| 0 |
| 1 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 8 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Provides a persistent sidebar panel within VS Code where users can compose arbitrary prompts and receive streaming responses from OpenAI's API. The extension maintains conversation history within the session, allows editing and resending previous prompts, and automatically handles response continuation when API responses are truncated, combining fragmented outputs into coherent answers without user intervention.
Unique: Implements automatic response continuation logic that detects and combines truncated API responses without user action, reducing friction in handling partial code outputs — a pattern not standard in most VS Code AI extensions which require manual prompt re-submission
vs alternatives: Simpler and more lightweight than GitHub Copilot for exploratory conversations, but lacks Copilot's codebase-aware context indexing and inline completion capabilities
Enables users to generate new files or code blocks directly from AI suggestions via a single-click action in the sidebar. The extension parses AI-generated code responses and provides a clickable interface to create files in the project workspace or insert code into the current editor, bypassing manual copy-paste workflows.
Unique: Provides direct file creation from AI responses without intermediate copy-paste, reducing context switching — implemented as a simple click handler that parses sidebar response text and invokes VS Code's file creation APIs
vs alternatives: More direct than Copilot's inline suggestions for file scaffolding, but less intelligent about project structure and dependencies than specialized code generators like Plop or Yeoman
Allows users to select code in the editor, send it to ChatGPT with a fix/modify request, and receive suggestions that can be applied back to the editor. The extension integrates with VS Code's selection API to capture highlighted code, passes it as context to the AI, and provides mechanisms to replace or insert the modified code directly into the file.
Unique: Integrates with VS Code's selection API to capture highlighted code as implicit context, reducing the need for explicit copy-paste — a pattern that leverages VS Code's native editor capabilities rather than requiring custom context management
vs alternatives: More flexible than Copilot's inline suggestions for arbitrary refactoring, but less context-aware than dedicated refactoring tools like Jetbrains IDEs which understand project structure and type information
Allows users to select between multiple OpenAI models (GPT-4, GPT-3.5, GPT-3, Codex) via extension settings, with all requests routed directly to OpenAI's API using a user-provided API key. The extension abstracts model selection into a configuration option, enabling users to switch models without code changes and manage API costs by choosing appropriate model tiers.
Unique: Provides direct model selection without abstraction layers, allowing users to manage API costs and capabilities directly — implemented as a simple configuration option that maps to OpenAI API model parameters
vs alternatives: More transparent about model selection than Copilot (which abstracts model choice), but less sophisticated than multi-model frameworks like LangChain which provide automatic model selection and fallback logic
Captures the entire conversation history from a session and exports it to a markdown file, preserving prompts, responses, and formatting. The export includes timestamps or conversation order, enabling users to archive discussions, share them with team members, or reference them later outside the IDE.
Unique: Provides simple markdown export without complex formatting or metadata — a lightweight approach that prioritizes portability and readability over structured data capture
vs alternatives: More portable than Copilot's inline suggestions (which are not easily exported), but less structured than dedicated conversation management tools like Slack or Notion which provide search, tagging, and collaboration features
Enables users to define custom prompt prefixes that are automatically prepended to user queries before sending to the API. This allows users to establish consistent context, tone, or instructions (e.g., 'You are a TypeScript expert') without repeating them in every prompt, reducing prompt engineering overhead and improving response consistency.
Unique: Implements simple string prepending to prompts, allowing users to inject context without modifying every query — a lightweight approach that trades sophistication for ease of use
vs alternatives: More flexible than Copilot's fixed system prompts, but less powerful than frameworks like LangChain or Prompt Engineering tools which support dynamic context injection and prompt templates
Streams responses from OpenAI's API in real-time to the sidebar, displaying partial results as they arrive. Users can interrupt streaming at any time to stop token consumption, and the extension provides a 'stop response' action to halt further API calls and preserve remaining token quota.
Unique: Provides manual token-aware interruption via 'stop response' action, giving users explicit control over API costs — a pattern that prioritizes cost transparency over convenience
vs alternatives: More cost-conscious than Copilot's always-complete responses, but less sophisticated than frameworks with automatic token budgeting and cost estimation
Maintains a history of all prompts sent during a session and allows users to select, edit, and resend previous prompts without retyping them. This enables iterative refinement of queries, A/B testing different prompt variations, and quick re-execution of similar requests with minor modifications.
Unique: Stores and allows editing of previous prompts within the sidebar UI, reducing friction in prompt iteration — a simple pattern that leverages VS Code's text editing capabilities
vs alternatives: More convenient than retyping prompts from scratch, but less sophisticated than dedicated prompt management tools like PromptBase or Hugging Face which provide version control and sharing
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 ChatGPT [deprecated] at 41/100. ChatGPT [deprecated] leads on adoption and ecosystem, while Supermaven is stronger on 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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