| 1 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 8 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Generates code suggestions in real-time as developers type by sending the current file's prefix and suffix context (relative to cursor position) to a configurable LLM backend (Hugging Face Inference API, Ollama, OpenAI, or TGI). The extension automatically tokenizes input using the tokenizers library to fit within the model's context window, constructs a prompt with special tokens (start_token, end_token, middle_token), and renders completions as ghost-text overlays matching VS Code's native completion UI pattern. Supports multiple model backends without leaving the editor.
Unique: Supports 4 distinct backend types (Hugging Face Inference API, Ollama, OpenAI-compatible, TGI) with automatic context window fitting via tokenizers library, allowing developers to switch between cloud and local inference without reconfiguring the extension. Default model (bigcode/starcoder) is open-source, avoiding vendor lock-in.
vs alternatives: Offers more backend flexibility than GitHub Copilot (cloud-only) and better local inference support than Tabnine (which primarily uses cloud), while remaining free for open-source models.
Detects whether generated code matches sequences in The Stack training dataset by performing a rapid first-pass Bloom filter lookup against a pre-built index, then optionally linking to stack.dataportraits.org for detailed attribution verification. The extension requires a minimum 50-character code sequence and sufficient surrounding context to perform matching. Triggered via the 'Cmd+Shift+A' keyboard shortcut or command palette. Uses probabilistic matching (Bloom filter) for speed, with acknowledged false positives.
Unique: Integrates Bloom filter-based probabilistic matching against The Stack dataset directly into the VS Code editor workflow, providing real-time attribution checking without requiring external tools or manual searches. Acknowledges false positives transparently and links to detailed verification.
vs alternatives: Provides training data attribution checking that GitHub Copilot does not expose, and integrates it directly into the editor rather than requiring separate tools like the Stack search interface.
Allows developers to select and switch between 4 different LLM backend types (Hugging Face Inference API, Ollama, OpenAI-compatible, Text Generation Inference) via VS Code settings without modifying code or restarting the extension. Each backend has configurable parameters: base URL, model ID, and custom request body JSON. The extension constructs HTTP POST requests with backend-specific URL patterns and forwards the configured requestBody to the selected endpoint. Supports automatic token counting to fit prompts within each model's context window.
Unique: Provides unified configuration for 4 distinct backend types with automatic context window fitting, allowing developers to switch between cloud (Hugging Face, OpenAI) and local inference (Ollama, TGI) without code changes. Default backend uses open-source StarCoder model, avoiding vendor lock-in.
vs alternatives: Offers more backend flexibility than GitHub Copilot (cloud-only) and Tabnine (primarily cloud), while supporting both commercial APIs and fully local inference in a single extension.
Automatically measures and fits the code completion prompt within each model's context window by using the tokenizers library to count tokens in the prefix, suffix, and surrounding code. If the combined prompt exceeds the model's maximum context length, the extension truncates the prefix and/or suffix to fit. This ensures requests succeed without manual context management by the developer. Token counting happens per-request with computational overhead.
Unique: Uses tokenizers library for accurate token counting across multiple model types, automatically truncating context to fit within each backend's limits without requiring manual configuration or developer intervention.
vs alternatives: Provides automatic context fitting that GitHub Copilot handles internally (opaque to users), while making it explicit and configurable for self-hosted backends like Ollama and TGI.
Exposes core extension functionality through VS Code's command palette (Cmd/Ctrl+Shift+P) and dedicated keyboard shortcuts. Documented commands include 'Llm: Login' for authentication and 'Llm: Code Attribution Check' (Cmd+Shift+A). The extension registers these commands with VS Code's command registry, making them discoverable and remappable. Additional commands exist but are not enumerated in available documentation.
Unique: Integrates with VS Code's native command palette and keybinding system, allowing developers to discover and customize extension commands without leaving the editor. Supports remappable shortcuts (Cmd+Shift+A for attribution checks).
vs alternatives: Provides standard VS Code integration patterns that match native editor workflows, unlike some extensions that rely on custom UI panels or external tools.
Manages Hugging Face API authentication by automatically detecting tokens from the huggingface-cli cache on disk (if huggingface-cli was previously configured) or accepting manual token entry via the 'Llm: Login' command. Tokens are stored in VS Code's secure credential storage (mechanism not specified). The extension validates tokens before making API requests to the Hugging Face Inference API. Tokens can be obtained from hf.co/settings/token.
Unique: Automatically detects and reuses Hugging Face CLI tokens from disk cache, reducing friction for developers already using Hugging Face tools. Falls back to manual entry via 'Llm: Login' command if auto-detection fails.
vs alternatives: Simpler authentication flow than GitHub Copilot (which requires GitHub OAuth) and more flexible than Tabnine (which requires account creation in extension UI).
Exposes extension configuration through VS Code's standard settings UI (Cmd+, → filter 'Llm'). Developers can configure backend type, model ID, base URLs, request body parameters, and other options via a searchable settings panel. The full list of available configuration options is not enumerated in documentation. Settings are persisted in VS Code's configuration store and applied immediately or after extension reload.
Unique: Integrates with VS Code's native settings UI and search, allowing configuration through the standard editor settings panel rather than custom dialogs or JSON files.
vs alternatives: Provides standard VS Code configuration patterns that match native editor workflows, unlike extensions with custom configuration dialogs or external configuration files.
Renders generated code completions as ghost-text overlays in the editor, matching VS Code's native code completion UI pattern. The extension inserts completions at the cursor position when accepted (typically via Tab or Enter key). Ghost-text appears in a dimmed color to distinguish it from actual code. The rendering is handled by VS Code's InlineCompletionItemProvider API (or similar completion API).
Unique: Uses VS Code's native InlineCompletionItemProvider API to render completions as ghost-text, providing a familiar UX that matches VS Code's built-in completion behavior without custom UI.
vs alternatives: Matches VS Code's native completion UX more closely than GitHub Copilot's dropdown-based suggestions, and simpler than custom completion panels used by some extensions.
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 llm-vscode at 36/100. llm-vscode 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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