AI Token Counter
Count tokens and estimate API costs for 100+ LLMs with exact BPE and provider tokenization.
Free online AI token counter and LLM tokenizer. Accurately count tokens and estimate per-request API costs across leading models including GPT-5, GPT-4o, Claude 3.7 Sonnet, Claude 3.5 Haiku, Gemini 2.5 Pro, Gemini 2.0 Flash, DeepSeek V3, Llama 3.3, and Mistral. Compare token consumption across 100+ AI models side-by-side to optimize prompt engineering, prevent context window overflow, and budget API inference spend. Uses Byte-Pair Encoding (BPE) tokenization rules and official provider rate cards. Runs 100% client-side in your browser for zero-telemetry privacy.
Keywords: token counter, ai token counter, tiktoken, llm tokens, openai tokens, claude tokens, gpt-4o tokens, gpt-5 tokens, token cost, token counter online, count tokens, token estimator, gemini tokens, llama tokens, deepseek tokens
Tags: tokens, ai, llm, tokenizer, count, estimate, pricing, bpe
Popular Developer Tools tools
- JWT DecoderDecode, inspect, and validate JWT tokens with claim and signature analysis.
- Cron ParserParse, validate, explain, and build cron expressions with next run times and visual timeline.
- Regex TesterTest, debug, and explain regular expressions with real-time match highlighting.
- Regex ExplainerBreak down any regex into plain English, token by token.
- JSON to TypeScript & Schema GeneratorGenerate TypeScript interfaces, Zod schemas, and Valibot schemas from JSON.
AI Token Counter is also known as: LLM Tokenizer, AI Token Calculator, Tiktoken Counter, OpenAI Token Counter, Claude Token Counter, Gemini Token Counter, BPE Tokenizer, Prompt Token Calculator, Context Window Calculator, LLM Token Estimator.
How to AI Token Counter Online
Paste or type your input text, system prompt, JSON schema, or code snippet into the input editor on the left.
Select your target AI model (e.g., GPT-5, GPT-4o, Claude 3.7 Sonnet, Gemini 2.5 Pro, DeepSeek V3, Llama 3.3) from the model selector dropdown.
Review real-time token counts, character lengths, word counts, line counts, and sentence breakdowns calculated instantly as you type.
Inspect dual-tier API cost estimates calculated per request for both input ingestion and output completion based on official vendor rate cards.
Check the context window gauge to monitor what percentage of the selected model's maximum token context limit your input consumes.
Click "Compare all models" or press ⌘⇧A to open the full cross-model matrix and evaluate tokenization efficiency across 100+ AI architectures.
Click "Model in AI Cost Calculator" to transfer your exact token counts and project monthly API bills with prompt caching and batch discounts.
AI Token Counter Features
Multi-Provider Real-Time Tokenization: Instantly count tokens across 121+ frontier, fast, and open-weight LLMs from OpenAI, Anthropic, Google, Meta, DeepSeek, Mistral, xAI, Cohere, Zhipu, and Qwen.
Byte-Pair Encoding (BPE) Precision: Uses production-grade subword tokenization heuristics modeled after OpenAI o200k_base and cl100k_base encodings for exact prompt budgeting.
Dual-Tier Per-Request Cost Modeling: Accurately computes input token ingestion and output completion generation costs using published per-1M-token API rate cards.
Full Cross-Model Comparison Matrix: Benchmark token counts, input costs, and output costs across all supported models side-by-side in a unified sortable table (⌘⇧A).
Context Window Utilization Gauges: Displays exact context window limits (from 4k to 2,000,000+ tokens) and visual percentage usage to prevent context overflow errors.
Granular Text Metrics & Token Density: Simultaneously tracks characters, words, lines, sentences, and average characters-per-token density for prompt optimization.
Code & Structured Data Optimization: Accurately benchmarks how indentation whitespace, JSON formatting, YAML keys, and programming syntax impact token consumption.
Keyboard-First Power Shortcuts: Accelerate prompt engineering with ⌘↵ to count, ⌘⇧A to toggle full model comparison, ⌘⇧K to clear input, and ⌘⇧C to copy results.
Zero-Telemetry Client-Side Privacy: All tokenization, character counting, and pricing calculations execute entirely in your browser with no network transmission.
Integrated AI Pipeline Ecosystem: Directly transition token metrics into the AI Cost Calculator to model prompt caching, batch rates, and monthly spend.
Supported Formats & Dialects
The AI Token Counter supports 6 syntax formats and dialects for accurate parsing and processing.
- OpenAI o200k_base Encoding (GPT-5, GPT-4o, o1, o3-mini)
- A 200,000-token BPE vocabulary engineered for high token efficiency across non-English languages and programming source code, reducing token counts by 15–20% compared to legacy tokenizers.
- OpenAI cl100k_base Encoding (GPT-4, GPT-3.5 Turbo, Embeddings)
- The foundational 100,000-token BPE vocabulary used across GPT-4 and text-embedding-3 models, establishing the standard ~4 characters per token baseline for English prose.
- Anthropic Claude Tokenizer (Claude 3.7 Sonnet, 3.5 Haiku, 3 Opus)
- A proprietary subword tokenizer optimized for natural language clarity, structured XML tag hierarchy (<antThinking>, <context>), and complex multi-turn system instructions.
- Google Gemini SentencePiece Unigram (Gemini 2.5 Pro, 2.0 Flash, 1.5 Pro)
- A language-agnostic subword tokenizer supporting ultra-large context windows up to 2,000,000 tokens with optimized token allocation for multimodal text and structured JSON outputs.
- Meta Llama & DeepSeek BPE (Llama 3.3, Llama 4, DeepSeek V3, DeepSeek R1)
- 128,000-token Tiktoken-style BPE architectures designed for open-weight reasoning models, chain-of-thought traces, and high-throughput self-hosted inference servers.
- Mistral & Qwen Multilingual Tokenizers (Mistral Small, Qwen 2.5 Coder, Qwen 3)
- Expanded vocabulary tokenizers with specialized byte-level fallback for resilient code generation, multi-language translation, and low token inflation on Asian scripts.
In-Depth Technical Guides
All GuidesClean HTML to Markdown for LLM & RAG Pipelines: Token Minimization & Semantic Table Extraction
Strip DOM boilerplate, convert complex HTML tables and semantic trees into clean Markdown, slash RAG embedding token costs by 60–80%, and preserve critical context for LLMs.
JSONL vs JSON: Streaming Large Datasets, LLM Fine-Tuning & NDJSON Best Practices
Understand the differences between JSON and JSON Lines (JSONL / NDJSON). Learn how to stream massive datasets, format AI training data, and process lines in Python and Node.js.
LLM API Cost Optimization: Prompt Caching, Model Routing & Token ROI
Slash your OpenAI, Anthropic, and Gemini API bills by 60–80%. Master prompt caching, tiered model routing, token budgeting, and batch processing economics.
LLM Token Counting & Context Window Budgeting: BPE Tokenizers & Cost Optimization
Understand BPE tokenization, calculate prompt vs output token consumption, utilize prompt caching, and budget context windows to optimize AI inference costs.
Mastering LLM Structured Outputs: Enforcing JSON Schema in Production
Master structured outputs with LLMs using JSON Schema Draft 2020-12, OpenAI strict schemas, Anthropic tool definitions, and token-optimized validation pipelines.
Prompt Injection Scanning: A Practical Security Checklist
A developer security guide to identifying direct, indirect, and multi-turn prompt injection vulnerabilities in LLM applications and agent pipelines.
Repairing Malformed & AI-Generated JSON: Field Guide to Syntax Recovery
Learn how to repair broken JSON from LLM outputs, APIs, and configs. Master automated AST recovery for trailing commas, single quotes, bare keys, and truncated streams.
Related Standards & RFC Specifications
All StandardsAI tokens are the fundamental atomic units of text and code processed by Large Language Models, governing pricing, context limits, and tokenization.
Byte-Pair Encoding (BPE) is a subword tokenization algorithm used by LLMs like GPT-4o, Claude, and Llama to convert text and code into numeric token vectors.
Prompt caching enables Large Language Models to reuse pre-computed attention states across API requests, cutting input costs by 50% to 90% and reducing latency.
Prompt engineering is the practice of structuring, refining, and designing inputs for Large Language Models to maximize accuracy, consistency, and alignment.
Prompt injection is an AI security vulnerability where untrusted inputs subvert system instructions, leading to data exfiltration, jailbreaks, or unauthorized tool calls.
Explore Full AI Model Pricing Directory
Compare per-token rates, prompt caching discounts, and context windows across leading LLMs (GPT-4o, Claude 3.5 Sonnet, Gemini 2.5 Flash, DeepSeek, and more) in our verified catalog.
Pre-built Automation Pipelines
Chain AI Token Counter with other utilities in a multi-step visual workflow.
Base64 Decode → JSON Format
Decode a Base64 string and pretty-print the JSON inside it.
CSV → JSON → YAML
Convert CSV data to JSON, then to YAML format.
JSON Format → TypeScript Schema
Format JSON and generate TypeScript/Zod schema from it.
Example Input & Output
Sample ReferenceInteractive Example: AI Token Counter in ActionShow example
Sample Input (text)
Explain quantum computing in simple terms with a real-world analogy.Sample Output
[Processed output for AI Token Counter]
Input transformed successfully using token counter, ai token counter, tiktoken.What happened:
Count tokens and estimate API costs for 100+ LLMs with exact BPE and provider tokenization.
Frequently Asked Questions
- How does Byte-Pair Encoding (BPE) tokenize natural language and source code?
- Byte-Pair Encoding (BPE) is a subword tokenization algorithm that builds a vocabulary by iteratively merging the most frequent character pairs in a training corpus into unified tokens. Common English words (e.g., "developer" or "system") condense into a single token ID. Rare terms, domain jargon, and source code identifiers (e.g., "calculateTaxRateWithDeductions") are decomposed into subword chunks (["calculate", "Tax", "Rate", "With", "De", "du", "ctions"]). Because code contains syntax symbols, camelCase names, and indentation spaces, it typically produces a higher token-to-character ratio (~2.5–3 characters per token) than fluent prose (~4 characters per token).
- Why do different AI models produce different token counts for the identical prompt?
- Each AI provider trains their own custom tokenizer with distinct vocabulary sizes and merge rules. OpenAI's o200k_base uses a 200,000-token vocabulary, whereas GPT-4's cl100k_base uses 100,000 tokens, and Anthropic, Google, and Meta maintain separate proprietary dictionaries. A larger vocabulary allows a model to represent longer words, phrases, and non-English characters in fewer discrete tokens. Consequently, pasting the exact same 1,000-word text may yield 1,250 tokens in GPT-4, 1,120 tokens in GPT-4o, and 1,180 tokens in Claude Sonnet.
- How do spaces, tabs, newlines, and JSON/YAML formatting impact token consumption?
- In subword tokenizers, whitespace is an explicit component of token representations. Leading spaces (such as " the" vs "the") are separate tokens in vocabulary dictionaries. In programming languages and structured formats (JSON, YAML, Python), indentation matters significantly: while some tokenizers merge sequences of 2, 4, or 8 spaces into single indentation tokens, non-standard indentation or mixed tabs can cause each individual whitespace character to consume a full token. Minifying JSON payloads by stripping unnecessary whitespace before sending them to LLMs can save 15% to 30% of input tokens.
- What are hidden wrapper tokens and delimiters like <|im_start|> and role headers?
- When you send a prompt via an API (such as the Chat Completions or Messages endpoint), providers automatically wrap each message turn with special structural tokens (e.g., `<|im_start|>system\n`, `<|im_start|>user\n`, `<|im_end|>\n`). In OpenAI ChatML and Anthropic Message formats, every message turn incurs 3 to 4 overhead tokens in addition to the text content itself. If your application sends 20 short conversation turns, the message envelope alone consumes 60–80 input tokens before accounting for user words or tool call definitions.
- How do reasoning tokens work in thinking models like OpenAI o1/o3 and DeepSeek R1?
- Reasoning models generate internal Chain-of-Thought (CoT) tokens before producing the visible response. While these reasoning tokens are not always displayed in the final user message, they count directly toward the model's maximum output token limit (max_completion_tokens) and are billed at full output generation rates. When budgeting requests for reasoning models, allocate a larger output token headroom (often 4,000 to 16,000 tokens) to prevent the reasoning process from exhausting the context window before writing the final answer.
- Why do non-English languages consume more tokens than English (Token Inflation)?
- Because most foundational training datasets are predominantly English, tokenizer vocabularies allocate single token IDs to common English words and syllables. Non-Latin scripts (such as Cyrillic, Arabic, Devanagari, Japanese Kanji, or Chinese Hanzi) frequently lack multi-character tokens in older tokenizers and must be broken down into individual Unicode bytes. This phenomenon, known as "token inflation," can cause non-English text to consume 2x to 5x more tokens for the exact same semantic meaning. Modern tokenizers like OpenAI o200k_base and Qwen 2.5 substantially alleviate this by expanding non-English vocabulary allocations.
- How can developers prevent context window overflow and context_length_exceeded errors?
- To prevent `400 context_length_exceeded` errors in production pipelines: (1) Use this Token Counter to calculate exact prompt sizes before dispatching requests; (2) Implement sliding window conversation memory by summarizing turns older than N messages; (3) Trim unparsed JSON API responses and RAG document chunks down to essential schema keys; (4) Ensure your input tokens plus `max_tokens` (or `max_completion_tokens`) reservation does not exceed the model's physical context limit.
- What is token density and how does it help evaluate prompt efficiency?
- Token density is the ratio of characters per token (Total Characters / Total Tokens). In standard English prose, token density typically ranges between 3.8 and 4.2 characters per token. In source code, dense math, or heavily punctuated JSON, density often drops to 2.2–3.0 characters per token. Monitoring token density helps identify token-inefficient prompt sections—such as redundant XML tags, repetitive delimiters, or unminified data structures—that inflate inference bills.
- How does the AI Token Counter connect with the AI Cost Calculator and Prompt Caching?
- The AI Token Counter provides immediate single-prompt token counts and per-call baseline costs. When planning production systems, click "Model in AI Cost Calculator" to carry your input and output token counts into our comprehensive pricing simulator. There you can model multi-turn daily request volumes (e.g., 50,000 calls/day), simulate 50% to 90% savings from Anthropic and OpenAI Prompt Caching, evaluate 50% Batch API discounts, and export audit-ready CSV reports.
- Are my prompts, proprietary code snippets, or system instructions stored or logged?
- No. DevFlow's AI Token Counter executes 100% client-side in your web browser using JavaScript. Your input text, prompt templates, API payloads, and source code are never transmitted to any external server, stored in any database, or used for AI training. You can safely inspect proprietary enterprise code, sensitive customer data, and system prompts with complete confidential privacy.
Related Developer Tools
- AI Cost CalculatorModel and compare LLM API spend across 120+ providers for any workload with prompt caching and batch pricing.
- AI Prompt BuilderBuild structured LLM prompts with per-section token counting, variable injection, and provider-aware exports for OpenAI, Anthropic, and Google.
- LLM JSON Schema GeneratorGenerate JSON Schema for LLM structured outputs — OpenAI, Anthropic, Gemini, Ollama.
- Prompt Injection & Secret ScannerScan prompts, code, and text for prompt injection patterns, secret leaks, unsafe instructions, and PII exposure.
- MCP Schema ValidatorValidate, format, test, and debug Model Context Protocol (MCP) server schemas and tool definitions.
- JSON Repair ToolRepair and fix malformed JSON data from AI outputs, API responses, and copy-paste.
- JSONL ConverterConvert between JSONL (JSON Lines / NDJSON) and standard JSON arrays with auto-detection.
- HTML to MarkdownConvert HTML to Markdown with support for GFM, CommonMark, and Obsidian syntax.
- cURL to Code ConverterConvert cURL commands to idiomatic code across 14 programming languages instantly.