To count tokens for GPT-5.4, Claude 4, or Gemini 3.1 APIs, use the tiktoken Python library with the o200k_base encoding or a free online token counter. In 2026, token counting must also account for reasoning tokens, tool search tokens, and agentic planning overhead.
If you need an immediate and accurate calculation of your payload before sending it to an API, paste your prompt directly into our free AI Token Counter. It handles different encodings automatically directly in your browser.
Managing context windows used to be as simple as counting words. But in 2026, as building with LLMs transitions entirely into building agentic workflows, measuring your API usage has become significantly more complex. Models are no longer just reading text; they are using browsers, managing memories, and searching for internal tools—all of which consume tokens.
This guide is for AI engineers and developers managing multi-model ecosystems who need to accurately calculate tokens, predict costs, and optimize agentic API calls for today's frontier models.
2026 Model Specs and Token Limits
Understanding the exact specifications of the current frontier models is critical before orchestrating your requests. Below is a comparison of the key capabilities driving the 2026 market.
| Model | Context Window | Best For | Notable Token Characteristics |
|---|---|---|---|
| GPT-5.4 | 1,000,000 | Agentic workflows, Computer-use | Highly efficient o200k_base encoding, high tool-use overhead |
| GPT-5.3-Codex | 500,000 | Advanced code generation | Optimized for code block syntax and dense repositories |
| Claude 4 Opus | 1,000,000 | Complex writing and logic | Higher multi-lingual efficiency, strict structured output tokens |
| Gemini 3.1 Pro | 2,000,000 | Multi-modal, big data | Advanced reasoning, efficient video frame tokenization |
| Gemini 3 Deep Think | 500,000 | Minutes-long specialized reasoning | Internal "thinking" tokens are generated and billed alongside output |
For a quick conversion of any text to verify these limits, utilize the AI Token Counter rather than building custom validation scripts for every project.
Programmatic Token Counting (Python & JS)
While the AI Token Counter is excellent for manual debugging, you must implement local counting before hitting API endpoints to prevent context_length_exceeded errors in production. The standard for OpenAI models in 2026 is the o200k_base encoding.
Python Implementation (using tiktoken)
import tiktoken
def count_tokens_gpt5(prompt_string: str) -> int:
# Use the 2026 standard encoding for GPT-5.4
encoding = tiktoken.get_encoding("o200k_base")
num_tokens = len(encoding.encode(prompt_string))
return num_tokens
prompt = "Analyze the financial reports from Q1 to Q3 and draft a summary."
print(f"Token count: {count_tokens_gpt5(prompt)}")
JavaScript/TypeScript Implementation (using js-tiktoken)
In Edge computing environments or browser-based AI apps, use the lightweight JS port.
import { getEncoding } from "js-tiktoken";
function countTokensGPT5(promptString) {
// Initialize the o200k_base encoding
const enc = getEncoding("o200k_base");
const tokens = enc.encode(promptString);
return tokens.length;
}
const prompt = "Execute a tool search to find the latest customer invoice.";
console.log(`Token count: ${countTokensGPT5(prompt)}`);
If you format complex prompts as JSON locally, always use a JSON Formatter to ensure the structure is valid before you pass it to the tokenizer, preventing inaccurate counts due to syntax errors.
Token Counting for Agentic Workflows
In 2026, an Agent doesn't just read your prompt and reply. It plans, it searches, and it acts. All of this background activity costs tokens.
GPT-5.4 Tool Search Tokens
If you provide an agent with 50 available functions, passing the schemas of every tool consumes massive context. Furthermore, GPT-5.4 often performs a "Tool Search" internally, generating invisible planning tokens to decide which tool to use before emitting the actual function call. To optimize this, ensure your schemas are perfect using an AI JSON Schema Validator.
Computer Use Tokens
Models like GPT-5.4 natively support "Computer Use" to interact with virtual browsers or desktops. When the model "clicks" or "scrolls," the API translates the UI state into a dense grid of semantic tokens. A single screenshot analyzed by the model might cost 1,500 tokens.
Gemini 3.1 Memory Import Tokens
Gemini 3.1 features explicit state-saving. Whenever an agent boots up, you can import its semantic "Memory" of the user. While this saves time, the imported memory embeddings are translated back into the active context window, instantly eating thousands of tokens before the user has even typed a message.
5 Ways to Cut Your AI API Costs in 2026
With frontier models handling agentic tasks, bill shock is a real threat. Implement these five strategies to reduce costs:
- Prompt Caching: All major providers now offer prompt caching. If you send the same system prompt and tool definitions to Claude 4 or GPT-5.4, the API caches the tokens. You only pay a fraction of the cost for subsequent requests.
- Prune Tool Schemas: Do not pass your entire application's API surface to the model. Route the request first, and only attach the JSON schemas necessary for that specific task.
- Route Simple Tasks intelligently: Do not use Gemini 3.1 Pro for basic summarization. Route complex reasoning to big models, and route simple text transformations to cheaper, faster models like Gemini 3.1 Flash or GPT-5.4 mini.
- Redact Pre-Flight Data: Remove massive, non-semantic data like raw IDs, base64 strings, or personal data before you send it to the LLM. Use an AI PII Redactor to strip sensitive information—this improves privacy and saves tokens.
- Use Markdown over HTML: LLMs are incredibly efficient at parsing markdown. Stripping HTML tags from web-scraped data and converting it to clean Markdown can reduce your token overhead by 60%.
Frequently Asked Questions
What is the token limit for GPT-5.4?
GPT-5.4 features a massive 1,000,000 token context window, roughly equivalent to 3,000 pages of text. This massive window allows for complete codebase ingestion and long-running agentic tasks where the model must remember past actions.
Do Gemini 3 Deep Think tokens cost more?
Yes. Gemini 3 Deep Think generates internal "reasoning" or "thinking" tokens while it solves complex logic problems. These tokens are not visible in the final response but are billed to your account. You are paying for the computational time the model spends thinking.
What is the difference between input and output tokens?
Input tokens (the prompt) are the tokens you send to the API. Output tokens (the completion) are the tokens the model generates. Output tokens are significantly more computationally expensive to generate, and therefore typical 2026 pricing models charge 3x to 5x more for output tokens compared to input tokens.