Alloy vs SWE-2: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Alloy and SWE-2 — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Alloy
Alloy
Create pixel-perfect, interactive prototypes by capturing your real product pages across desktop and mobile.
Key features
- Instant Browser Capture: A browser extension captures live product pages and UI state instantly to create prototypes that mirror the real product’s visuals and layout.
- Pixel-Perfect Prototypes: Builds lifelike, pixel-accurate prototypes that preserve styling and layout for realistic demos and usability testing.
- Interactive Behavior: Supports interactive prototypes with realistic navigation and interactions so stakeholders can experience flows like the real product.
- Cross-Platform Clients: Desktop (macOS, Windows), web, iOS and Android apps allow capturing, viewing, and testing prototypes across devices.
- AI-Powered Prototyping: Uses AI to accelerate prototype generation and streamline the process of converting captured pages into interactive mockups.
- Real-Time Mobile Collaboration: Mobile app features enable real-time communication and synchronization between devices for field teams and device testing.
- Sharing & Team Workflows: Tools to share prototypes with teammates and customers for feedback, demos, and user testing with minimal setup.
- Quick Start Guides: Step-by-step documentation and guides to get started quickly, including capturing pages and building shareable prototypes.
- Instantly capture real product pages from the browser via a browser extension
- Generate lifelike, interactive prototypes that mirror the real product UI
- Cross-platform apps: macOS, Windows, Web, iOS and Android
- AI-powered assistance for rapid prototyping (public content references AI-powered prototyping)
- Share prototypes with teams and customers for feedback and demos
- Alloy Mobile for real-time communication between devices/field users
Best for
- Rapid UX Validation: Capture a live web page and convert it into a clickable prototype to run usability tests with users within hours.
- Stakeholder Demos: Produce pixel-perfect interactive demos from the actual product to show realistic flows to customers or executives.
- Cross-Device Testing: Use desktop and mobile clients to test interactions and layouts across platforms and replicate real-device behavior.
- Field Collaboration: Equip field teams with Alloy Mobile to communicate in real time between devices and validate device-specific workflows.
- Design Iteration: Quickly capture current product screens, iterate on interactions, and share updated prototypes for fast feedback cycles.
- Pre-Release QA: Create prototypes from the production UI to validate edge-case interactions and flows before shipping changes to users.
- Design teams creating high-fidelity prototypes that match the live product for usability testing
- Product teams demonstrating realistic product flows to stakeholders and customers
- Marketing and sales teams preparing interactive demos that reflect current product UI
- Field teams using Alloy Mobile for real-time device-to-device communication during installations or on-site workflows
SWE-2
Cognition
Cognition's coding model that scores 50.0% on FrontierCode 1.1 Main at 64% lower cost than comparable frontier models.
Key features
- Pareto-Frontier Cost Efficiency: Matches GPT-5.6 Sol and Fable 5/5.1 on coding benchmarks at a fraction of their price and comes within a few points of GPT-6 Astra at roughly a quarter of the cost.
- Single-Run Multi-Effort RL: A reinforcement learning algorithm trains all reasoning-effort levels in one run, applying a per-level linear cost penalty derived from the base model's local frontier slope.
- Focused Codebase Exploration: Stronger engineering judgment lets the model decide which parts of a repository matter, cutting mean steps per run from 127 to 53 at medium effort.
- Selectable Effort Levels: Ships medium, high and max reasoning settings so teams can trade additional steps and cost for accuracy on harder tasks.
- End-to-End Test Writing: Produces tests that validate an implementation end to end, catching regressions and edge cases more reliably than previous SWE models.
- Resourceful Task Recovery: When an expected route is blocked — an unavailable MCP integration, for example — it finds an alternative path to the same answer within the user's stated boundaries.
- Efficient Training and Serving Stack: NVFP4/FP8 kernels, quantization-aware training and an online draft model cut memory use and train-inference mismatch despite nearly 3x the base parameters of SWE-1.7.
- Hardened Verifier Flywheel: Triples the number of RL environments, adds instruction-following overlays, and uses earlier SWE-2 checkpoints to iteratively strengthen verifiers.
Best for
- Agentic Software Engineering: Powering Devin sessions that plan, edit, build and test changes across a real repository with minimal supervision.
- Cost-Sensitive Coding at Scale: Teams running large volumes of automated coding tasks pick a model that holds frontier-adjacent accuracy at a materially lower per-task cost.
- Terminal and Tooling Workflows: Strong Terminal-Bench results suit tasks driven through shell commands, build systems and command-line tooling.
- Regression Test Generation: Generating end-to-end tests for existing implementations to catch edge cases before a release.
- Effort-Tiered Task Routing: Routing simple tickets to medium effort and hard migrations to high or max effort within the same model deployment.
- Benchmark and Model Evaluation: Engineering leaders compare coding model options on published FrontierCode, DeepSWE and Terminal-Bench numbers alongside cost.