Supermaven ranks higher at 71/100 vs CodeGPT: write and improve code using AI at 42/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | CodeGPT: write and improve code using AI | Supermaven |
|---|---|---|
| Type | Extension | Extension |
| UnfragileRank | 42/100 | 71/100 |
| Adoption | 1 |
| 1 |
| Quality | 0 | 1 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 6 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Accepts natural language instructions typed directly in VS Code editor and generates code snippets or complete functions by sending context (selected text, file content, cursor position) to OpenAI's GPT-3 or ChatGPT API. The extension captures the active editor state, constructs a prompt with code context, and inserts generated code at the cursor position or replaces selected text. Uses VS Code's TextEditor API to read/write document content and maintain cursor position awareness.
Unique: Integrates directly into VS Code's editor context via the Extension API, allowing inline code generation without leaving the IDE or managing separate chat windows. Uses VS Code's command palette and editor selection state to minimize friction compared to web-based code generation tools.
vs alternatives: Faster iteration than GitHub Copilot for users already comfortable with explicit prompting, and cheaper than Copilot for low-volume usage due to pay-as-you-go OpenAI pricing model.
Analyzes selected code blocks and generates human-readable explanations by sending the code to GPT-3/ChatGPT with a system prompt asking for clarification. The extension extracts the selected text from the active editor, constructs a prompt like 'Explain this code:', sends it to OpenAI, and displays the response in a side panel or new editor tab. Supports syntax-aware selection via VS Code's editor selection API.
Unique: Operates on editor selection state rather than requiring copy-paste to a separate tool, reducing context-switching. Displays explanations inline or in a side panel, keeping the original code visible for reference.
vs alternatives: More accessible than reading source code comments or external documentation, and faster than asking colleagues for explanations.
Scans selected code or entire files for potential bugs by sending code to GPT-3/ChatGPT with a prompt asking for bug identification and fixes. The extension constructs a prompt like 'Find bugs in this code and suggest fixes:', receives a structured response listing issues and corrections, and displays them in a VS Code diagnostic panel or inline code lens. Uses VS Code's Diagnostic API to render issues with severity levels and quick-fix suggestions.
Unique: Integrates bug detection into the VS Code diagnostic workflow, displaying issues with severity levels and quick-fix suggestions inline, rather than requiring manual interpretation of a separate report.
vs alternatives: Complements traditional linters and type checkers by catching logic-level bugs that static analysis cannot, though with lower precision.
Accepts refactoring requests (e.g., 'extract this function', 'rename variables for clarity', 'simplify this logic') and generates refactored code by sending the selected code and refactoring intent to GPT-3/ChatGPT. The extension receives refactored code, displays it in a diff view or side-by-side editor, and allows the developer to accept or reject the changes. Uses VS Code's diff editor API to visualize changes before applying them.
Unique: Provides refactoring suggestions with a diff preview before applying changes, allowing developers to review and approve modifications rather than auto-applying transformations.
vs alternatives: More flexible than IDE-native refactoring tools (which are language-specific and limited to predefined patterns) because it can handle arbitrary refactoring requests in natural language.
Provides a chat panel within VS Code where developers can ask coding questions, request code reviews, or discuss implementation approaches. The extension maintains a conversation history, sends messages to GPT-3/ChatGPT with accumulated context, and displays responses in a chat UI. Supports context injection (selected code, file content, error messages) into chat messages. Uses VS Code's WebView API to render the chat interface and manages conversation state in memory.
Unique: Embeds a chat interface directly in VS Code's sidebar, allowing developers to maintain context with selected code and file content while conversing with AI, without switching to a web browser or separate application.
vs alternatives: More integrated than ChatGPT web interface for coding tasks, and supports richer context injection (selected code, file content) compared to generic chat applications.
Allows developers to configure and switch between OpenAI API keys and select between GPT-3 and ChatGPT models via VS Code settings. The extension reads API keys from VS Code's secure credential storage (or environment variables) and constructs API requests with the selected model endpoint. Supports multiple API key profiles and model selection via the command palette or settings UI. Uses VS Code's SecretStorage API for secure credential management.
Unique: Uses VS Code's SecretStorage API for secure, OS-level credential storage rather than plain-text configuration files, reducing the risk of accidental credential exposure in version control.
vs alternatives: More secure than environment variable-based approaches because credentials are encrypted by the OS, and more user-friendly than manual API key injection in each request.
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 CodeGPT: write and improve code using AI at 42/100. CodeGPT: write and improve code using AI 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.
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