Supermaven ranks higher at 71/100 vs WiseGPT (Coding Assistant by DhiWise) at 40/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | WiseGPT (Coding Assistant by DhiWise) | Supermaven |
|---|---|---|
| Type | Extension | Extension |
| UnfragileRank | 40/100 | 71/100 |
| Adoption | 1 |
| 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 |
Analyzes the entire codebase within a VS Code workspace to build a semantic understanding of code patterns, architecture, and style conventions. The extension sends codebase metadata to DhiWise backend servers which index and vectorize the code for context-aware generation. Uses @codebase mention syntax in chat to trigger full repository context retrieval, enabling the AI to understand existing patterns, naming conventions, and architectural decisions before generating new code.
Unique: Uses @codebase mention syntax to explicitly trigger full repository context retrieval in chat, combined with backend-side indexing and vectorization rather than local AST parsing, enabling context-aware generation without requiring developers to manually provide file references
vs alternatives: Differs from GitHub Copilot's file-local context by analyzing entire repository patterns upfront, and from Cursor's local indexing by offloading computation to backend servers, trading latency for broader context coverage
Integrates with task management platforms (Jira, Trello, Asana, ClickUp) to extract task descriptions and requirements, then generates production-ready code that implements those tasks. The extension reads task metadata including title, description, acceptance criteria, and linked resources, sends them to the DhiWise backend along with codebase context, and returns generated code that matches the project's existing style and architecture. Eliminates the need for manual prompt engineering by converting structured task data into code generation requests.
Unique: Directly integrates with task management APIs to extract structured requirements and convert them to code generation requests without manual prompt writing, combining task metadata parsing with codebase-aware generation to produce contextually appropriate implementations
vs alternatives: Unlike Copilot which requires manual task-to-prompt translation, WiseGPT reads task data directly from project management tools; differs from GitHub Copilot's chat by automating the requirement extraction step entirely
Generates code across multiple programming languages and frameworks, with support claimed for 'all programming languages and frameworks'. The extension analyzes the project's technology stack and generates code in the appropriate language and framework, using language-specific idioms and best practices. Backend inference adapts to language-specific patterns, syntax, and conventions, ensuring generated code is idiomatic rather than generic translations.
Unique: Claims support for all programming languages and frameworks with language-specific idiom generation, adapting backend inference to language conventions rather than using generic code patterns
vs alternatives: Broader language coverage than Copilot which focuses on popular languages; differs from language-specific tools by supporting polyglot projects in a single interface
Operates on a freemium pricing model with free tier access to basic code generation and chat features, while advanced features like vulnerability detection and code implementation for tasks are restricted to enterprise users. The extension manages feature access through backend authentication and account tier checking, enabling free users to access core capabilities while reserving advanced security and automation features for paid tiers. Specific free tier limits (requests per day, codebase size, etc.) are not documented.
Unique: Implements feature-gated access model where advanced capabilities like vulnerability detection and task-based code implementation are restricted to enterprise tiers, while basic generation and chat are available to free users
vs alternatives: Similar freemium model to GitHub Copilot but with less transparent pricing and feature documentation; differs by explicitly gating security features to enterprise tier
Converts Figma design files into functional code by analyzing design components, layouts, and styling, then generates code using the project's existing UI libraries and component patterns. The extension reads Figma design metadata (components, constraints, colors, typography) and sends it to the DhiWise backend along with codebase context, which then generates code that reuses existing project components and styling conventions rather than creating new ones. Supports integration with DhiWise Design Converter projects to pull source code directly into the IDE.
Unique: Combines Figma design analysis with codebase-aware code generation to reuse existing project components and styling conventions, rather than generating generic code from designs; integrates with DhiWise Design Converter for bidirectional design-code workflow
vs alternatives: Differs from Figma's native code export by understanding project-specific component libraries and generating code that reuses existing patterns; more integrated than standalone design-to-code tools by maintaining context with the actual codebase
Provides real-time code completion suggestions as developers type, with suggestions personalized to match the project's coding style and patterns. The extension monitors editor changes and sends partial code context to the DhiWise backend, which returns completion suggestions that align with existing code conventions, naming patterns, and architectural decisions. Supports both traditional autocompletion and comment-based code generation where developers write comments describing desired functionality and the AI generates matching code.
Unique: Combines real-time inline completion with comment-based code generation and style-aware personalization, using backend inference to match project patterns rather than local heuristics or regex-based completion
vs alternatives: Unlike GitHub Copilot which uses local context windows, WiseGPT leverages full codebase analysis for style matching; differs from Tabnine by emphasizing comment-driven generation alongside traditional completion
Scans code for security vulnerabilities and generates fixes that remediate identified issues while maintaining code functionality. The extension analyzes the codebase for common vulnerability patterns (SQL injection, XSS, insecure dependencies, etc.) and sends findings to the DhiWise backend, which generates corrected code that fixes the vulnerability using secure coding practices appropriate to the project's technology stack. Integrates with the codebase context to ensure fixes follow existing patterns and conventions.
Unique: Combines vulnerability detection with style-aware code generation to produce fixes that integrate seamlessly with existing codebase patterns, rather than generic security patches that may conflict with project conventions
vs alternatives: Differs from static analysis tools like SonarQube by generating fixes automatically rather than just reporting issues; more integrated than standalone security tools by maintaining codebase context
Automatically generates unit tests, integration tests, and test cases based on code implementation and task requirements. The extension analyzes function signatures, logic flow, and acceptance criteria from linked tasks, then generates test code that covers normal cases, edge cases, and error conditions. Generated tests follow the project's testing framework conventions and style, integrating with existing test suites rather than creating isolated test files.
Unique: Generates tests from both code implementation and task requirements, creating test cases that verify both functional correctness and acceptance criteria compliance, with style-aware generation matching project testing conventions
vs alternatives: Unlike generic test generators, WiseGPT combines code analysis with requirement understanding to generate tests that verify business logic; differs from Copilot by explicitly targeting test generation as a primary capability
+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 WiseGPT (Coding Assistant by DhiWise) at 40/100. WiseGPT (Coding Assistant by DhiWise) 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