LLM Context Window Token Counter
Estimate token counts for GPT-4, Claude, Gemini, and other LLM models before sending API requests. This free context window validator helps developers avoid costly API errors by checking if your prompt, document, or code fits within model token limits. Works entirely in your browser your data never leaves your device. Use Token Counter when you need answers fast during debugging, reviews, or incident triage. Paste your input, validate the output, then copy results into tickets or docs in seconds. Most processing runs in your browser, so you can test safely without unnecessary data exposure. Built for speed, clarity, and repeat use. Method details for Token Counter Calculator: Processing follows explicit developer-facing rules for api payload shape, json/yaml structure, schema validation, and when applicable regex, hash, and checksum behavior.
Context Window Analysis
Note: Token estimates use the industry-standard approximation of ~4 characters per token for English text. Actual token counts may vary by ±15% depending on language, code syntax, and special characters. For precise counts, use the official tokenizer for your target model.
How to Count LLM Tokens
- Select Your Model - Choose from GPT-4, Claude 3, Gemini, or Llama 2 to set the correct context window limit.
- Paste Your Content - Input your prompt, code snippet, or document text into the editor.
- Check Token Count - Instantly see the estimated token usage and percentage of the context window used.
- Adjust & Optimize - Edit your text to fit within limits or switch models to accommodate larger payloads.
Why Check Token Counts?
Prevent API Errors: Sending a request that exceeds the model's context window (e.g., 8k tokens for GPT-4) results in an immediate 400 Bad Request error. Validating beforehand saves debugging time.
Cost Optimization: LLM APIs charge by the token. Understanding your token usage helps you estimate costs accurately and optimize prompts to be more concise without losing meaning.
Model Selection: By knowing your exact token count, you can choose the most cost-effective model that fits your data (e.g., using GPT-3.5 Turbo for smaller tasks vs. Claude 3 Opus for massive contexts).