llm-vscode vs IntelliCode
Side-by-side comparison to help you choose.
| Feature | llm-vscode | IntelliCode |
|---|---|---|
| Type | Extension | Extension |
| UnfragileRank | 37/100 | 39/100 |
| Adoption | 1 | 1 |
| Quality | 0 | 0 |
| Ecosystem |
| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 7 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.
Provides IntelliSense completions ranked by a machine learning model trained on patterns from thousands of open-source repositories. The model learns which completions are most contextually relevant based on code patterns, variable names, and surrounding context, surfacing the most probable next token with a star indicator in the VS Code completion menu. This differs from simple frequency-based ranking by incorporating semantic understanding of code context.
Unique: Uses a neural model trained on open-source repository patterns to rank completions by likelihood rather than simple frequency or alphabetical ordering; the star indicator explicitly surfaces the top recommendation, making it discoverable without scrolling
vs alternatives: Faster than Copilot for single-token completions because it leverages lightweight ranking rather than full generative inference, and more transparent than generic IntelliSense because starred recommendations are explicitly marked
Ingests and learns from patterns across thousands of open-source repositories across Python, TypeScript, JavaScript, and Java to build a statistical model of common code patterns, API usage, and naming conventions. This model is baked into the extension and used to contextualize all completion suggestions. The learning happens offline during model training; the extension itself consumes the pre-trained model without further learning from user code.
Unique: Explicitly trained on thousands of public repositories to extract statistical patterns of idiomatic code; this training is transparent (Microsoft publishes which repos are included) and the model is frozen at extension release time, ensuring reproducibility and auditability
vs alternatives: More transparent than proprietary models because training data sources are disclosed; more focused on pattern matching than Copilot, which generates novel code, making it lighter-weight and faster for completion ranking
IntelliCode scores higher at 39/100 vs llm-vscode at 37/100. llm-vscode leads on ecosystem, while IntelliCode is stronger on adoption and quality.
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Analyzes the immediate code context (variable names, function signatures, imported modules, class scope) to rank completions contextually rather than globally. The model considers what symbols are in scope, what types are expected, and what the surrounding code is doing to adjust the ranking of suggestions. This is implemented by passing a window of surrounding code (typically 50-200 tokens) to the inference model along with the completion request.
Unique: Incorporates local code context (variable names, types, scope) into the ranking model rather than treating each completion request in isolation; this is done by passing a fixed-size context window to the neural model, enabling scope-aware ranking without full semantic analysis
vs alternatives: More accurate than frequency-based ranking because it considers what's in scope; lighter-weight than full type inference because it uses syntactic context and learned patterns rather than building a complete type graph
Integrates ranked completions directly into VS Code's native IntelliSense menu by adding a star (★) indicator next to the top-ranked suggestion. This is implemented as a custom completion item provider that hooks into VS Code's CompletionItemProvider API, allowing IntelliCode to inject its ranked suggestions alongside built-in language server completions. The star is a visual affordance that makes the recommendation discoverable without requiring the user to change their completion workflow.
Unique: Uses VS Code's CompletionItemProvider API to inject ranked suggestions directly into the native IntelliSense menu with a star indicator, avoiding the need for a separate UI panel or modal and keeping the completion workflow unchanged
vs alternatives: More seamless than Copilot's separate suggestion panel because it integrates into the existing IntelliSense menu; more discoverable than silent ranking because the star makes the recommendation explicit
Maintains separate, language-specific neural models trained on repositories in each supported language (Python, TypeScript, JavaScript, Java). Each model is optimized for the syntax, idioms, and common patterns of its language. The extension detects the file language and routes completion requests to the appropriate model. This allows for more accurate recommendations than a single multi-language model because each model learns language-specific patterns.
Unique: Trains and deploys separate neural models per language rather than a single multi-language model, allowing each model to specialize in language-specific syntax, idioms, and conventions; this is more complex to maintain but produces more accurate recommendations than a generalist approach
vs alternatives: More accurate than single-model approaches like Copilot's base model because each language model is optimized for its domain; more maintainable than rule-based systems because patterns are learned rather than hand-coded
Executes the completion ranking model on Microsoft's servers rather than locally on the user's machine. When a completion request is triggered, the extension sends the code context and cursor position to Microsoft's inference service, which runs the model and returns ranked suggestions. This approach allows for larger, more sophisticated models than would be practical to ship with the extension, and enables model updates without requiring users to download new extension versions.
Unique: Offloads model inference to Microsoft's cloud infrastructure rather than running locally, enabling larger models and automatic updates but requiring internet connectivity and accepting privacy tradeoffs of sending code context to external servers
vs alternatives: More sophisticated models than local approaches because server-side inference can use larger, slower models; more convenient than self-hosted solutions because no infrastructure setup is required, but less private than local-only alternatives
Learns and recommends common API and library usage patterns from open-source repositories. When a developer starts typing a method call or API usage, the model ranks suggestions based on how that API is typically used in the training data. For example, if a developer types `requests.get(`, the model will rank common parameters like `url=` and `timeout=` based on frequency in the training corpus. This is implemented by training the model on API call sequences and parameter patterns extracted from the training repositories.
Unique: Extracts and learns API usage patterns (parameter names, method chains, common argument values) from open-source repositories, allowing the model to recommend not just what methods exist but how they are typically used in practice
vs alternatives: More practical than static documentation because it shows real-world usage patterns; more accurate than generic completion because it ranks by actual usage frequency in the training data