| 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 |
Generates contextually appropriate code completions using JetBrains' proprietary Mellum LLM, which is optimized for developer workflows and syntax awareness across 10+ programming languages. The extension analyzes the current file context, detects the active programming language, and produces completions that respect language-specific syntax rules and project conventions. Completions are delivered inline within the editor with latency optimized for real-time developer interaction.
Unique: Uses JetBrains' proprietary Mellum LLM specifically trained for developer code completion rather than general-purpose LLMs; integrates directly with VS Code's IntelliSense API for native inline rendering without overlay UI, and leverages JetBrains' IDE telemetry to understand project-specific coding patterns.
vs alternatives: Faster and more syntax-accurate than GitHub Copilot for Java/Kotlin/C# because Mellum is trained on JetBrains' massive IDE telemetry dataset, and more language-aware than generic LLM completions because it respects language-specific AST structures.
Provides a natural language chat interface that maintains awareness of the current file, project structure, and code context. The chat system allows developers to ask questions about code, request explanations, and iteratively refine prompts while the AI maintains conversation history and project context. The interface is built into VS Code's sidebar or panel UI and integrates with the Mellum LLM backend for language understanding and code-aware responses.
Unique: Integrates chat directly into VS Code's native UI (sidebar/panel) rather than as a separate window or web interface, and automatically infers project context from the active editor state without requiring explicit file selection or context specification by the user.
vs alternatives: More integrated into the development workflow than ChatGPT or Claude web interfaces because it maintains automatic awareness of the current codebase and file context without copy-pasting code into a separate tool.
Automatically infers project context from the currently open file, active editor state, and workspace metadata without requiring developers to explicitly select files or directories for analysis. The system detects the programming language, identifies related files (imports, dependencies), and builds a mental model of the codebase scope. Context scope is limited to files accessible within VS Code; the extension does not directly access the file system outside the editor.
Unique: Infers project context automatically from editor state and workspace metadata without requiring explicit file selection or configuration, reducing friction for developers but introducing uncertainty about what context is actually being used.
vs alternatives: More seamless than tools requiring manual context specification because inference is automatic, but less transparent than explicit context selection because developers cannot see or control what context is being analyzed.
Collects telemetry data from the extension to improve product features and user experience. The system tracks usage patterns, feature adoption, and error conditions, transmitting this data to JetBrains servers for analysis. Telemetry collection is enabled by default, but an opt-out mechanism is not documented in the marketplace or extension documentation, requiring users to consult external privacy policies.
Unique: Collects telemetry by default without prominent opt-out UI in the extension, relying on external privacy policies for disclosure; specific data collection practices are undocumented.
vs alternatives: Enables JetBrains to improve products based on real usage data, but less transparent than tools with explicit telemetry controls and documented data practices.
Enables the AI to propose and apply changes across multiple files in a single interaction through an 'Edit' or 'Agentic' mode. This mode orchestrates multiple AI models (specific models undocumented) to decompose complex refactoring or feature-addition tasks, generate code changes, and apply them directly to the codebase. The system operates with human-in-the-loop supervision, requiring developer approval before changes are committed, and integrates with VS Code's file system and editor APIs to apply diffs.
Unique: Implements human-in-the-loop agentic editing where the AI proposes multi-file changes but requires explicit developer approval before applying them, rather than autonomous auto-commit; uses undocumented multi-model orchestration to handle complex cross-file dependencies.
vs alternatives: More integrated and safer than command-line refactoring tools because changes are previewed and approved within the IDE before application, and more capable than single-file code generation because it understands and modifies call sites and dependencies across the codebase.
Analyzes staged or uncommitted code changes and generates contextually appropriate commit messages using the Mellum LLM. The system examines diffs, understands the semantic intent of changes, and produces commit messages that follow conventional commit formats or project-specific conventions. This capability integrates with VS Code's source control UI and can be triggered from the commit dialog or command palette.
Unique: Integrates directly into VS Code's native source control UI and analyzes actual code diffs rather than requiring manual description, using Mellum's code understanding to infer semantic intent from syntax changes.
vs alternatives: More context-aware than generic commit message templates because it analyzes actual code changes, and more integrated than standalone commit message generators because it operates within the IDE's native workflow.
Generates human-readable explanations of code functions, classes, or entire files, and can automatically produce documentation in language-appropriate formats (docstrings for Python, JSDoc for JavaScript, etc.). The system analyzes code structure, detects the programming language, and produces documentation that matches the language's standard conventions. Documentation can be inserted directly into the code or displayed in the chat interface.
Unique: Generates language-specific documentation formats (Python docstrings, JavaScript JSDoc, etc.) by detecting the active language and applying format-appropriate templates, rather than producing generic documentation that requires manual conversion.
vs alternatives: More language-aware than generic documentation tools because it understands language-specific conventions, and more integrated than external documentation generators because it operates within the IDE and can insert documentation directly into code.
Analyzes code to identify potential bugs, performance issues, and optimization opportunities, then presents findings and suggestions through the chat interface or inline comments. The system uses static analysis patterns combined with Mellum's code understanding to detect common pitfalls (null pointer dereferences, inefficient loops, etc.) and suggests improvements. Suggestions are presented as conversational recommendations rather than enforced linting rules.
Unique: Combines static pattern matching with Mellum's semantic code understanding to identify bugs and optimization opportunities, presenting findings as conversational suggestions rather than enforced linting rules, allowing developers to evaluate and apply recommendations selectively.
vs alternatives: More conversational and explainable than traditional linters because it provides reasoning for suggestions, and more comprehensive than single-purpose static analysis tools because it combines multiple analysis patterns and semantic understanding.
+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 Assistant by JetBrains at 36/100. AI Assistant by JetBrains 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