Supermaven ranks higher at 71/100 vs Ollama Code Fixer - AI Coding Assistant at 32/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | Ollama Code Fixer - AI Coding Assistant | Supermaven |
|---|---|---|
| Type | Extension | Extension |
| UnfragileRank | 32/100 | 71/100 |
| Adoption | 0 |
| 1 |
| Quality | 0 | 1 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 13 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Analyzes selected code blocks using local Ollama models (default: CodeLlama 7B) to identify syntax errors, logic bugs, and runtime issues, then generates corrected code with explanations. The extension sends the selected code as context to the local Ollama API endpoint (default http://localhost:11434), receives the fixed version, and presents it in a preview before applying changes. This approach eliminates cloud dependency and API costs while maintaining full code privacy on the developer's machine.
Unique: Uses local Ollama models instead of cloud APIs, enabling offline operation and zero data transmission to external servers. Implements configurable preview-before-apply workflow with optional automatic backup of original code before modifications.
vs alternatives: Faster than GitHub Copilot for privacy-sensitive codebases and eliminates per-request API costs, but trades accuracy for privacy since local 7B models are less capable than cloud-based GPT-4 or Claude 3.
Sends selected code to the local Ollama model with an optimization prompt, requesting improvements to algorithmic efficiency, memory usage, and code readability. The model analyzes the code structure and generates refactored versions with explanations of optimizations applied (e.g., reducing time complexity, removing redundant operations, improving variable naming). Results are previewed in the editor before application, with optional automatic backup of the original code.
Unique: Runs optimization analysis locally without cloud transmission, allowing developers to iterate on performance improvements in real-time. Includes configurable insertion modes (replace, above, below, new file) for flexible code workflow integration.
vs alternatives: Provides privacy-first optimization suggestions compared to cloud-based tools like Copilot, but lacks integration with actual profiling data or benchmarking that would validate optimization effectiveness.
Provides a dedicated chat panel in the VS Code sidebar for conversational interaction with the local Ollama model. Developers can ask questions about code, request explanations, discuss design decisions, or get coding advice in a multi-turn conversation. Chat context includes the current file and selected code, allowing the model to provide contextually relevant responses. All conversation stays local and private.
Unique: Provides conversational AI assistance within VS Code without cloud transmission, enabling developers to have private, cost-free conversations with local models. Integrates current file context into chat for more relevant responses.
vs alternatives: More privacy-preserving than cloud-based coding assistants like ChatGPT or Claude, but conversational quality from local 7B models is typically lower than GPT-4 or Claude 3, particularly for nuanced design discussions.
Optionally automates starting and stopping the local Ollama server based on extension usage. When enabled via configuration (`autoStartOllama`), the extension detects if Ollama is not running and automatically starts it before executing operations. This eliminates the need for developers to manually start Ollama in a separate terminal. Server lifecycle is managed transparently in the background.
Unique: Automates Ollama server startup transparently, eliminating manual terminal commands and reducing setup friction. Integrated into the extension's operation flow rather than requiring separate configuration.
vs alternatives: More convenient than requiring manual `ollama serve` commands in a terminal, but less robust than containerized solutions (Docker) that guarantee consistent server state and isolation.
Provides the extension interface in multiple languages (English, Russian, Ukrainian) through configuration. Developers can set the UI language via the `ollamaCodeFixer.language` setting, and all menus, buttons, and messages are displayed in the selected language. Localization is static (not dynamic language detection) and requires configuration change to switch languages.
Unique: Provides UI localization for non-English speaking developers, though limited to three languages. Localization is configuration-based rather than automatic.
vs alternatives: Enables non-English developers to use the extension, but language support is limited compared to major tools like VS Code itself which support 40+ languages.
Processes selected code through the local Ollama model to generate natural language explanations of what the code does, how it works, and why specific patterns are used. The extension sends code context to the model and receives human-readable explanations that help developers understand complex logic, unfamiliar patterns, or legacy code. A separate 'Add Comments' operation generates inline code comments at appropriate locations.
Unique: Generates both standalone explanations and inline comments through separate operations, allowing developers to choose between quick understanding (explanation) and persistent documentation (comments). All processing stays local, preserving code privacy.
vs alternatives: More privacy-preserving than cloud-based documentation tools, but explanations from smaller local models (7B) may lack the nuance and clarity of GPT-4-powered alternatives.
Analyzes selected code and generates unit tests using the local Ollama model, with documented support for edge case identification and coverage. The model receives the function/method as context and produces test cases covering normal inputs, boundary conditions, error states, and edge cases. Generated tests are formatted for the detected language (Jest for JavaScript, pytest for Python, etc.) and can be inserted above, below, or in a new file based on configuration.
Unique: Explicitly documents edge case coverage as a feature, attempting to generate tests beyond happy-path scenarios. Supports multiple test framework formats through language detection and configurable insertion modes.
vs alternatives: Local execution avoids API costs and code transmission compared to cloud test generators, but edge case coverage quality depends on the 7B model's training data and may miss domain-specific edge cases that developers would catch.
Sends selected code to the Ollama model with a refactoring prompt requesting structural and architectural improvements. The model suggests changes to code organization, design patterns, separation of concerns, and maintainability without changing functionality. Refactoring suggestions are presented in preview mode before application, allowing developers to review and accept changes selectively.
Unique: Focuses on structural improvements and design patterns rather than just syntax cleanup. Integrates with VS Code's preview system to allow developers to review changes before committing, with optional automatic backup of original code.
vs alternatives: Provides local, privacy-preserving refactoring suggestions compared to cloud-based tools, but lacks integration with team-specific linting rules or architectural guidelines that would make suggestions more contextually appropriate.
+5 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 Ollama Code Fixer - AI Coding Assistant at 32/100. Ollama Code Fixer - AI Coding Assistant 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