AI Tools

Estimate token counts for PDFs, Markdown, JSON, HTML, source code, and CSV files. Compare token usage and input costs across GPT-4o, Claude, Gemini, and more. Runs 100% in browser - files never leave your device.

Estimate LLM token counts from character counts for quick API budgeting. Apply model-specific chars-per-token ratios for prose and code, compare across 10 models, and forecast monthly API costs.

Visualize LLM context window utilization. Break prompts into named slots (system prompt, history, RAG, user message, output reserve), see remaining capacity, and compare payload fits across GPT-4o, Claude, Gemini, DeepSeek, and more.

Count tokens, words, characters, and sentences from any text. Compare token estimates across GPT-4o, Claude, Gemini, DeepSeek, Grok, and Mistral. See context window usage and estimated API costs. Runs 100% in browser.

Estimate token counts from word counts using model-specific word-to-token ratios for GPT, Claude, Gemini, DeepSeek, Llama, and Mistral. See API cost projections, context window usage, and cross-model comparisons. Runs 100% in browser.

Estimate knowledge base vector index storage, chunk count, embedding ingestion costs, and multi-month growth projections for RAG pipelines. Compare 9 embedding models and 7 vector databases.

Analyze how much of a model's context window is consumed by input data. Break prompts into named segments and get per-segment percentage breakdowns for GPT-4o, Claude, Gemini, Mistral, and more.

Estimate how many chunks your document dataset produces for RAG pipelines. Compare fixed-size, sliding window, sentence, paragraph, recursive, and semantic chunking strategies. Calculate embedding costs and vector storage.

Find the optimal chunk size and overlap for RAG pipelines. Configure document length, content type, embedding model limits, and LLM context windows to get quality scores, context budget breakdowns, and embedding cost projections.