Supermaven ranks higher at 71/100 vs AI Pundit Magic - Design to Code | Figma to Code at 33/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | AI Pundit Magic - Design to Code | Figma to Code | Supermaven |
|---|---|---|
| Type | Extension | Extension |
| UnfragileRank | 33/100 | 71/100 |
| Adoption |
| 0 |
| 1 |
| Quality | 0 | 1 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 12 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Accepts Figma design URLs as input and generates production-ready React component code with automatic styling and layout implementation. The system parses Figma design structure (layers, constraints, typography, colors) and maps them to selected design system frameworks (Material UI, Ant Design, Chakra UI, Fluent UI), generating JSX with pre-configured component imports and prop structures. Real-time progress updates indicate generation pipeline stages.
Unique: Integrates Figma design parsing with multi-framework code generation in a VS Code extension, allowing developers to target different design system libraries (Material UI, Ant Design, Chakra UI, Fluent UI) from the same design input without leaving the editor. Uses cloud-based AI Pundit Engine for design-to-code transformation rather than client-side processing.
vs alternatives: Supports more design system frameworks than Figma's native code export and maintains design consistency through framework-specific component mapping, but depends on cloud service availability unlike offline tools like Penpot or Framer.
Generates Angular-compatible component code from Figma design URLs with support for Angular Material, NG-Zorro, and PrimeNG component libraries. The system translates Figma design elements into Angular template syntax (HTML with Angular directives) and TypeScript component classes with property bindings and lifecycle hooks, maintaining framework-specific patterns and conventions.
Unique: Extends design-to-code generation to Angular ecosystem with support for three major component libraries (Angular Material, NG-Zorro, PrimeNG), generating both template and component class files with proper TypeScript typing and Angular conventions.
vs alternatives: Provides Angular-specific code generation that Figma's native export lacks, but limited to three component libraries compared to React's broader ecosystem support.
Generates code for the same Figma design across multiple selected frameworks (React + Angular, React + Flutter, etc.) and presents side-by-side comparison of outputs. The system highlights framework-specific differences in component structure, styling approach, and API usage. Generated code for all frameworks can be exported simultaneously, enabling developers to choose the best output or use multiple frameworks in the same project.
Unique: Enables side-by-side code generation and comparison across multiple frameworks from single design input, allowing developers to evaluate framework suitability and export code for multiple platforms simultaneously.
vs alternatives: Provides integrated multi-framework comparison within VS Code, but lacks the visual design preview and interactive testing capabilities of dedicated design-to-code platforms.
Manages project-level configuration for design system and component library selections, allowing developers to set default frameworks, design systems, and component mappings. The system stores configuration in project workspace, enabling consistent code generation across team members and preventing framework selection errors. Configuration can be version-controlled and shared across team repositories.
Unique: Provides project-level configuration management for design system and framework selections, enabling consistent code generation across team members and version-controlled configuration sharing.
vs alternatives: Offers integrated configuration management within VS Code workspace, but lacks the centralized governance and policy enforcement of dedicated design system management platforms.
Generates Flutter widget code and Dart class definitions from Figma design URLs, translating design elements into Flutter-specific widget trees with Material Design or Cupertino styling. The system maps Figma layout constraints to Flutter layout widgets (Column, Row, Stack), converts colors and typography to Flutter Theme properties, and generates stateless/stateful widget scaffolding with proper Dart syntax.
Unique: Extends design-to-code capability to mobile development by generating Dart/Flutter widget code from Figma designs, enabling cross-platform mobile development from single design source without platform-specific design tools.
vs alternatives: Provides Flutter code generation that Figma lacks natively, enabling mobile developers to use Figma as single design source, but lacks integration with Flutter-specific state management and navigation patterns.
Analyzes generated or existing code and automatically inserts comments following language-specific conventions and documentation best practices. The system uses the AI Pundit Engine to understand code intent, function signatures, and logic flow, then generates JSDoc, TSDoc, or Dart doc comments with parameter descriptions, return types, and usage examples. Comments are inserted at appropriate locations (function declarations, complex logic blocks, class definitions) without modifying code logic.
Unique: Integrates AI-driven comment generation into VS Code workflow as part of Pundit Toolbox, automatically inserting language-appropriate documentation comments (JSDoc, TSDoc, Dart doc) without requiring manual documentation writing or external documentation tools.
vs alternatives: Automates documentation generation for generated code in single IDE, but lacks granular control over comment style and format compared to manual documentation or dedicated documentation generators like TypeDoc.
Scans generated or existing code for potential bugs, anti-patterns, and code quality issues using the AI Pundit Engine's analysis capabilities. The system identifies common issues such as missing null checks, unused variables, type mismatches, performance problems, and security vulnerabilities. Results are presented with severity levels and suggested fixes, integrating with VS Code's problem panel for inline diagnostics.
Unique: Provides AI-driven static analysis specifically tuned for generated code, identifying issues that traditional linters miss by understanding code intent and design patterns. Integrates analysis results directly into VS Code's problem panel for seamless developer workflow.
vs alternatives: Complements traditional linters like ESLint by using semantic analysis to detect logic errors and design pattern violations, but lacks the configurability and ecosystem integration of established linting tools.
Generates detailed natural language explanations of code sections, functions, or entire files using the AI Pundit Engine. The system analyzes code structure, logic flow, and dependencies to produce human-readable documentation that explains what code does, why it's structured that way, and how to use it. Explanations can be displayed in hover tooltips, side panels, or exported as markdown documentation.
Unique: Uses AI to generate human-readable explanations of code intent and structure, integrated into VS Code workflow via hover tooltips and side panels. Specifically designed for explaining generated code that may lack clear intent or documentation.
vs alternatives: Provides semantic code explanation beyond syntax highlighting or type information, but lacks the precision and customization of manual documentation or domain-specific documentation generators.
+4 more capabilities
Generates single-line and multi-line code suggestions in real-time as developers type, using semantic indexing of the entire codebase to retrieve relevant type definitions, function signatures, and contextual patterns. The system maintains a 1M token context window (Pro/Team tiers) that enables suggestions informed by distant code definitions and cross-file dependencies, constructed via local codebase semantic search rather than simple token-based recency. Suggestions adapt to detected coding style on Pro/Team tiers through implicit pattern learning from recent edits.
Unique: 1M token context window with codebase-wide semantic indexing enables suggestions informed by distant code definitions and cross-file patterns, versus competitors (Copilot, Tabnine) that typically use fixed context windows (4K-32K tokens) or file-local context. Claimed 250ms latency suggests optimized retrieval pipeline, though indexing mechanism and performance at scale remain undisclosed.
vs alternatives: Larger context window than GitHub Copilot (8K-32K tokens) and faster latency than unnamed competitors (250ms vs 783ms claimed), enabling suggestions on large codebases with minimal typing delay; trade-off is cloud dependency and undisclosed free tier limitations.
Provides a separate chat interface supporting multiple LLM backends (GPT-4o, Claude 3.5 Sonnet, GPT-4, others) for conversational code assistance. Users attach files, reference recent edits, and trigger compiler diagnostic uploads; the system generates diffs and applies code changes directly to the editor. Model selection is per-conversation, and $5/month in credits (included in Pro/Team) covers external model API costs; overage pricing is undisclosed. Hotkey-driven workflow enables rapid context switching between inline completion and chat.
Unique: Multi-model chat interface with per-conversation model selection and integrated diff application, combined with compiler diagnostic auto-upload. Unlike Copilot Chat (single model per tier) or standalone ChatGPT, Supermaven Chat unifies multiple LLM backends in a single hotkey-driven workflow with direct editor integration for change application.
Supermaven scores higher at 71/100 vs AI Pundit Magic - Design to Code | Figma to Code at 33/100. AI Pundit Magic - Design to Code | Figma to Code leads on ecosystem, while Supermaven is stronger on adoption and quality.
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vs alternatives: Supports multiple LLM backends (GPT-4o, Claude 3.5 Sonnet) in one interface with included credits, whereas GitHub Copilot Chat is single-model per tier and requires separate ChatGPT subscription for model switching; trade-off is credit limits and unknown overage pricing.
Supermaven Chat can automatically upload compiler diagnostic messages (errors, warnings) alongside code context to provide error-aware suggestions and fixes. The mechanism is described as 'automatically uploading your code together with compiler diagnostic messages,' but specific language/compiler support and the upload trigger mechanism are undisclosed. This feature is Chat-only and not available in inline completion.
Unique: Automatic compiler diagnostic upload in Chat for error-aware suggestions, versus competitors (Copilot, Tabnine) that require manual error context or have limited diagnostic integration. Supermaven's approach reduces friction but with undisclosed language/compiler support.
vs alternatives: Automatic diagnostic upload reduces manual context-gathering compared to manual copy-paste; trade-off is undisclosed language support and unclear upload trigger mechanism.
Supermaven offers a 30-day free trial of the Pro tier ($10/month), providing full access to 1M token context window, largest model, style adaptation, and $5/month chat credits. No credit card is required to start the trial (implied), and trial conversion to paid is automatic after 30 days unless cancelled. Trial terms and auto-renewal policy are not explicitly detailed.
Unique: 30-day free trial of Pro tier with full feature access (1M context, largest model, chat credits), versus competitors (Copilot 2-month free trial, Tabnine free tier only) with different trial lengths and feature access. Supermaven's approach is generous but with undisclosed auto-renewal terms.
vs alternatives: Full Pro feature access during trial compared to limited free tier; trade-off is undisclosed auto-renewal policy and potential unexpected charges if not cancelled.
Supermaven requires internet connectivity and server-side inference; no offline mode or local inference capability is mentioned or available. All code completion requests are sent to Supermaven's backend servers for processing, and responses are returned over the network. This creates a hard dependency on network connectivity and Supermaven's service availability; if the service is down or network is unavailable, code completion is not available.
Unique: Supermaven has no offline mode or local inference capability; all processing is server-side. GitHub Copilot also requires server-side inference, but Tabnine offers local inference options for some use cases. Supermaven's lack of offline capability is a significant limitation for developers with connectivity constraints.
vs alternatives: Supermaven's server-side-only approach is comparable to GitHub Copilot; Tabnine offers local inference options, making Tabnine more suitable for offline work. Supermaven's lack of offline capability is a weakness vs. Tabnine.
Analyzes recent code edits and inferred coding patterns to adapt inline suggestions to match team conventions, naming patterns, and structural preferences. The mechanism is implicit (not explicit fine-tuning) and operates only on Pro/Team tiers, suggesting pattern learning from editor activity rather than explicit configuration. Free tier uses a single base model without personalization.
Unique: Implicit style adaptation via editor activity analysis without explicit configuration, versus competitors (Copilot, Tabnine) that require manual style guides or explicit fine-tuning. Supermaven's approach is transparent to the user but also non-configurable and undisclosed in mechanism.
vs alternatives: Requires no manual style configuration compared to tools requiring explicit style guides; trade-off is lack of transparency and inability to control or export learned styles.
Delivers code suggestions to the editor inline as the developer types, with a claimed baseline latency of 250ms from keystroke to suggestion display. The system uses a cloud inference backend and local editor plugin to minimize round-trip time. Latency claim is positioned against an unnamed competitor (783ms), but methodology is undisclosed and no independent verification is provided.
Unique: Claimed 250ms latency via optimized cloud inference pipeline and editor plugin architecture, versus competitors with higher latency (783ms unnamed baseline). Actual differentiation is undisclosed; mechanism may involve request batching, model quantization, or edge caching, but specifics are not public.
vs alternatives: Faster than unnamed competitor (250ms vs 783ms claimed); trade-off is cloud dependency and unverified latency claim with no SLA or performance guarantee.
Provides native editor extensions for VS Code, JetBrains IDEs (IntelliJ IDEA, PyCharm, WebStorm, etc.), and Neovim, enabling inline suggestion rendering, hotkey-driven chat access, and compiler diagnostic integration directly within the editor. Each plugin variant is maintained separately and integrates with the editor's native autocomplete UI, keybinding system, and file context APIs.
Unique: Native plugins for three major editor ecosystems (VS Code, JetBrains, Neovim) with integrated chat and diff application, versus competitors (Copilot, Tabnine) that support broader editor ecosystems but with less deep integration in some cases. Supermaven's approach prioritizes depth over breadth.
vs alternatives: Deep integration with VS Code and JetBrains (native autocomplete UI, hotkey system) compared to web-based tools or lighter integrations; trade-off is limited editor support (no Sublime, Vim, Emacs) and undisclosed Neovim support details.
+5 more capabilities