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So far, vibe coding platforms have largely relied on current giant language fashions (LLMs) to assist write code.
Nevertheless, writing code is just one of many various duties builders have to carry out to construct a full enterprise-grade manufacturing platform. Different duties within the full software program engineering workflow require utilizing completely different instruments to assist evaluation, commit and preserve code over time. It’s a problem Windsurf (previously Codeium) is taking up with a collection of recent frontier AI fashions it calls SWE-1 (software program engineer 1) as a part of the corporate’s Wave 9 replace.
The information comes as Windsurf is reportedly within the midst of being acquired by AI chief OpenAI for as a lot as $3 billion. That deal has not but formally closed, and Windsurf shouldn’t be presently publicly commenting on the deal.
SWE-1 is a household of frontier-class AI fashions particularly designed to speed up all the software program engineering course of. Not like general-purpose AI fashions which were tailored for coding duties, the SWE-1 household was constructed to deal with the complete spectrum of software program engineering actions.
The brand new fashions goal to assist builders by way of a number of surfaces, incomplete work states and long-running duties that characterize real-world software program improvement. Out there instantly to Windsurf customers, SWE-1 marks the corporate’s entry into frontier mannequin improvement with efficiency aggressive to established basis fashions, however with a give attention to software program engineering workflows.
“Our predominant aim right here is to speed up all software program engineering by 99%,” Anshul Ramachandran, head of product and technique at Windsurf, instructed VentureBeat.
Enterprise builders want extra than simply coding-capable fashions
The core innovation behind SWE-1 is Windsurf’s recognition that coding represents solely a fraction of what software program engineers truly do.
This method addresses a important limitation in present AI coding LLMs. Many various fashions can be utilized at this time to jot down utility code, together with OpenAI’s GPT-4.1, Anthropic Claude 3.7 and Google’s Gemini 2.5 Professional I/O version.
Windsurf has a modular interface that may allow use of a number of completely different fashions. Ramachandran defined that Windsurf customers have given the corporate suggestions that current coding fashions are inclined to do properly with consumer steerage, however over time are inclined to miss issues.
This limitation stems from a elementary distinction in job construction. Whereas code technology is commonly a single-shot job, actual software program engineering entails navigating a number of instruments, working with incomplete code and sustaining context throughout long-running tasks.
The SWE-1 household: Function-built for various engineering duties
Slightly than making a one-size-fits-all answer, Windsurf has developed three specialised fashions:
- SWE-1: Full-size mannequin designed for superior reasoning and gear use, accessible to all paid customers.
- SWE-1-lite: A smaller however highly effective mannequin changing Windsurf’s current Cascade Base, accessible to all customers (each free and paid).
- SWE-1-mini: A light-weight mannequin powering passive code predictions in Windsurf Tab, limitless for all customers.
The SWE fashions have been constructed by way of an intensive in-house coaching course of targeted particularly on software program engineering duties. Ramachandran mentioned that the corporate used a brand new information mannequin with sequential steps for coaching.
Efficiency benchmarks: How SWE-1 compares
Whereas SWE-1 isn’t positioned to exchange basis fashions from main labs, Windsurf claims it achieves frontier-class efficiency particularly for software program engineering duties. The corporate experiences that it considerably outperforms mid-sized basis fashions and open-weight fashions.
Nevertheless, Windsurf is cautious to not oversell these preliminary outcomes.
“Even our benchmark exhibits it’s not objectively higher than all the opposite fashions,” Ramachandran acknowledged.
As an alternative, the aim is to place SWE-1 as step one towards purpose-built fashions that may ultimately surpass general-purpose ones for particular engineering duties — and probably at a decrease value.
The technical edge: Movement consciousness and shared timelines
What makes Windsurf’s method technically distinctive is its implementation of the circulation consciousness idea.
The fundamental concept is {that a} circulation of steps have to occur as a part of enterprise improvement. Slightly than simply writing code for one particular step, circulation consciousness is about being conscious of the broader context.
Movement consciousness is centered on making a shared timeline of actions between people and AI in software program improvement. The core concept is to progressively switch duties from human to AI by understanding the place AI can most successfully help.
This method creates a steady enchancment loop for the fashions.
“As we proceed to enhance the fashions, extra of the steps in that shared timeline will likely be flipped from human to AI,” mentioned Ramachandran. “The AI will be capable to do extra issues that the human needed to do earlier than as a result of the AI wasn’t proper.”
What this implies for technical decision-makers
For enterprises constructing or sustaining software program, SWE-1 represents an necessary evolution in AI-assisted improvement. Slightly than treating AI coding assistants as merely autocomplete instruments, this method guarantees to speed up all the improvement lifecycle.
The potential impression extends past simply writing code extra shortly. The popularity that utility improvement is extra concerned will assist mature the vibe coding paradigm to be extra relevant for secure enterprise software program improvement.
Whereas it’s nonetheless early days for SWE-1, this transfer is necessary. If and when OpenAI completes the acquisition of Windsurf, the brand new fashions may develop into much more necessary as they intersect with the bigger mannequin analysis and improvement sources that may develop into accessible.
Technical leaders ought to think about how a lot of their improvement workflow may gain advantage from AI help past mere code technology. Groups spending vital time on code evaluations, debugging and managing technical debt would possibly see extra substantial advantages from instruments like SWE-1 than these primarily targeted on producing new code.
