Supermaven ranks higher at 71/100 vs Double - DeepSeek R1, OpenAI o1, Sonnet, and more at 37/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | Double - DeepSeek R1, OpenAI o1, Sonnet, and more | 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 code suggestions as the user types in the editor, with support for multiple cursor positions and mid-line completions. The extension monitors keystroke events in real-time, sends the current file context and cursor position to a cloud-based AI model (OpenAI o1, DeepSeek R1, Claude Sonnet, or Llama variants), and streams back suggestions that appear inline without interrupting the editing flow. Suggestions are accepted via Tab key and automatically include relevant imports for functions, variables, and libraries based on the detected language and project context.
Unique: Supports switching between 7+ distinct AI models (OpenAI o1, DeepSeek R1, Claude 3.5 Sonnet, Llama 3.1 variants) within a single extension, allowing developers to compare model quality and cost trade-offs without changing tools. Most competitors (Copilot, Codeium) lock users into a single model or require separate extensions.
vs alternatives: Offers model flexibility and latest reasoning models (o1, R1) faster than GitHub Copilot's official support, but likely has higher latency than Copilot's local caching and may require manual API key management vs Copilot's GitHub account integration.
Provides a persistent chat panel (accessed via Cmd+M / Ctrl+M) where developers can send free-form prompts to generate code, explain existing code, write tests, add documentation, or analyze code quality. The chat accepts the current file as context and allows explicit code selection via Cmd+Shift+M / Ctrl+Shift+M to focus AI analysis on specific code blocks. Responses are streamed back as formatted text with syntax highlighting for code blocks, enabling iterative refinement through follow-up questions.
Unique: Integrates chat and inline autocomplete in a single extension with model switching, whereas most competitors (Copilot, Codeium) separate chat into a different product or require GitHub Copilot Chat subscription. Double's chat accepts highlighted code context via keybinding (Cmd+Shift+M) for faster context passing than copy-paste workflows.
vs alternatives: Faster context passing than ChatGPT or Claude web interfaces (one keybinding vs copy-paste), but lacks persistent conversation history and cross-file codebase understanding that Copilot Chat provides through GitHub integration.
Displays AI-generated code changes in a side-by-side or unified diff format, allowing developers to review additions, deletions, and modifications before accepting them. The extension highlights changes with color coding (additions in green, deletions in red) and provides accept/reject controls for each suggestion, enabling careful review of multi-line edits or refactoring suggestions before they are applied to the file.
Unique: Integrates diff-style review directly into the VS Code sidebar chat, avoiding context switching to external diff tools. Most competitors (Copilot, Codeium) apply suggestions inline without explicit diff review, or require manual comparison.
vs alternatives: Provides explicit code review workflow similar to GitHub's PR diff interface, but integrated into the editor for faster feedback loops than reviewing changes in a separate tool or PR interface.
Allows developers to choose from 7+ AI models (OpenAI o1, GPT-4o, DeepSeek R1, Claude 3.5 Sonnet, Claude 3 Opus, Llama 3.1 405B/70B/8B) for both autocomplete and chat features. The extension abstracts away model-specific API differences and routing, enabling users to switch models without changing configuration or restarting the editor. Model selection mechanism (per-query, per-session, or global setting) is not documented, but the capability enables cost-quality trade-offs and experimentation with latest reasoning models.
Unique: Supports 7+ distinct models including latest reasoning models (o1, DeepSeek R1) in a single extension, with abstracted API routing that hides provider-specific differences. GitHub Copilot locks users into OpenAI models; Codeium offers fewer model choices; most competitors require separate extensions or tools for model switching.
vs alternatives: Fastest way to access latest models (o1, R1) without waiting for official IDE integrations, and enables cost optimization by mixing models. However, requires manual API key management for each provider vs Copilot's GitHub account integration.
Analyzes the current file's coding style, naming conventions, indentation, and language-specific patterns to generate suggestions that match the developer's existing code style. The extension examines the file's syntax tree or token stream to infer conventions (camelCase vs snake_case, tabs vs spaces, comment style, import organization) and instructs the AI model to generate suggestions conforming to these patterns. This reduces the need for manual formatting or style corrections after accepting AI suggestions.
Unique: Automatically detects and matches file-level style conventions without explicit configuration, whereas most competitors (Copilot, Codeium) generate code in a default style and rely on post-generation formatters. Double's approach reduces friction by embedding style awareness into the suggestion generation itself.
vs alternatives: Reduces manual formatting work compared to Copilot, but lacks integration with project-wide linting tools (ESLint, Pylint) that could provide more accurate style rules than file-level inference.
Detects when AI-generated code references external functions, classes, or libraries and automatically generates the necessary import statements. The extension analyzes the generated code's identifiers, matches them against the project's available dependencies (inferred from package.json, requirements.txt, or similar), and inserts import statements at the appropriate location in the file. This eliminates the manual step of adding imports after accepting AI suggestions.
Unique: Automatically generates imports as part of the suggestion workflow, whereas most competitors (Copilot, Codeium) generate code without imports and rely on IDE's built-in import resolution or manual addition. Double's approach is more complete but requires accurate dependency detection.
vs alternatives: Reduces friction compared to Copilot by eliminating the import-addition step, but accuracy depends on project metadata being accessible and up-to-date, which may fail in monorepos or projects with non-standard dependency structures.
Allows developers to delegate code editing tasks to the AI, which generates and applies changes directly to the file. The mechanism is described as 'delegate your edits' but implementation details are not documented. Likely works by accepting a natural language instruction (via chat or command), generating modified code, and applying it to the selected code block or file. Changes are shown in diff format for review before being committed.
Unique: Offers 'edit delegation' as a first-class feature, whereas most competitors (Copilot, Codeium) focus on suggestion generation and require manual acceptance. Unknown if Double's implementation is more sophisticated or just a rebranding of standard code generation.
vs alternatives: Potentially faster workflow for refactoring if implementation is robust, but complete lack of documentation makes it impossible to assess reliability or scope compared to alternatives.
Provides dedicated keybindings (Cmd+M / Ctrl+M for chat, Cmd+Shift+M / Ctrl+Shift+M for passing selected code) that enable developers to invoke AI features without using the mouse or navigating menus. Selected code is automatically passed as context to the chat interface, reducing the friction of copy-pasting code into prompts. This design pattern prioritizes keyboard-driven workflows common in developer tools.
Unique: Implements dedicated keybindings for context passing (Cmd+Shift+M) as a first-class feature, whereas most competitors rely on copy-paste or require navigating UI menus. This design prioritizes keyboard efficiency and reduces context-switching friction.
vs alternatives: Faster context passing than Copilot Chat's default workflows, but less discoverable for new users and requires memorizing keybindings vs Copilot's more intuitive UI.
+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 Double - DeepSeek R1, OpenAI o1, Sonnet, and more at 37/100. Double - DeepSeek R1, OpenAI o1, Sonnet, and more 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