Supermaven ranks higher at 71/100 vs CodeViz | Visual codebase maps at 37/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | CodeViz | Visual codebase maps | Supermaven |
|---|---|---|
| Type | Extension | Extension |
| UnfragileRank | 37/100 | 71/100 |
| Adoption | 1 | 1 |
| Quality | 0 | 1 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 9 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Generates interactive visual maps of codebases by leveraging Anthropic LLMs to analyze code structure and produce Mermaid/Draw.io diagrams spanning from high-level architecture down to individual function calls. The extension processes code locally to generate embeddings, sends minimal context to Anthropic's API (with zero-day retention), and renders interactive webview diagrams where nodes link directly to source locations. Users can click any diagram element to jump to the corresponding code in the editor.
Unique: Combines LLM-driven code analysis with local embedding generation and interactive webview rendering, enabling click-to-code navigation from generated diagrams without storing code on external servers. Uses Anthropic's API with explicit zero-day retention guarantee, differentiating from competitors that may retain code for model improvement.
vs alternatives: Faster codebase comprehension than manual code reading and more privacy-preserving than tools that store code for analysis, though dependent on internet connectivity and Anthropic API availability unlike local-only alternatives.
Accepts plain English questions about code structure and generates focused, contextual diagrams in response by routing queries through Anthropic's LLM. The extension maintains awareness of the user's current file context and produces diagram suggestions tailored to the query scope. Generated diagrams are rendered interactively in the webview with direct links to relevant source code sections.
Unique: Implements context-aware querying where the LLM understands the user's current file position and generates diagrams scoped to the query intent, rather than always returning full codebase maps. Combines query processing with automatic suggestion generation to guide users toward relevant visualizations.
vs alternatives: More intuitive than command-line code search tools because it accepts natural language and returns visual diagrams, though slower than local grep-based tools due to LLM latency and internet dependency.
Generates comprehensive, codebase-wide context summaries in a single click, formatted for consumption by downstream LLM-based tools (e.g., Copilot, Claude, custom agents). The extension analyzes the full codebase locally to extract relevant code snippets, architecture patterns, and dependency information, then produces a structured prompt or context block that can be copied and pasted into other AI tools without requiring those tools to re-analyze the codebase.
Unique: Bridges CodeViz's local codebase analysis with external LLM tools by generating pre-formatted context blocks that can be directly injected into other AI systems' prompts, eliminating the need for those tools to independently analyze the codebase. Leverages local embeddings to identify the most relevant code sections for inclusion.
vs alternatives: More efficient than manually copying code snippets or re-explaining codebase structure to each new LLM tool, though less integrated than tools with native codebase indexing (e.g., Copilot's workspace awareness) due to the copy-paste workflow.
Enables direct navigation from generated diagram elements to source code by maintaining bidirectional links between diagram nodes and file locations. When a user clicks any node or connection in a Mermaid/Draw.io diagram rendered in the CodeViz webview, the extension automatically opens the corresponding source file and scrolls to the relevant function, class, or module definition. This is achieved through the extension's access to VS Code's editor API and file system context.
Unique: Maintains semantic links between LLM-generated diagram elements and actual source code locations, enabling seamless navigation without requiring users to manually search or remember file paths. Leverages VS Code's editor API to provide native editor integration rather than opening external tools.
vs alternatives: More intuitive than traditional code search because navigation is visual and contextual, though less reliable than language server-based navigation (e.g., Go to Definition) due to LLM-based location identification rather than AST analysis.
Exports generated codebase diagrams in multiple formats (Mermaid, Draw.io) to enable sharing and reuse across teams and tools. Mermaid diagrams are Markdown-compatible and can be embedded in documentation, GitHub READMEs, and wikis. Draw.io exports create editable diagram files that can be opened in Draw.io, Lucidchart, or other compatible tools. The extension handles format conversion and file generation locally without requiring external services.
Unique: Supports dual export formats (Mermaid for documentation, Draw.io for editing) from a single diagram, enabling both version-controlled documentation and collaborative refinement workflows. Mermaid export is Markdown-native, allowing diagrams to be embedded directly in Git repositories.
vs alternatives: More flexible than tools that export to a single format, though less feature-rich than native Draw.io or Lucidchart for diagram refinement since exports are generated artifacts rather than live-editable sources.
Generates code embeddings locally within the VS Code extension process without transmitting raw code to external servers. The extension uses these embeddings to identify relevant code sections for diagram generation and context extraction. Embeddings are computed on-device using an unspecified embedding model, enabling semantic code analysis while maintaining code privacy. Only minimal processed context (not raw code) is sent to Anthropic's API for LLM analysis.
Unique: Performs semantic code analysis locally without transmitting raw code to external servers, differentiating from cloud-only code analysis tools. Combines local embeddings with minimal-context LLM queries to Anthropic (with zero-day retention guarantee) to achieve both privacy and intelligence.
vs alternatives: More privacy-preserving than tools that upload entire codebases to cloud APIs, though less transparent than fully open-source local-only tools since the embedding model and computation method are not documented.
Provides explicit commands to regenerate architecture visualizations and diagrams on demand via the command palette (`CodeViz: Regenerate Architecture`). When triggered, the extension re-analyzes the codebase, recomputes embeddings, and regenerates all diagrams to reflect recent code changes. This enables users to keep visualizations in sync with evolving codebases without manual diagram updates.
Unique: Provides explicit user control over diagram regeneration timing via command palette, avoiding automatic updates that might consume API quota unexpectedly. Enables on-demand synchronization of visualizations with code changes without background processing.
vs alternatives: More cost-conscious than tools with automatic continuous regeneration, though less convenient than tools that automatically update diagrams on file save or CI/CD triggers.
Collects usage telemetry (error logs, webview open events, session replays, user queries) to improve the extension, with a binary toggle in extension settings to disable all telemetry. When enabled, telemetry is transmitted to CodeViz servers; when disabled, no usage data is collected. Notably, raw code and LLM prompts are explicitly NOT collected, and all data sent to Anthropic, GCP, and AWS has zero-day retention (deleted immediately after processing).
Unique: Explicitly guarantees zero-day retention for all data sent to Anthropic, GCP, and AWS, and commits to not storing raw code or prompts, providing stronger privacy guarantees than many AI tools. However, session replay and query collection practices are less transparent than competitors.
vs alternatives: More privacy-conscious than tools that retain code for model improvement, though less transparent than tools with detailed data retention policies and audit logs.
+1 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 CodeViz | Visual codebase maps at 37/100. CodeViz | Visual codebase maps 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