# AI Token Estimator Analyze language mix and estimate token usage across OpenAI, Codex, Claude, and DeepSeek profiles > Canonical page: https://elysiatools.com/en/tools/ai-token-estimator - **Category:** AI - **Keywords:** ai tokens, token estimator, openai, codex, claude, deepseek, multilingual ## Overview Estimate token usage for pasted text or uploaded TXT/Markdown files. What it does: - Detects mixed language/script composition, including Chinese Han, Latin, Kana, Hangul, Cyrillic, Arabic, emoji, symbols, and code-like lines - Counts OpenAI / Codex o200k_base and OpenAI cl100k_base with an offline tokenizer - Counts Claude with Anthropic count_tokens when CLAUDE_API_KEY or ANTHROPIC_API_KEY is available, and falls back to heuristic only if the official call fails - Estimates DeepSeek token usage with transparent heuristics when exact provider token counters are unavailable - Marks each profile as exact-offline-tokenizer, official-provider-api, or heuristic so the result does not overclaim precision ## Inputs - **Input Text** (textarea): Paste English, Chinese, code, or mixed-language text here... - **Text File** (file) - **Model Profiles** (select) - **Count Mode** (select) ## When to use - When preparing large prompts or datasets containing mixed languages, code, or emojis and you need to estimate costs before sending them to LLM APIs. - When optimizing prompt lengths to fit within specific model context windows like OpenAI cl100k_base or o200k_base. - When analyzing log files, CSVs, or Markdown documents to calculate bulk token consumption for batch processing. ## How it works - Paste your text into the input area or upload a supported file format such as TXT, Markdown, CSV, JSON, or log files. - Select your target model profile (e.g., OpenAI cl100k_base, Claude, DeepSeek, or All Profiles) and choose between Raw Text or Chat Message counting modes. - The tool analyzes the script composition (such as Latin, Chinese Han, or symbols) and runs offline tokenizers or API-based estimators. - View the structured JSON output detailing character counts, detected language mix, and token estimates labeled by precision type (exact, official API, or heuristic). ## Use cases - Budgeting API costs for high-volume translation tasks involving mixed English and Chinese text. - Pre-filtering large Markdown documentation files to ensure they do not exceed Claude or OpenAI context limits. - Analyzing system log files to estimate the token footprint of debugging data before feeding it to an LLM. ## Frequently asked questions ### How accurate are the token estimates? OpenAI and Codex counts are exact using offline tokenizers. Claude uses official API counts when keys are provided, while DeepSeek and other profiles use transparent heuristic estimations. ### Can I estimate tokens for chat messages instead of raw text? Yes, you can switch the Count Mode option from Raw Text to Chat Message to simulate chat format overhead. ### What file formats does the estimator support? You can upload TXT, Markdown (MD), CSV, JSON, and log files up to 20MB. ### Does this tool support multilingual text? Yes, it automatically detects mixed scripts including Chinese Han, Latin, Kana, Hangul, Cyrillic, Arabic, emojis, and code lines. ### Are my API keys or text data stored? No, all text processing and offline tokenization happen locally, and API calls are made directly to the providers without storing your data. ## Related tools - [Text Encoding Forensics and Repair](https://elysiatools.com/en/tools/text-encoding-forensics-and-repair): Inspect text encoding, BOM, newline style and mojibake symptoms, then produce a scored local repair candidate. - [TXT File Merger](https://elysiatools.com/en/tools/txt-merger): Merge multiple text files with various strategies (concatenate, interleave, etc.) - [Structured Log Analyzer](https://elysiatools.com/en/tools/structured-log-analyzer): Detect common log formats, extract core fields, infer field types, and export parsed logs as JSON, CSV, or SQL inserts - [Time Series Forecast & Seasonality Analyzer](https://elysiatools.com/en/tools/time-series-forecast-seasonality-analyzer): Forecast future periods from CSV or JSON time-series data and inspect trend, seasonal, and residual decomposition in one report - [Messy Text to Structured Data Workbench](https://elysiatools.com/en/tools/messy-text-to-structured-data-workbench): Infer tables from pasted text, mixed delimiters, key-value lines, or HTML and export CSV, JSON, and Markdown. - [Audio Chord Progression Detector](https://elysiatools.com/en/tools/audio-chord-progression-detector): Estimate an audio file's chord sequence locally and download CSV, JSON, SVG, and method notes in one ZIP. - [Audio Silence Map](https://elysiatools.com/en/tools/audio-silence-map): Output silence intervals as JSON and CSV - [Mock Data Prefix / Abbreviation Conflict Detector](https://elysiatools.com/en/tools/mock-data-naming-conflict-detector): Detect visually confusing field names and prefix collisions in CSV, JSON, or schema inputs, then suggest clearer renames ## Samples - [PDF Samples](https://elysiatools.com/en/samples/pdf-samples): Generated PDF samples from tools dated 2026-02-01 to 2026-02-10 - [CSV Samples](https://elysiatools.com/en/samples/csv-samples): Sample CSV files with various data types, sizes, and complexity levels - [Python Samples](https://elysiatools.com/en/samples/python): Essential Python code examples and Hello World demonstrations - [JWT Samples](https://elysiatools.com/en/samples/jwt-samples): Comprehensive JWT examples from basic token structure to advanced security implementations ## Related content - [RAG Chunking and Retrieval Prep](https://elysiatools.com/en/hubs/rag-chunking-retrieval-prep): Clean PDFs and Word files, remove extraction noise, scan hidden prompt risks, split text, score chunk quality, and package citation-ready RAG inputs. - [Prompt Engineering and LLM Input Preparation](https://elysiatools.com/en/hubs/prompt-engineering-llm-input-workflows): Prepare LLM inputs by structuring prompts, checking token budgets, translating or identifying language, cleaning PDF evidence, and reviewing injection, math, regex, and data risks before use.