The landscape of AI-powered coding assistants shifted dramatically in early 2025 with Windsurf's SWE-1.5 (Software Engineering 1.5) model. Combining frontier-level intelligence with unprecedented 950 tokens-per-second generation speed, SWE-1.5 delivers a practical coding assistant that understands agency workflows while maintaining the responsiveness developers demand for real-time collaboration.
Unlike traditional models that force developers to choose between speed and intelligence, SWE-1.5 achieves both through specialized training on software engineering tasks and deployment on Cerebras's WSE infrastructure. For digital agencies managing multiple client projects with tight deadlines, this 14x speed advantage over Claude Sonnet 4.5 compounds into hours saved daily—transforming AI coding assistance from a novelty into a genuine productivity multiplier.
Why This Matters: For agencies, the difference between waiting 30 seconds for code suggestions and getting them in under 5 seconds eliminates context-switching tax. When you're debugging client code on a Friday afternoon or iterating through design revisions during a discovery call, that 25-second reduction compounds into maintained flow state and preserved momentum.
Key Takeaways
- 14x Speed Advantage: Windsurf's SWE-1.5 delivers 950 tokens/second, outpacing Claude Sonnet 4.5 by 14x and completing tasks in under 5 seconds.
- Reinforcement Learning Excellence: Trained with RLHF on real coding workflows, SWE-1.5 adapts to agency-specific patterns and improves through continuous feedback.
- Cerebras WSE Infrastructure: Powered by Cerebras Wafer Scale Engine chips with 900,000 AI cores, enabling real-time code generation at unprecedented scale.
- Multi-Modal Intelligence: Handles code, documentation, APIs, and UI simultaneously with context-aware suggestions across all project layers.
- Agency-Optimized Applications: From client onboarding (75% faster) to campaign deployment (3x speed), SWE-1.5 transforms every stage of digital marketing workflows.
What is SWE-1.5?
Windsurf's SWE-1.5 (Software Engineering 1.5) represents a breakthrough in AI-powered code generation, combining frontier intelligence with unprecedented speed to deliver a practical coding assistant that understands agency workflows. Unlike traditional coding models that prioritize raw intelligence over responsiveness, SWE-1.5 achieves the rare combination of both.
At its core, it's a specialized large language model trained specifically on software engineering tasks, with deep optimization for the iterative, collaborative workflows that define modern digital agencies. The model's architecture leverages a mixture-of-experts (MoE) approach, activating only the relevant specialized sub-networks for each task. This selective computation enables SWE-1.5 to maintain frontier-level intelligence while generating code at 950 tokens per second—13 times faster than Claude Sonnet 4.5 and 6 times faster than Haiku.
For agencies, this means the difference between waiting 30 seconds for a code suggestion and getting it in under 5 seconds. When you're debugging client code at 4 PM on a Friday, or iterating through design revisions during a discovery call, that 25-second reduction compounds into hours saved across every project.
Model Architecture Highlights
- Mixture-of-Experts (MoE): Activates specialized sub-networks for each coding task
- Context Window: 128K tokens with full-file awareness and multi-file reasoning
- Multi-Modal: Handles code, documentation, APIs, UI, and terminal simultaneously
- RLHF Training: Reinforcement learning from human feedback on real agency workflows
Codemaps: AI-Powered Codebase Visualization
One of SWE-1.5's standout capabilities is powering Windsurf's Codemaps—a revolutionary feature that creates hierarchical maps of your codebase, showing how components actually work together rather than just documenting individual functions.
How Codemaps Work
Unlike traditional documentation that describes symbols in isolation, Codemaps visualize execution order and component relationships across your entire project. A specialized AI agent explores your repository, identifies relevant files and functions, then generates interactive hierarchical visualizations that reveal your codebase's actual runtime behavior.
Codemaps Key Capabilities:
- Interactive Navigation: Click any node to jump directly to corresponding files and functions in your codebase, eliminating manual file searching
- AI-Generated Maps: Automatic exploration and visualization based on navigation history, custom prompts, or Cascade conversation context
- Cascade Integration: Reference Codemaps in conversations using @-mention syntax for context-aware code discussions with SWE-1.5
- Team Collaboration: Share Codemaps as browser-accessible links with teammates, enabling collaborative codebase understanding without local repository access
Agency Use Cases
For digital agencies, Codemaps dramatically accelerates client onboarding and technical discovery:
New Developer Onboarding
Reduce the typical 1-2 week ramp-up time for new developers joining a project. Instead of manually tracing execution paths through dozens of unfamiliar files, developers see the complete system flow at a glance and can dive into specific components as needed.
Client Codebase Audits
Generate comprehensive architecture visualizations during technical discovery phases. Present Codemaps to clients to demonstrate your understanding of their system and identify optimization opportunities before quoting project timelines.
Legacy System Documentation
Create living documentation for poorly-documented legacy codebases. Codemaps automatically reveal how undocumented systems actually work, making refactoring and feature additions significantly safer and faster.
Speed Meets Intelligence
SWE-1.5's 950 tokens per second generation speed isn't just a benchmark—it's a paradigm shift in how agencies interact with AI coding assistants.
The Speed Benchmark Revolution
Traditional frontier models like Claude Sonnet 4.5 and GPT-5 High (latest) prioritize intelligence over responsiveness, averaging 70-80 tokens per second. This creates a "wait-and-see" workflow where developers request code, switch contexts during the 20-30 second wait, then return to review and iterate.
SWE-1.5 eliminates this context-switching tax. At 950 tokens per second, most coding tasks complete in under 5 seconds. Developers stay in flow state, iterating rapidly without the cognitive overhead of task switching.
Traditional Models (70-80 tok/s):
- 20-30 second wait per task
- Context switching overhead
- Developer flow disruption
- Reduced iteration speed
SWE-1.5 (950 tok/s):
- <5 second completion
- Maintained focus
- Sustained flow state
- Rapid iteration cycles
Compound Productivity Gains
The speed advantage compounds throughout a typical agency project. Consider a standard WordPress customization project requiring 50 AI-assisted code generations:
- Claude Sonnet 4.5: 50 tasks × 25 seconds = 20.8 minutes of waiting
- SWE-1.5: 50 tasks × 5 seconds = 4.2 minutes of waiting
- Time Saved: 16.6 minutes per project (80% reduction)
Across 20 projects per month, that's 5.5 hours saved—equivalent to nearly a full workday of recovered developer time every month, all from eliminating waiting.
Intelligence Without Compromise
Speed would be meaningless without accuracy. SWE-1.5 maintains frontier-level intelligence through its specialized training on software engineering tasks. In SWE-Bench evaluations—the industry standard for measuring coding model performance—SWE-1.5 achieves comparable accuracy to Claude Sonnet 4.5 while operating 14x faster.
This "best of both worlds" positioning makes SWE-1.5 uniquely suited for agency work, where developers need both correct suggestions and rapid iteration to maintain momentum during client calls, debugging sessions, and sprint deadlines.
Performance Benchmarks: Speed vs Accuracy
SWE-1.5 achieves the rare combination of near-frontier accuracy with unprecedented speed, as demonstrated in SWE-Bench Pro—the industry standard for evaluating AI coding models on real-world GitHub issues.
SWE-Bench Pro Results
Across 731 agentic coding tasks spanning 41 repositories, SWE-1.5 delivers exceptional performance while maintaining the fastest generation speed of any frontier model:
| Model | Accuracy Score | Generation Speed |
|---|---|---|
| SWE-1.5 | 40.08% | 950 tok/s |
| Claude Sonnet 4.5 | 43.60% | 69 tok/s |
| GPT-5 High | 36.30% | 43 tok/s |
Key Insight: While SWE-1.5 trails Sonnet 4.5 by 3.5 percentage points in accuracy (40.08% vs 43.60%), it delivers this performance at 13.8x faster speed (950 tok/s vs 69 tok/s)—a speed advantage that dramatically outweighs the marginal accuracy difference for most agency workflows.
Performance Across Different Agent Harnesses
SWE-1.5's performance varies depending on the agent harness used to orchestrate its coding workflows. Windsurf's custom Cascade harness achieves the highest scores:
| Harness | SWE-Bench Pro Score |
|---|---|
| Cascade (Windsurf) | 40.08% |
| SWE-agent | 34.47% |
| Claude Code | 29.00% |
The 11.1 percentage point advantage of Cascade over Claude Code demonstrates that harness design significantly impacts model performance. Windsurf's Cascade harness was specifically optimized during SWE-1.5's reinforcement learning training, creating a tightly integrated system where model and orchestration layer work in harmony.
The Reinforcement Learning Edge
SWE-1.5's reinforcement learning training creates a model that doesn't just generate code—it understands the workflows, patterns, and priorities of professional software engineering.
RLHF for Software Engineering
Reinforcement Learning from Human Feedback (RLHF) trains SWE-1.5 on real-world coding interactions. Professional developers review thousands of code generations, marking accurate, maintainable, and idiomatic solutions as preferred. The model learns not just syntax, but the subtle judgment calls that separate junior-level code from senior-level engineering.
This RLHF training manifests in three critical ways:
- Contextual Awareness: SWE-1.5 understands when to prioritize performance over readability, when to add defensive error handling, and when to suggest refactoring versus quick fixes.
- Framework Fluency: The model internalizes best practices for popular frameworks (React, Next.js, WordPress, Shopify) and suggests patterns that align with each framework's conventions.
- Agency Workflow Patterns: SWE-1.5 recognizes common agency scenarios—client customizations, theme modifications, plugin integration—and tailors suggestions to these repeatable workflows.
Real-World RLHF Impact
Scenario: WordPress Custom Post Type Creation
Pre-RLHF Model: Generates basic register_post_type() call with minimal options.
SWE-1.5 (RLHF-trained): Generates complete CPT with hierarchical structure, REST API support, Gutenberg editor integration, and rewrite rules—matching professional WordPress development standards.
Real Agency Applications
SWE-1.5 transforms every stage of digital agency workflows, from initial client discovery to ongoing maintenance and optimization.
1. Client Website Audits & Onboarding
75% faster initial codebase analysis and technical recommendations
Before: Manual code review of new client websites took 4-6 hours, involving file-by-file inspection, dependency analysis, and security audits. Technical onboarding reports required another 2 hours to compile.
After: SWE-1.5 analyzes entire codebases in 15-20 minutes, identifying technical debt, security vulnerabilities, performance bottlenecks, and optimization opportunities. Auto-generated onboarding reports include specific file locations, severity ratings, and recommended fixes.
Time Saved: 4-5 hours per client onboarding | Impact: Faster client turnaround, more detailed technical assessments
2. Campaign Landing Page Development
3x faster landing page creation with conversion optimization built-in
Before: Custom landing pages required 8-12 hours of development time, including responsive design, form integration, tracking setup, and A/B testing configuration.
After: SWE-1.5 generates production-ready landing pages in 2-3 hours, with responsive layouts, integrated analytics, form validation, and pre-configured A/B testing variants. The model suggests conversion optimization patterns based on industry benchmarks.
Time Saved: 6-9 hours per landing page | Impact: Faster campaign launches, more design iterations, higher conversion rates
3. WordPress Theme Customization
60% reduction in theme modification time with style guide compliance
Before: Client-specific theme modifications took 10-15 hours, involving child theme setup, template overrides, custom CSS, and PHP functions. Ensuring consistency with client brand guidelines required additional review cycles.
After: SWE-1.5 generates child themes with all required customizations in 4-6 hours. The model references uploaded brand style guides to automatically match colors, typography, and spacing. Generated code follows WordPress coding standards and includes inline documentation.
Time Saved: 6-9 hours per theme project | Impact: Consistent brand implementation, cleaner code, fewer revision cycles
4. E-Commerce Integration & Automation
4x faster checkout customization and payment gateway integration
Before: Custom checkout flows and payment gateway integrations required 15-20 hours, involving API documentation review, error handling, webhook setup, and extensive testing.
After: SWE-1.5 generates complete payment integrations in 4-5 hours, including error handling, webhook processors, receipt generation, and automated testing suites. The model references current API documentation to ensure compatibility with the latest gateway versions.
Time Saved: 11-15 hours per integration | Impact: Faster e-commerce launches, reduced integration bugs, improved payment reliability
5. API Integration & Data Synchronization
70% faster third-party API connections with automatic error handling
Before: Integrating CRM systems, email platforms, or analytics tools required 6-8 hours per service, involving authentication setup, data mapping, rate limiting, and error recovery logic.
After: SWE-1.5 generates complete API integrations in 2-3 hours, with OAuth flows, automatic retry logic, rate limit handling, and data transformation pipelines. The model suggests optimal caching strategies to minimize API calls.
Time Saved: 4-5 hours per integration | Impact: More reliable data sync, reduced API costs, faster client onboarding
6. Performance Optimization & Debugging
85% faster identification and resolution of performance bottlenecks
Before: Diagnosing slow page loads or database performance issues required 3-5 hours of profiling, log analysis, and iterative testing to identify root causes.
After: SWE-1.5 analyzes performance traces, database queries, and server logs in 20-30 minutes, pinpointing bottlenecks with specific line numbers and suggested optimizations. The model generates optimized code alternatives and benchmarks expected improvements.
Time Saved: 2.5-4.5 hours per optimization project | Impact: Faster issue resolution, improved site performance, better client satisfaction
Cerebras Partnership & Infrastructure
SWE-1.5's unprecedented speed is powered by Cerebras's Wafer Scale Engine (WSE) infrastructure, delivering significantly higher AI throughput compared to traditional GPU clusters.
Cerebras Wafer Scale Engine Architecture
The Cerebras WSE represents a generational leap in AI inference hardware. Unlike traditional GPUs that excel at training but struggle with real-time inference, the WSE is purpose-built for low-latency generation at massive scale.
Key architectural advantages include:
- Wafer-Scale Design: The WSE-3 is a single chip covering an entire wafer (46,225 mm²), eliminating data transfer overhead between components and reducing latency significantly.
- On-Chip SRAM: 44GB of on-chip SRAM memory with ultra-low latency enables the entire SWE-1.5 model to remain memory-resident, eliminating model loading delays.
- Massive Parallelism: 900,000 AI cores deliver 125 petaflops of AI compute, optimized for transformer inference operations.
Cerebras Inference Platform
Windsurf partners with Cerebras to deploy SWE-1.5 on their inference-optimized platform. Cerebras manages WSE systems specifically tuned for low-latency code generation, with automatic load balancing and geographic distribution to minimize latency for global agency teams.
The platform's real-time monitoring adjusts inference parameters based on request patterns, prioritizing low-latency responses during peak agency hours (9 AM - 6 PM in each timezone) and shifting to batch processing during off-peak hours for cost efficiency.
Cost Efficiency for Agencies
Despite its speed advantage, SWE-1.5 maintains competitive pricing. The WSE's inference efficiency means agencies pay similar per-token costs to Claude Sonnet 4.5 ($12/M tokens vs $15/M), but complete tasks 14x faster—effectively delivering 14x more value per dollar spent.
ROI Calculation for Agencies
For a typical agency using 1 million tokens per month:
- API Cost: $12 (SWE-1.5) vs $15 (Claude Sonnet 4.5)
- Speed Advantage: 14x faster means 14x more tasks completed
- Effective Value: $12 delivers ~$195 worth of Sonnet-equivalent work
- Time Saved: 5-10 developer hours monthly = $500-1,000 in labor costs
- ROI: Positive from week 1, compounds monthly
SWE-1.5 vs Competition
How Windsurf SWE-1.5 compares to leading AI coding assistants across speed, intelligence, and agency-specific features.
| Feature | Windsurf SWE-1.5 | Cursor Composer | Claude Sonnet 4.5 | GPT-5 High |
|---|---|---|---|---|
| Generation Speed | 950 tok/s | 200 tok/s | 69 tok/s | 43 tok/s |
| Typical Completion Time | <5 seconds | 12-15 seconds | 25-30 seconds | 30-35 seconds |
| SWE-Bench Pro Accuracy | 40.08% | 52.1% | 43.60% | 36.30% |
| Context Window | 128K tokens | 200K tokens | 200K tokens | 128K tokens |
| Multi-File Reasoning | Excellent | Excellent | Good | Good |
| Framework-Specific Training | WordPress, React, Next.js | React, Next.js | General purpose | General purpose |
| RLHF on Agency Workflows | Yes | Yes | No | No |
| Browser Testing Integration | Playwright, Puppeteer | Full browser control | No | No |
| Pricing (per 1M tokens) | $12 | $10 | $15 | $10 |
| Best For | Speed-critical workflows | Full-stack applications | General development | General development |
Competitive Positioning
Speed Leader: SWE-1.5's 950 tok/s makes it the fastest frontier-level coding model, ideal for agencies where rapid iteration drives client satisfaction.
Intelligence Trade-off: Behind Cursor Composer (52.1% vs 40.08% on SWE-Bench Pro), but the 4.75x speed advantage often outweighs the accuracy gap for most agency tasks.
Framework Specialization: Unlike general models (Claude, GPT-5), SWE-1.5 includes dedicated training on WordPress, React, and Next.js—the core of agency client work.
Implementation for Agencies
Practical guidance for integrating SWE-1.5 into agency development workflows, from pilot projects to team-wide deployment.
Getting Started
Windsurf offers SWE-1.5 access through three integration paths:
Windsurf IDE
Native editor with SWE-1.5 built-in. Ideal for agencies starting fresh or willing to switch IDEs for maximum integration depth.
VS Code Extension
Windsurf's VS Code extension brings SWE-1.5 to your existing development environment. Recommended for teams with established VS Code workflows and custom extensions.
API Access
Direct API integration for custom tooling, CI/CD pipelines, or proprietary development platforms. Requires developer resources for integration.
Pilot Project Selection
Start with a pilot project that showcases SWE-1.5's strengths while minimizing risk. Ideal characteristics include:
- Repetitive Code Patterns: Projects with similar structures (e.g., WordPress plugins, REST APIs, CRUD applications) where SWE-1.5's pattern recognition excels.
- Tight Deadlines: Time-sensitive projects where SWE-1.5's speed delivers immediate, measurable value.
- Junior Developer Workload: Tasks typically assigned to junior developers, allowing you to evaluate SWE-1.5 as a productivity multiplier for your team's less experienced members.
Team Training
SWE-1.5 works best when developers understand how to prompt effectively and review generated code critically:
Effective Prompting Strategies
❌ Vague Prompt: "Create a contact form"
✅ Specific Prompt: "Create a React contact form with TypeScript, using React Hook Form for validation, featuring name, email, message fields with appropriate validation rules, and integrate with our /api/contact endpoint using fetch with error handling. Style with Tailwind CSS to match our design system (zinc color scheme, rounded-lg borders)."
Code Review Process
Treat SWE-1.5 generated code like junior developer submissions—assume good intentions but verify rigorously. Focus reviews on:
- Security: Input validation, SQL injection prevention, XSS protection, authentication checks
- Performance: Database query efficiency, unnecessary API calls, memory leaks
- Maintainability: Code clarity, documentation, adherence to team coding standards
- Edge Cases: Error handling, null checks, boundary conditions
Measuring ROI
Track these metrics to quantify SWE-1.5's impact on agency productivity:
- Development Hours: Compare project completion times before/after SWE-1.5 adoption
- Code Review Time: Measure time spent reviewing AI-generated vs human-written code
- Bug Rates: Track production bugs in AI-assisted vs traditional development
- Developer Satisfaction: Survey team members on productivity, cognitive load, and job satisfaction
Agencies typically see 30-50% productivity gains within 3 months of SWE-1.5 adoption, with experienced developers benefiting most from rapid prototyping and boilerplate elimination.
Enterprise Considerations
Security, compliance, and governance considerations for agencies deploying SWE-1.5 across client projects.
Data Privacy & Security
Client code confidentiality is paramount for agencies. Windsurf implements several safeguards to protect proprietary client codebases:
- Zero-Retention Mode: Enterprise plans include zero data retention, where submitted code is processed in memory and immediately discarded after generation—never stored, logged, or used for model training.
- On-Premise Deployment: For agencies with strict data residency requirements, Windsurf offers self-hosted SWE-1.5 deployments on agency infrastructure (requires compatible GPU hardware such as NVIDIA H100).
- Client Isolation: Enterprise deployments support per-client model instances, ensuring one client's code never influences suggestions for another client's projects.
Compliance
Windsurf maintains compliance certifications relevant to agency work:
- SOC 2 Type II: Annual audits verify security controls for customer data protection
- GDPR Compliance: Data processing agreements available for EU-based agencies and clients
- HIPAA: Business Associate Agreements (BAA) available for healthcare client projects
License Compliance
SWE-1.5 is trained on open-source code, raising questions about license obligations in generated code. Windsurf's approach:
- License Detection: SWE-1.5 identifies code patterns that closely match specific open-source projects and flags potential license obligations in generated code comments.
- Indemnification: Enterprise plans include legal indemnification for copyright claims related to AI-generated code (subject to standard limitations).
- Originality Scoring: Each code generation includes an "originality score" estimating how closely it matches training data, helping developers make informed decisions about code usage.
Vendor Lock-In
Agencies rightfully worry about dependence on AI coding assistants. Windsurf mitigates lock-in through several design decisions:
- Standard Output: SWE-1.5 generates standard, framework-idiomatic code—no proprietary APIs or Windsurf-specific patterns. If you later switch to a different AI assistant, your codebase remains portable.
- Multi-Model Support: Windsurf IDE supports fallback to Claude, GPT-5, or other models if SWE-1.5 is unavailable or unsuitable for specific tasks.
- Open API Access: API integration allows you to switch to alternative models without changing your development workflows or tooling.
Cost Management
For agencies managing multiple client projects, controlling SWE-1.5 costs is essential:
- Per-Project Budgets: Set token limits for each client project to prevent overuse
- Developer Quotas: Allocate monthly token quotas to team members based on role and project load
- Usage Analytics: Monitor which projects and developers consume the most tokens to identify optimization opportunities
- Caching: Configure aggressive caching for repetitive code patterns to reduce API calls
Typical Agency Usage & Cost:
- Monthly Usage: 500K-2M tokens per month ($6-24), depending on team size and project complexity.
- Developer Time Saved: $3,000-10,000 in developer time saved monthly for a 5-person development team.
- Net Savings: $2,976-9,976 per month
Conclusion
Windsurf SWE-1.5 represents a fundamental evolution in AI-assisted development for digital agencies. By achieving frontier-level coding intelligence at 14x faster generation speed than Claude Sonnet 4.5, it makes AI pair programming practical for interactive development workflows where responsiveness matters as much as accuracy.
The combination of reinforcement learning optimization, mixture-of-experts architecture, and Cerebras WSE infrastructure creates a development tool where AI agents genuinely augment developer productivity rather than simply generating code suggestions. For agencies managing multiple client projects with tight deadlines and demanding quality standards, SWE-1.5's speed advantage compounds into measurable ROI—typically 30-50% productivity gains within three months.
As reinforcement learning training continues to improve the model's understanding of agency-specific workflows, early adopters position themselves to benefit from continuous model improvements while competitors struggle with slower, less specialized alternatives.
Frequently Asked Questions
How fast is Windsurf SWE-1.5 compared to Claude Sonnet 4.5?
SWE-1.5 generates code at 950 tokens per second, making it approximately 14x faster than Claude Sonnet 4.5 (69 tok/s). For a typical code generation task (300 tokens), SWE-1.5 completes in under 5 seconds while Sonnet requires 25-30 seconds. This speed advantage compounds throughout projects, saving agencies hours of waiting time per week.
Does SWE-1.5 match Claude Sonnet's code quality?
SWE-1.5 achieves 40.08% accuracy on SWE-Bench Pro (industry standard coding benchmark), behind Claude Sonnet 4.5 at 43.60%. For most agency tasks—WordPress customizations, React components, API integrations—this 3.5 percentage point difference is negligible and far outweighed by SWE-1.5's 14x speed advantage. Both models produce production-quality code with proper review.
What is reinforcement learning (RLHF) and how does it benefit agencies?
Reinforcement Learning from Human Feedback (RLHF) trains SWE-1.5 on real developer interactions, learning which code patterns are preferred by professional engineers. For agencies, this means SWE-1.5 understands framework conventions (WordPress coding standards, React best practices), recognizes common client customization patterns, and suggests maintainable code that matches professional quality standards—not just syntactically correct code.
How much does SWE-1.5 cost compared to other coding models?
SWE-1.5 costs approximately $12 per 1 million tokens, comparable to Claude Sonnet 4.5 ($15/M) and GPT-5 High ($10/M). For a typical agency using 1 million tokens per month, that's $12 in API costs. Given SWE-1.5's 13.8x speed advantage over Sonnet (950 tok/s vs 69 tok/s), you complete 13.8x more tasks per dollar—effectively delivering $207 worth of Sonnet-equivalent work for $12. The time saved (5-10 developer hours monthly) is worth $500-1,000 in labor costs.
Is client code used to train SWE-1.5?
No. Windsurf's Enterprise plans include zero data retention, where submitted code is processed in memory and immediately discarded after generation. Your client code is never stored, logged, or used for model training. For agencies with strict data residency requirements, Windsurf offers self-hosted deployments where code never leaves your infrastructure.
Can I use SWE-1.5 with my existing VS Code setup?
Yes. Windsurf provides a VS Code extension that integrates SWE-1.5 into your existing development environment. You can continue using your current extensions, themes, and workflows while accessing SWE-1.5's speed. Alternatively, Windsurf IDE offers a native editor with deeper SWE-1.5 integration for teams willing to switch environments.
What frameworks does SWE-1.5 specialize in?
SWE-1.5 includes specialized training on WordPress, React, Next.js, and Node.js—the core frameworks for most agency client work. This means the model understands WordPress coding standards (sanitization, nonce verification, REST API conventions), React patterns (hooks, component composition), and Next.js architecture (App Router, Server Components). General-purpose models like Claude lack this framework-specific expertise.
How long does it take agencies to see ROI from SWE-1.5?
Most agencies see immediate productivity gains in week 1 (10-15% faster development), reaching 30-50% productivity improvements by month 3 once teams master effective prompting and code review workflows. A 5-person development team typically saves 20-40 hours monthly ($2,000-4,000 in labor costs) while spending $50-150 on API usage. ROI is positive from week 1 and compounds as teams identify more use cases.
Does SWE-1.5 work for debugging and optimization, or just new code generation?
SWE-1.5 excels at debugging, performance optimization, and code analysis—not just new code generation. It can analyze slow database queries and suggest indexing improvements, identify performance bottlenecks from profiling data, review codebases for security vulnerabilities, and debug complex errors by analyzing stack traces and application logs. Many agencies find debugging support even more valuable than code generation.
What hardware powers SWE-1.5's speed?
SWE-1.5 runs on Cerebras's inference platform powered by their proprietary Wafer Scale Engine (WSE) chips. The WSE architecture features 900,000 AI cores with 44GB of on-chip SRAM memory and massive parallel processing capabilities optimized for transformer inference. This specialized hardware delivers significantly higher AI throughput than traditional GPU clusters, enabling SWE-1.5's 950 tokens/second generation speed while maintaining cost efficiency.
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