simply writing code faster. The realization that application development is more complex will help mature the vibe programming paradigm so it can be more applicable to stable enterprise software development. This move is important, even though it’s early days for SWE-1. OpenAI’s acquisition of Windsurf could make the new models even more valuable, as they will be able to interact with the larger research and development resources.

Tech leaders should consider what part of their development workflow can benefit from AI assistance, beyond code generation. SWE-1 could be more beneficial to teams that spend a lot of time on code reviews and debugging, or managing technical debt.