How to Count Tokens for GPT-4o, Claude Opus and Gemini (And

Knowing how to count tokens for GPT-4o, Claude Opus and Gemini is one of the most practical skills any developer or AI builder can have. Tokens determine what you can send to a model, how much context it retains and, most importantly, how much each API call costs. This guide walks you through the mechanics of token counting, the best free tools available and concrete strategies to reduce your spend.

How Token Counting Works Across Models

Every large language model (LLM) converts text into numeric units called tokens before processing it. A token is not exactly a word. It is more like a chunk of characters, and the exact chunking depends on the algorithm the model uses.

BPE Tokenization vs Other Algorithms

OpenAI's GPT-4o uses Byte Pair Encoding (BPE), specifically the cl100k_base vocabulary. BPE starts with individual characters and repeatedly merges the most frequent adjacent pairs until it reaches a target vocabulary size. Common English words like "the" or "and" become single tokens. Rare or technical words get split into two or more tokens.

Anthropic's Claude Opus uses a similar BPE-style tokenizer but with a different vocabulary, which means the same text can produce a slightly different token count. Google's Gemini models use SentencePiece, a subword tokenization method that handles multilingual text especially well. As a rough rule: 1,000 tokens converts to approximately 750 words in English, though this ratio shifts with code, numbers and non-Latin scripts.

These differences matter. A 500-word prompt might cost 650 tokens on GPT-4o, 670 tokens on Claude Opus and 640 tokens on Gemini 3.1 Pro. Multiply that across millions of API calls and the gap becomes significant.

Comparing Token Limits and Context Windows

The context window is the maximum number of tokens a model can process in a single request, including both your input and the model's output. Exceeding this limit causes the API to reject the request or truncate content silently.

GPT-4o: 128,000 token context window, with a maximum output of 4,096 tokens per response
Claude Opus (Claude 4.7): 200,000 token context window, making it the leader for long-document analysis
Gemini 1.5 Pro / Gemini 3.1 Pro: Up to 1,000,000 tokens, designed for extremely long contexts like entire codebases
GPT-4o mini: 128,000 token context window at a significantly lower cost per token
GPT-5.5: Context window details are model-version dependent; check OpenAI's documentation for the latest figures

Understanding the maximum token count in GPT-4o (128,000) versus Claude Opus (200,000) helps you choose the right model for a task. For summarizing a 300-page legal document, Claude Opus fits the entire text in one call. GPT-4o would require chunking the same document into segments.

Top Free Token Calculators for GPT-4o, Claude and Gemini

You do not need to write code to count tokens. Several free online tools handle this instantly and many run entirely in your browser, keeping your data private.

The Best Tools Available Right Now

token-calculator.net supports GPT-5.5, Claude Opus 4.7 and Gemini 3.1 Pro. It uses an accurate BPE tokenizer for inputs, cached inputs and outputs, and it also estimates API costs in real time. This is a strong choice for a quick OpenAI token calculator check.

runcell.dev/tool/token-counter covers 20-plus models including GPT-5, GPT-4o, Claude 4 and Gemini 3. It runs entirely in the browser with no data sent to a server, which matters when you are working with sensitive content. It also displays context window limits side by side for easy comparison.

gptforwork.com/tools/tokenizer functions as a tokenizer playground where you can visualize exactly how text is broken into tokens. This is particularly useful when you want to understand why a prompt is longer than expected. Learn more about optimizing prompts for specific AI models by seeing the tokenization breakdown visually.

Each of these tools acts as a Claude token calculator, a Gemini token counter online and an OpenAI token calculator in one place. The key difference between them is whether they support cost estimation and how many models they cover.

Estimating API Costs with a Token Cost Calculator

Token counting without cost context is only half the picture. The real value comes from mapping token counts to dollar amounts before you run large workloads.

Cost Per Use Case: What to Expect

Pricing models differ by provider. OpenAI charges separately for input tokens and output tokens. Anthropic follows the same structure for Claude. Google's pricing for Gemini varies by context length, with requests under 128,000 tokens priced lower than those above that threshold.

Here is a practical comparison across common use cases:

Customer support chatbot (50 turns/day, avg 200 tokens per turn): roughly $0.01 per day on GPT-4o mini, closer to $0.15 on GPT-4o standard
Document summarization (10,000 tokens input, 500 tokens output): approximately $0.03 on GPT-4o, $0.075 on Claude Opus
Code generation (2,000 tokens input, 1,000 tokens output): about $0.012 on GPT-4o mini, $0.018 on Gemini 1.5 Pro

A file token counter is especially useful when you are about to send an entire document or codebase to the API. Counting the tokens first tells you whether you are about to spend $0.05 or $5.00 on that single call. See how to estimate monthly API costs for production workloads to build a realistic budget before deploying.

Prompt caching is another cost lever. Both Anthropic and OpenAI offer cached input pricing, where repeated system prompts or context passed in multiple requests cost less after the first call. A token cost calculator that accounts for cached inputs, like token-calculator.net, gives you a more accurate projection.

Programmatic Token Counting: Code Examples

When you build applications, you need to count tokens in code, not by pasting text into a web tool. Here are working examples for the three major providers.

Counting Tokens for GPT-4o in Python

OpenAI publishes the tiktoken library specifically for this. Install it with pip install tiktoken, then use the following:

import tiktoken

encoder = tiktoken.get_encoding("cl100k_base")
text = "How many tokens is this sentence?"
tokens = encoder.encode(text)
print(f"Token count: {len(tokens)}")

This gives you an exact count using the same BPE tokenizer that GPT-4o uses. For GPT-5.5, check OpenAI's documentation for the correct encoding name, as it may differ.

Counting Tokens for Claude Opus via the Anthropic API

Anthropic does not publish a standalone tokenizer library. Instead, you call the count_tokens endpoint directly:

import anthropic

client = anthropic.Anthropic()
response = client.messages.count_tokens(
    model="claude-opus-4-5",
    messages=[{"role": "user", "content": "How many tokens is this?"}]
)
print(f"Token count: {response.input_tokens}")

Counting Tokens for Gemini in Python

Google provides token counting through the google-generativeai SDK:

import google.generativeai as genai

genai.configure(api_key="YOUR_API_KEY")
model = genai.GenerativeModel("gemini-1.5-pro")
result = model.count_tokens("How many tokens does Gemini see here?")
print(f"Token count: {result.total_tokens}")

Explore our guide to building token-aware API wrappers in Python for production-ready patterns that track cumulative token usage across sessions.

Handling Image Tokens in Vision Models

GPT-4o, Claude Opus and Gemini all accept images as input, but images consume tokens too. The token cost for images is not fixed. It depends on image resolution and, for GPT-4o, the detail level you specify.

GPT-4o calculates image tokens by dividing the image into 512x512 tiles. A low-detail image costs a flat 85 tokens. A high-detail 1024x1024 image costs approximately 765 tokens. Sending 10 high-resolution screenshots in one request can easily add 7,000 tokens before you write a single word of your prompt.

Gemini uses a similar tile-based approach. Images up to 384x384 pixels cost 258 tokens. Larger images scale proportionally. Claude Opus charges by image size in a comparable way, with a base cost of around 1,500 tokens for a standard resolution photo.

The practical takeaway: resize images before sending them to the API when you do not need full resolution for your task. Dropping a 2048x2048 image to 768x768 can reduce its token cost by more than 70 percent.

Optimizing Prompts to Reduce Token Usage

Counting tokens is only valuable if you act on the data. Here are specific techniques to reduce token consumption without degrading output quality.

Use system prompt caching: Pass your system instructions once and cache them; OpenAI and Anthropic both offer discounted rates for cached tokens on repeated calls
Trim whitespace and formatting: Extra blank lines, markdown decorators and redundant labels add tokens with no semantic value
Summarize long contexts: Instead of passing entire conversation histories, summarize older turns into a compact paragraph before appending new messages
Choose the right model for the job: GPT-4o mini and Gemini Flash handle simple classification and extraction tasks at a fraction of the cost of full models

A Gpt 5 token calculator or Claude token calculator helps you A/B test prompt variants before deploying. Write two versions of a system prompt, count the tokens in both and compare the projected monthly cost at your expected call volume. Small optimizations at the prompt level compound into meaningful savings at scale.

Frequently Asked Questions

How do you count tokens for Claude models?

The most reliable method is to use Anthropic's official count_tokens API endpoint, which returns an exact count for any message you plan to send. For quick estimates without an API call, tools like runcell.dev and token-calculator.net both support Claude 4.7 and Claude Opus with accurate approximations. Remember that Claude uses a different vocabulary from GPT-4o, so the same text may tokenize differently between the two.

How do you count Gemini tokens?

Google's google-generativeai Python SDK includes a count_tokens() method that returns exact token counts for text and multimodal inputs. For browser-based counting without code, Gemini token counter online tools like runcell.dev support Gemini 3 and Gemini 1.5 Pro. Gemini's SentencePiece tokenizer handles multilingual text differently from BPE, so always verify counts when working with non-English content.

What is the maximum token count in GPT-4o?

GPT-4o supports a context window of 128,000 tokens. This includes both your input (prompt, system message, conversation history and any images) and the model's output. The maximum output length per response is 4,096 tokens by default, though this can be configured. If your combined input and intended output exceeds 128,000 tokens, you need to either chunk the content or switch to a model with a larger context window, such as Claude Opus at 200,000 tokens.

Is 1,000 tokens equal to 750 words?

Approximately, yes. The ratio of 1,000 tokens to around 750 words holds reasonably well for standard English prose. However, code, JSON, numbers and non-Latin scripts can shift this ratio significantly. A 1,000-token block of Python code might represent only 500 words, while the same count of Japanese text might represent far fewer characters. Always use a token counter for precise measurements rather than relying on word counts alone.

Can you count tokens for files and documents, not just text snippets?

Yes. A file token counter works by reading the file content and passing it through the appropriate tokenizer. Tools like token-calculator.net allow you to paste large amounts of text. For programmatic workflows, you can read a file's contents in Python and pass the string directly to tiktoken for GPT models or the Anthropic/Google SDKs for Claude and Gemini respectively.

Final Thoughts

Learning how to count tokens for GPT-4o, Claude Opus and Gemini gives you precise control over two things that matter most in production: context management and cost. The three providers each use slightly different tokenization approaches, which means token counts are not interchangeable across models. Always measure with the correct tool for the model you are targeting.

Start by integrating a token counter into your development workflow before you deploy. Use tiktoken for OpenAI models, the Anthropic API for Claude and Google's SDK for Gemini. Combine these counts with a token cost calculator to project your monthly API spend at realistic call volumes. Then apply the prompt optimization techniques above to reduce waste, starting with system prompt caching and image resizing, which deliver the highest return for the least effort.

The difference between an unoptimized and an optimized prompt strategy can easily be 40 to 60 percent lower API costs at scale. Token counting is where that optimization begins. Pick one of the free tools mentioned here, run your prompts through it today and start making data-driven decisions about every token you send.

Try the ToolsVela tools mentioned in this guide

All of these run in your browser — no signup, no uploads, completely free.

Token Calculator — Estimate token counts and cost for any LLM in real time.
Words to AI Models Token Calculator — Calculate cost from word counts before writing.
Context Window Calculator — Check how much context space your model has left.
LLM Output Tester — Test regex extraction on sample LLM outputs.

Browse all 4 free tools →

How to Count Tokens for GPT-4o, Claude Opus and Gemini (And Cut Your API Costs)