Live LLM Token Counter
ExtensionFreeLive Token Counter for Language Models
Capabilities5 decomposed
real-time local token counting with live status bar display
Medium confidenceCounts tokens for selected text or entire open document using embedded local tokenizers (tiktoken for GPT, Anthropic's official tokenizer for Claude, approximation for Gemini) with zero API calls. Updates trigger on every keystroke, selection change, or model family switch, displaying results in VS Code's status bar with customizable template formatting using {count}, {family}, {model}, and {provider} placeholders. No external dependencies or authentication required.
Uses embedded local tokenizers (tiktoken, Anthropic official tokenizer) with zero API calls, enabling instant token counting without latency or authentication overhead. Template-based status bar customization allows developers to display token counts in custom formats without code changes.
Faster and more privacy-preserving than cloud-based token counters (e.g., OpenAI Tokenizer web tool) because all processing happens locally in VS Code with no network requests; supports three major model families simultaneously with instant switching.
visual token boundary highlighting with customizable band colors
Medium confidenceRenders inline visual decorations in the editor that highlight token boundaries using alternating even/odd band colors, making token segmentation visible as you edit. Color customization is provided via a dedicated UI command that opens color pickers for even/odd token bands with hex input and opacity/alpha sliders, with live preview of contrast. Highlighting can be toggled on/off via status bar palette icon or command palette, and is editor-aware (excludes Output/Debug panes).
Provides dedicated color configurator UI with live contrast preview and per-band (even/odd) color customization, enabling theme-aware token visualization without manual color code entry. Rendering is editor-aware and excludes non-text panes.
More granular than simple monochrome highlighting because it uses alternating band colors to distinguish adjacent tokens visually; includes dedicated UI for color customization rather than requiring manual theme.json edits.
multi-model tokenizer switching with fallback chains
Medium confidenceAllows users to switch between three pre-configured model families (GPT, Claude, Gemini) via status bar click or command palette, with automatic fallback logic for tokenizer resolution. GPT uses tiktoken with fallback chain: gpt-5 encoding → o200k_base → cl100k_base. Claude uses Anthropic's official tokenizer. Gemini uses approximation (~4 chars/token) when precise tokenizer unavailable. Model selection persists in extension state and updates all displays (status bar, highlighting) instantly.
Implements automatic fallback chains for GPT tokenizers (gpt-5 → o200k_base → cl100k_base) ensuring graceful degradation when specific model encodings are unavailable. Supports three major model families with instant switching without extension reload.
Faster model comparison than using separate tools or web interfaces because switching is instant (single status bar click) and all tokenizers are embedded locally; fallback chains ensure robustness vs. hard failures.
customizable status bar token count display with template formatting
Medium confidenceDisplays token count in VS Code's status bar using a customizable template format that supports placeholders: {count} for token count value, {family} or {model} for model family name (GPT, Claude, Gemini), and {provider} for provider identifier (openai, anthropic, gemini). Template configuration is stored in extension settings (exact mechanism unspecified). Status bar element is clickable to switch model families, and includes a palette icon to toggle highlighting.
Provides placeholder-based template formatting ({count}, {family}, {model}, {provider}) for status bar display, allowing developers to customize token count presentation without code changes. Status bar element is interactive (clickable for model switching).
More flexible than fixed status bar displays because template customization allows teams to match their own conventions; interactive status bar element reduces command palette usage for model switching.
selection-aware and document-wide token analysis
Medium confidenceAnalyzes token counts for both selected text ranges and entire open documents independently. When text is selected, the extension counts only the selected range; when no selection is active, it counts the entire document. Token count updates are triggered by selection changes, typing, or model family switches. Both modes use the same underlying tokenizer (GPT, Claude, or Gemini) and display results in the status bar.
Dynamically switches between selection-based and document-wide counting based on active selection state, with real-time updates on every selection change. No explicit mode toggle required — behavior is implicit based on editor state.
More intuitive than tools requiring explicit mode selection because counting mode is automatic based on selection state; enables quick comparison of token counts across prompt sections without manual toggling.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Best For
- ✓prompt engineers iterating on LLM prompts in VS Code
- ✓developers building LLM applications who need token budgeting visibility
- ✓teams evaluating multi-model strategies (OpenAI, Anthropic, Google) with token-aware workflows
- ✓prompt engineers who need visual feedback on tokenization patterns
- ✓developers with accessibility requirements (custom contrast/color settings)
- ✓teams using dark/light theme switching who need theme-aware token visualization
- ✓multi-model teams evaluating cost/performance tradeoffs across OpenAI, Anthropic, and Google AI
- ✓prompt engineers prototyping the same prompt for multiple LLM providers
Known Limitations
- ⚠Local tokenizers only — cannot count tokens for custom or proprietary models not in the three supported families
- ⚠Gemini tokenizer uses crude ~4 chars/token approximation; no precise token boundary detection available for Google AI models
- ⚠Performance on very large documents (>100k tokens) unknown; real-time updates on every keystroke may cause latency on large files
- ⚠No project-wide token analysis — only current file and selection supported
- ⚠Highlighting visual overlays excluded from Output and Debug panes
- ⚠Gemini tokenizer does not support highlighting — only GPT and Claude models can render visual token boundaries
Requirements
Input / Output
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Live Token Counter for Language Models
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