Prompt Token Counter
Estimate token count and API cost for GPT-4o, Claude 3.5 Sonnet, Llama 3.1, and Gemini. See context window usage in real time.
Est. Tokens
0
Characters
0
Words
0
Lines
0
Context Window Usage
0%Input Cost Estimate
< $0.000001
0 tokens × $2.5/1M — GPT-4o
Rate
$2.50 / 1M tokens
All Models at Current Token Count
Estimates only — actual counts vary by tokenizer. Token counts use a BPE approximation (~4 chars/token for English, ~3 for code, ~1.5 for CJK).
Understanding LLM Token Counting
Tokens are the fundamental unit of input and output for large language models. Rather than processing raw characters, LLMs operate on subword units produced by a tokenizer — a vocabulary of common words, word fragments, and special characters. The tokenizer converts your text into a sequence of integer token IDs before passing it to the model. Understanding token counts matters for three practical reasons: staying within context window limits, predicting API costs, and optimizing prompt efficiency.
| Model | Context | Input Price | Provider |
|---|---|---|---|
| GPT-4o | 128k tokens | $2.50 / 1M | OpenAI |
| GPT-4 | 8k tokens | $30.00 / 1M | OpenAI |
| GPT-3.5 Turbo | 16k tokens | $0.50 / 1M | OpenAI |
| Claude 3.5 Sonnet | 200k tokens | $3.00 / 1M | Anthropic |
| Claude 3 Opus | 200k tokens | $15.00 / 1M | Anthropic |
| Llama 3.1 70B | 128k tokens | $0.59 / 1M | Groq |
| Gemini 1.5 Pro | 1M tokens | $1.25 / 1M |
Prices are indicative. Verify current rates with each provider. Output token pricing is not shown.
Reducing Token Usage in Prompts
Prompt engineering for token efficiency can meaningfully reduce API costs, especially at scale. Here are proven techniques:
Use concise system prompts
System prompts run on every call. Trim them to the minimum needed — avoid long preambles, repeated instructions, and verbose role descriptions. Each word costs.
Avoid over-specifying format
Don't ask for JSON AND describe each field AND provide examples if a simple field list works. Trust the model; add detail only when it fails without it.
Truncate retrieved context
When injecting documents, truncate to the most relevant sections. Semantic chunking + retrieval beats dumping full documents into every prompt.
Cache repeated prefix tokens
OpenAI and Anthropic offer prompt caching for long repeated prefixes. If your system prompt is >1k tokens and static across calls, caching can cut costs by ~90% on those tokens.
Choosing the Right Model for Your Use Case
Not every task needs GPT-4 or Claude Opus. Matching model capability to task complexity is the most impactful cost optimization. Simple classification, extraction, and structured output tasks work well with smaller, cheaper models like GPT-3.5 Turbo or Llama 3.1 70B. Complex reasoning, multi-step analysis, and nuanced writing benefit from frontier models but should be reserved for cases where quality meaningfully impacts outcomes.
Context window size should drive model selection for document-heavy workflows. Gemini 1.5 Pro's 1M token window makes it uniquely suited for full codebase analysis or processing entire books. Claude's 200k window handles most enterprise document processing. GPT-4's base 8k window requires careful chunking strategies for long inputs — use GPT-4o (128k) or Claude instead.
Frequently Asked Questions
How are token counts estimated?
This tool uses a BPE (Byte Pair Encoding) approximation: roughly 4 characters per token for English prose, 3 characters per token for code-heavy content, and 1.5 characters per token for Chinese/Japanese text. These ratios match the averages produced by the OpenAI tiktoken and Anthropic tokenizers for typical inputs. The real count may vary ±10–15% depending on punctuation density, rare words, and mixed-language content.
Why does the same text have different token counts for different models?
Different model families use different tokenizers. OpenAI models use tiktoken (cl100k_base for GPT-4/GPT-4o), Anthropic uses a custom BPE tokenizer, Llama uses a SentencePiece tokenizer, and Gemini uses its own. Vocabularies differ in size (32k–100k+ tokens), which affects how subwords are split. Common English words usually map 1:1 to tokens, but rare words, code identifiers, and punctuation sequences split differently. For exact counts, use each provider's official tokenizer library.
What is a context window and why does it matter?
A context window is the maximum number of tokens a model can process in a single request — including both the input (prompt) and the output (response). If your combined input + expected output exceeds the context limit, the API will return an error or truncate content. GPT-4o at 128k tokens, Claude at 200k, and Gemini 1.5 Pro at 1M tokens represent current state-of-the-art limits. Larger context windows enable processing full codebases, long documents, or multi-turn chat histories in a single call.
How is the cost estimate calculated?
Cost is calculated as: (estimated tokens ÷ 1,000,000) × price per million input tokens. Prices shown reflect each provider's public API pricing for input tokens only — output tokens are typically priced separately and often at a higher rate. The Llama 3.1 70B pricing shown uses Groq's public API rates. Always verify current pricing directly with each provider, as rates change frequently and volume discounts may apply.
What is the difference between input tokens and output tokens?
Input tokens are the tokens in your prompt — everything you send to the model. Output tokens are the tokens in the model's response. Most LLM APIs charge for both separately, with output tokens typically priced at 2–4× the input rate. This tool shows input cost estimates only because output length varies by task. A rule of thumb: budget output tokens at roughly 25–50% of your input length for summarization tasks, or up to 200% for generation and code tasks.