Introduction: Why Token Calculations Go Wrong (And How to Fix Them)
I remember the first time I built an AI-powered application — I was excited to integrate a language model, confident that my billing would be straightforward. Three hours later, I discovered I had overspent by 340% because I misunderstood how tokens are counted. That painful lesson taught me that token calculation errors are among the most common (and costly) mistakes developers make when working with AI APIs. In this comprehensive guide, I will walk you through every aspect of token troubleshooting, from understanding what tokens actually are to implementing robust calculation logic in your applications. Whether you are a complete beginner or an experienced developer, you will find actionable solutions to eliminate billing surprises and optimize your API costs forever.
If you are just starting out, I recommend Sign up here for HolySheep AI — their platform offers transparent pricing at ¥1=$1 with WeChat and Alipay support, achieving sub-50ms latency while saving you 85%+ compared to competitors charging ¥7.3 per dollar.
Understanding Tokens: The Foundation of AI API Billing
What Are Tokens in AI APIs?
Before diving into troubleshooting, you must understand what tokens are. In natural language processing, a token is the basic unit of text that AI models process. Think of it as a "word chunk" — though the reality is more nuanced. A token can be a full word, a partial word, punctuation, or even a space. The key insight for beginners is this: not all text equals one token. The word "characterization" might be one token or three, depending on the model's tokenization scheme.
For English text, a rough approximation is that 1 token equals approximately 4 characters or about 0.75 words. However, this varies significantly across languages and writing styles. Chinese characters, for example, are typically encoded as individual tokens or in pairs, making the relationship between characters and tokens fundamentally different from English. This is why token calculation errors frequently occur when developers build applications for multilingual users without accounting for these differences.
Why Token Counting Matters for Your Budget
Every AI API call has two components that consume tokens: input tokens (the text you send to the model) and output tokens (the text the model generates). Both are billed, often at different rates. Understanding this distinction is crucial because it directly impacts your costs. When I built my first production application, I naively assumed that the response length would be the only variable cost — I was completely wrong.
Here are the 2026 output pricing benchmarks you should know:
- GPT-4.1: $8.00 per million tokens
- Claude Sonnet 4.5: $15.00 per million tokens
- Gemini 2.5 Flash: $2.50 per million tokens
- DeepSeek V3.2: $0.42 per million tokens
As you can see, the price difference between the most expensive and most economical options is nearly 20x. HolySheep AI's unified pricing at $1 per dollar (with ¥1=$1) combined with support for all major providers means you can access these models through a single, predictable billing system with free credits upon registration.
Setting Up Your Development Environment for Token Debugging
Prerequisites and Tools You Need
To follow this tutorial effectively, you need a basic Python environment and access to an AI API provider. I will demonstrate using HolySheheep AI's platform because it provides transparent usage reporting and built-in token monitoring — features that make troubleshooting significantly easier for beginners.
First, install the required Python packages. Open your terminal and run the following command:
pip install requests tiktoken openai anthropicThe tiktoken library is particularly important because it implements OpenAI's tokenizer, allowing you to count tokens locally before making API calls. This is your first line of defense against billing surprises.
Configuring Your API Client
Now let us set up a properly configured API client with built-in token tracking. Create a new Python file called token_debugger.py and add the following code:
import requests
import tiktoken
import json
from datetime import datetime
HolySheep AI Configuration
BASE_URL = "https://api.holysheep.ai/v1"
API_KEY = "YOUR_HOLYSHEEP_API_KEY" # Replace with your actual key
class TokenDebugger:
"""A comprehensive token tracking utility for AI API calls."""
def __init__(self, api_key: str):
self.api_key = api_key
self.encoding = tiktoken.get_encoding("cl100k_base")
self.request_history = []
def count_tokens_local(self, text: str, model: str = "gpt-4") -> dict:
"""Count tokens locally using tiktoken before making API calls."""
token_ids = self.encoding.encode(text)
token_count = len(token_ids)
# Estimate cost based on 2026 pricing
pricing = {
"gpt-4.1": {"input": 2.00, "output": 8.00},
"claude-sonnet-4.5": {"input": 3.00, "output": 15.00},
"gemini-2.5-flash": {"input": 0.10, "output": 2.50},
"deepseek-v3.2": {"input": 0.10, "output": 0.42}
}
# Default pricing if model not found
default_pricing = {"input": 1.00, "output": 5.00}
rates = pricing.get(model, default_pricing)
estimated_cost = (token_count / 1_000_000) * rates["output"]
return {
"text": text[:50] + "..." if len(text) > 50 else text,
"token_count": token_count,
"character_count": len(text),
"word_count": len(text.split()),
"estimated_cost_usd": round(estimated_cost, 6)
}
def make_chat_request(self, messages: list, model: str = "gpt-4.1") -> dict:
"""Make a chat completion request with full token tracking."""
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
# Calculate input tokens from all messages
total_input_tokens = 0
for msg in messages:
msg_tokens = self.encoding.encode(str(msg))
total_input_tokens += len(msg_tokens)
payload = {
"model": model,
"messages": messages,
"max_tokens": 1000
}
start_time = datetime.now()
response = requests.post(
f"{BASE_URL}/chat/completions",
headers=headers,
json=payload,
timeout=30
)
latency_ms = (datetime.now() - start_time).total_seconds() * 1000
if response.status_code == 200:
result = response.json()
usage = result.get("usage", {})
record = {
"timestamp": datetime.now().isoformat(),
"model": model,
"input_tokens": usage.get("prompt_tokens", total_input_tokens),
"output_tokens": usage.get("completion_tokens", 0),
"total_tokens": usage.get("total_tokens", 0),
"latency_ms": round(latency_ms, 2),
"cost_usd": self._calculate_cost(model, usage)
}
self.request_history.append(record)
return {"success": True, "data": result, "usage": record}
else:
return {
"success": False,
"error": response.text,
"status_code": response.status_code
}
def _calculate_cost(self, model: str, usage: dict) -> float:
"""Calculate the cost of an API call based on token usage."""
pricing = {
"gpt-4.1": {"input": 2.00, "output": 8.00},
"claude-sonnet-4.5": {"input": 3.00, "output": 15.00},
"gemini-2.5-flash": {"input": 0.10, "output": 2.50},
"deepseek-v3.2": {"input": 0.10, "output": 0.42}
}
rates = pricing.get(model, {"input": 1.00, "output": 5.00})
input_cost = (usage.get("prompt_tokens", 0) / 1_000_000) * rates["input"]
output_cost = (usage.get("completion_tokens", 0) / 1_000_000) * rates["output"]
return round(input_cost + output_cost, 6)
def get_usage_summary(self) -> dict:
"""Generate a summary of all API calls and their costs."""
if not self.request_history:
return {"message": "No requests made yet"}
total_input = sum(r["input_tokens"] for r in self.request_history)
total_output = sum(r["output_tokens"] for r in self.request_history)
total_cost = sum(self._calculate_cost(r["model"], r) for r in self.request_history)
avg_latency = sum(r["latency_ms"] for r in self.request_history) / len(self.request_history)
return {
"total_requests": len(self.request_history),
"total_input_tokens": total_input,
"total_output_tokens": total_output,
"total_tokens": total_input + total_output,
"total_cost_usd": round(total_cost, 6),
"average_latency_ms": round(avg_latency, 2)
}
Initialize the debugger
debugger = TokenDebugger(API_KEY)
Example usage
test_text = "Hello, this is a test message for token counting demonstration."
result = debugger.count_tokens_local(test_text)
print(f"Token analysis: {json.dumps(result, indent=2)}")This utility class provides the foundation for all your token debugging needs. You can instantiate it with your HolySheep AI API key, count tokens locally before making expensive API calls, and track all request costs in a persistent history.
Common Token Calculation Errors and Their Solutions
Error 1: Mismatched Tokenizer Between Client and API
The most frequent cause of token calculation discrepancies is using a different tokenizer than the one employed by your API provider. Every AI company uses their own tokenization scheme, and minor differences can lead to significant cost miscalculations over thousands of requests. For example, OpenAI's cl100k_base tokenizer handles certain Unicode characters differently than Anthropic's tokenizer, resulting in token count differences even for identical input text.
The solution is to always use the tokenizer that matches your specific API provider. For HolySheep AI integrations, use tiktoken with the cl100k_base encoding for OpenAI-compatible models. Here is a corrected implementation that handles tokenizer matching properly:
import tiktoken
def get_correct_tokenizer_for_provider(provider: str, model: str) -> tiktoken.Encoding:
"""
Returns the correct tokenizer based on provider and model.
This prevents the #1 cause of token calculation errors.
"""
# HolySheep AI uses OpenAI-compatible tokenization for most models
openai_models = ["gpt-4", "gpt-4-turbo", "gpt-3.5-turbo", "gpt-4.1"]
anthropic_models = ["claude-3", "claude-3.5", "claude-sonnet-4.5"]
google_models = ["gemini-pro", "gemini-flash", "gemini-2.5-flash"]
if any(m in model.lower() for m in openai_models):
return tiktoken.get_encoding("cl100k_base")
elif any(m in model.lower() for m in anthropic_models):
# Anthropic uses a different tokenizer - use tiktoken for estimation
return tiktoken.get_encoding("cl100k_base") # Best approximation
elif any(m in model.lower() for m in google_models):
# Google uses SentencePiece - tiktoken provides reasonable estimates
return tiktoken.get_encoding("cl100k_base")
else:
# Default to OpenAI-compatible tokenization
return tiktoken.get_encoding("cl100k_base")
def accurate_token_count(text: str, model: str) -> dict:
"""Calculate tokens with provider-aware tokenizer selection."""
tokenizer = get_correct_tokenizer_for_provider("holysheep", model)
token_ids = tokenizer.encode(text)
return {
"text_length": len(text),
"token_count": len(token_ids),
"tokens_per_character": round(len(token_ids) / max(len(text), 1), 3),
"model_used": model,
"provider": "HolySheep AI"
}
Test the corrected function
test_cases = [
("Hello, world!", "gpt-4.1"),
("The quick brown fox jumps over the lazy dog.", "claude-sonnet-4.5"),
("AI APIs can be expensive if you don't track tokens carefully.", "gemini-2.5-flash"),
("Token calculation is essential for cost management.", "deepseek-v3.2")
]
for text, model in test_cases:
result = accurate_token_count(text, model)
print(f"Model: {model}")
print(f" Tokens: {result['token_count']}")
print(f" Efficiency: {result['tokens_per_character']} tokens/char\n")Error 2: Ignoring System and Context Tokens
When troubleshooting token calculations, many developers only count the user message tokens while forgetting critical components like system prompts, conversation history, and metadata. If your system prompt is 500 tokens, your user message is 200 tokens, and you have 3 prior conversation turns at 150 tokens each, you are actually sending 1,100 tokens, not 200. This oversight can cause your cost estimates to be off by 500% or more.
Here is a comprehensive token calculator that accounts for all components:
def calculate_total_request_tokens(
system_prompt: str,
conversation_history: list[dict],
current_message: str,
model: str
) -> dict:
"""
Calculate the true token count for a complete API request.
This prevents the common error of only counting the current message.
"""
tokenizer = tiktoken.get_encoding("cl100k_base")
# Token count for system prompt
system_tokens = len(tokenizer.encode(system_prompt))
# Token count for conversation history (each message)
history_tokens = 0
for msg in conversation_history:
# Each message has overhead: role, content, and formatting
role_tokens = len(tokenizer.encode(msg.get("role", "")))
content_tokens = len(tokenizer.encode(msg.get("content", "")))
# Average overhead per message (newline, separators, etc.)
overhead = 4
history_tokens += role_tokens + content_tokens + overhead
# Token count for current user message
current_tokens = len(tokenizer.encode(current_message))
# Additional overhead for the messages array structure
messages_array_overhead = 3 # Opening [, each message wrapper, closing ]
total_input_tokens = (
system_tokens +
history_tokens +
current_tokens +
messages_array_overhead
)
# Calculate estimated cost (using HolySheep AI pricing)
pricing = {
"gpt-4.1": {"input": 2.00, "output": 8.00},
"claude-sonnet-4.5": {"input": 3.00, "output": 15.00},
"gemini-2.5-flash": {"input": 0.10, "output": 2.50},
"deepseek-v3.2": {"input": 0.10, "output": 0.42}
}
rates = pricing.get(model, {"input": 2.00, "output": 8.00})
return {
"breakdown": {
"system_prompt_tokens": system_tokens,
"history_tokens": history_tokens,
"current_message_tokens": current_tokens,
"overhead_tokens": messages_array_overhead
},
"total_input_tokens": total_input_tokens,
"estimated_input_cost": round((total_input_tokens / 1_000_000) * rates["input"], 6),
"model": model,
"pricing_per_mtok": rates
}
Practical example showing the error
example_system = "You are a helpful assistant for a customer support ticket system."
example_history = [
{"role": "user", "content": "I need help with my order #12345"},
{"role": "assistant", "content": "I'd be happy to help with order #12345. What specific issue are you experiencing?"},
{"role": "user", "content": "The package was damaged when it arrived."}
]
example_current = "Can I get a replacement shipped immediately?"
result = calculate_total_request_tokens(
example_system,
example_history,
example_current,
"gpt-4.1"
)
print("=== Token Breakdown ===")
for key, value in result["breakdown"].items():
print(f" {key}: {value}")
print(f"\nTotal Input Tokens: {result['total_input_tokens']}")
print(f"Estimated Cost: ${result['estimated_input_cost']}")
print("\n💡 Notice: If you only counted the current message, you'd miss " +
f"{result['total_input_tokens'] - result['breakdown']['current_message_tokens']} tokens!")Error 3: Exceeding Context Window Limits
Every AI model has a maximum context window — the total number of tokens it can process in a single request. Exceeding this limit results in errors that can crash your application or, worse, silently truncate your input. For GPT-4.1, the context window is 128,000 tokens; for Claude Sonnet 4.5, it is 200,000 tokens. DeepSeek V3.2 typically supports 64,000 tokens. Failing to check these limits is a critical error that affects both functionality and billing.
Implement proper context window validation with this robust checking function:
from typing import Tuple, Optional
Model context window limits (in tokens) - Updated 2026
CONTEXT_WINDOWS = {
"gpt-4.1": 128000,
"gpt-4-turbo": 128000,
"gpt-3.5-turbo": 16385,
"claude-sonnet-4.5": 200000,
"claude-opus-4": 200000,
"gemini-2.5-flash": 1000000, # 1M context for Flash
"gemini-pro": 32000,
"deepseek-v3.2": 64000,
"deepseek-coder": 16000,
}
def validate_context_window(
system_prompt: str,
conversation_history: list[dict],
current_message: str,
model: str,
reserved_output_tokens: int = 2000
) -> Tuple[bool, dict]:
"""
Validate that a request fits within the model's context window.
Returns (is_valid, details) tuple.
Args:
system_prompt: The system instruction text
conversation_history: List of prior messages
current_message: The new user message
model: The model identifier
reserved_output_tokens: Tokens reserved for the response
Returns:
(is_valid, details_dict) - First element is True if request fits
"""
tokenizer = tiktoken.get_encoding("cl100k_base")
# Calculate all token counts
system_tokens = len(tokenizer.encode(system_prompt))
history_tokens = sum(
len(tokenizer.encode(str(msg))) for msg in conversation_history
)
current_tokens = len(tokenizer.encode(current_message))
total_input_tokens = system_tokens + history_tokens + current_tokens
# Get model context window
context_window = CONTEXT_WINDOWS.get(model, 128000) # Default fallback
available_for_input = context_window - reserved_output_tokens
is_valid = total_input_tokens <= available_for_input
details = {
"model": model,
"context_window": context_window,
"reserved_output_tokens": reserved_output_tokens,
"available_for_input": available_for_input,
"tokens_breakdown": {
"system": system_tokens,
"history": history_tokens,
"current_message": current_tokens,
"total_input": total_input_tokens
},
"is_valid": is_valid,
"overage_tokens": max(0, total_input_tokens - available_for_input),
"percentage_used": round((total_input_tokens / available_for_input) * 100, 2) if available_for_input > 0 else 0
}
if not is_valid:
details["recommendation"] = (
f"Reduce input by {details['overage_tokens']} tokens. "
f"Consider summarizing older conversation history or shortening the system prompt."
)
return is_valid, details
Test the validation
test_result = validate_context_window(
system_prompt="You are a helpful assistant." * 100, # Intentionally long
conversation_history=[
{"role": "user", "content": "Previous question about coding." * 50},
{"role": "assistant", "content": "Here is a detailed explanation." * 100}
],
current_message="Can you help me debug this error?",
model="gpt-4.1"
)
print(f"Request Valid: {test_result[1]['is_valid']}")
print(f"Context Window: {test_result[1]['context_window']:,} tokens")
print(f"Total Input Tokens: {test_result[1]['tokens_breakdown']['total_input']:,}")
print(f"Percentage Used: {test_result[1]['percentage_used']}%")
if not test_result[1]['is_valid']:
print(f"\n⚠️ WARNING: {test_result[1]['recommendation']}")Error 4: Incorrect Cost Calculations in Multi-Currency Environments
When working with international API providers, currency conversion errors compound quickly. If your billing system expects dollars but the provider prices in a different currency, even small rounding errors become significant at scale. HolySheep AI solves this elegantly by using ¥1=$1 pricing — a simple 1:1 ratio that eliminates conversion confusion entirely. This represents an 85%+ savings compared to providers charging ¥7.3 per dollar.
def calculate_cost_universal(
input_tokens: int,
output_tokens: int,
model: str,
pricing_currency: str = "USD",
currency_conversion_rate: float = 1.0
) -> dict:
"""
Universal cost calculator supporting any currency with conversion.
Note: HolySheep AI uses ¥1=$1, eliminating conversion complexity.
For other providers, apply appropriate conversion rates.
"""
# Pricing per million tokens (USD) - 2026 rates
usd_pricing = {
"gpt-4.1": {"input": 2.00, "output": 8.00},
"claude-sonnet-4.5": {"input": 3.00, "output": 15.00},
"gemini-2.5-flash": {"input": 0.10, "output": 2.50},
"deepseek-v3.2": {"input": 0.10, "output": 0.42}
}
rates = usd_pricing.get(model, {"input": 2.00, "output": 8.00})
# Calculate USD cost
input_cost_usd = (input_tokens / 1_000_000) * rates["input"]
output_cost_usd = (output_tokens / 1_000_000) * rates["output"]
total_cost_usd = input_cost_usd + output_cost_usd
# Convert to target currency (for HolySheep, this is 1:1)
total_cost_converted = total_cost_usd * currency_conversion_rate
return {
"input_cost_usd": round(input_cost_usd, 6),
"output_cost_usd": round(output_cost_usd, 6),
"total_cost_usd": round(total_cost_usd, 6),
f"total_cost_{pricing_currency}": round(total_cost_converted, 6),
"exchange_rate_applied": currency_conversion_rate,
"model": model,
"token_breakdown": {
"input": input_tokens,
"output": output_tokens,
"total": input_tokens + output_tokens
}
}
Example: Calculating cost for a typical request
Using HolySheep's ¥1=$1 rate means NO conversion needed
example_cost = calculate_cost_universal(
input_tokens=1500,
output_tokens=350,
model="gpt-4.1",
pricing_currency="CNY",
currency_conversion_rate=1.0 # HolySheep's ¥1=$1 rate
)
print("=== Cost Analysis ===")
print(f"Input Cost: ${example_cost['input_cost_usd']:.4f}")
print(f"Output Cost: ${example_cost['output_cost_usd']:.4f}")
print(f"Total Cost (USD): ${example_cost['total_cost_usd']:.4f}")
print(f"Total Cost (CNY): ¥{example_cost['total_cost_CNY']:.4f}")
print(f"\n💡 HolySheep's ¥1=$1 rate means simple calculations!")Advanced Debugging: Real-World Troubleshooting Scenarios
Scenario 1: Detecting Token Leaks in Long Conversations
One of the most insidious token-related problems is the gradual accumulation of context that never gets cleaned up. As conversations grow longer, developers often forget that every historical message contributes to token counts — and costs. Here is a diagnostic tool that identifies potential token leaks:
from collections import defaultdict
class ConversationTokenAnalyzer:
"""
Analyzes conversation history to detect token efficiency issues.
Essential for optimizing long-running chat applications.
"""
def __init__(self):
self.tokenizer = tiktoken.get_encoding("cl100k_base")
def analyze_conversation(self, messages: list[dict], model: str) -> dict:
"""
Perform comprehensive token analysis on a conversation.
Identifies inefficiencies and cost optimization opportunities.
"""
total_input_tokens = 0
message_analysis = []
role_distribution = defaultdict(int)
repeated_content = defaultdict(int)
for i, msg in enumerate(messages):
role = msg.get("role", "unknown")
content = msg.get("content", "")
tokens = len(self.tokenizer.encode(content))
# Track role distribution
role_distribution[role] += tokens
# Detect repeated content
content_hash = hash(content)
repeated_content[content_hash] += 1
message_analysis.append({
"index": i,
"role": role,
"token_count": tokens,
"character_count": len(content),
"preview": content[:50] + "..." if len(content) > 50 else content
})
total_input_tokens += tokens
# Identify potential optimizations
optimizations = []
# Check for repeated messages (possible duplication bug)
for content_hash, count in repeated_content.items():
if count > 1:
optimizations.append({
"type": "duplication",
"severity": "high",
"message": f"Found {count} identical messages — possible duplication bug"
})
# Check for very long system prompts
system_tokens = role_distribution.get("system", 0)
if system_tokens > 2000:
optimizations.append({
"type": "system_prompt_bloat",
"severity": "medium",
"message": f"System prompt is {system_tokens} tokens — consider condensing"
})
# Check token efficiency
avg_tokens_per_message = total_input_tokens / len(messages) if messages else 0
if avg_tokens_per_message > 500:
optimizations.append({
"type": "inefficient_messaging",
"severity": "low",
"message": f"Average {avg_tokens_per_message:.0f} tokens/message — consider message summarization"
})
context_window = 128000 # Default, adjust per model
context_usage_pct = (total_input_tokens / context_window) * 100
return {
"total_messages": len(messages),
"total_tokens": total_input_tokens,
"context_window_usage_pct": round(context_usage_pct, 2),
"role_distribution": dict(role_distribution),
"message_analysis": message_analysis,
"optimizations": optimizations,
"recommendation": self._generate_recommendation(context_usage_pct, optimizations)
}
def _generate_recommendation(self, usage_pct: float, optimizations: list) -> str:
"""Generate actionable recommendations based on analysis."""
recommendations = []
if usage_pct > 80:
recommendations.append("CRITICAL: Context window >80% full. Implement message summarization soon.")
elif usage_pct > 50:
recommendations.append("WARNING: Context window >50% full. Plan for conversation management.")
high_severity = [o for o in optimizations if o["severity"] == "high"]
if high_severity:
recommendations.append(f"Address {len(high_severity)} high-severity issues immediately.")
return " ".join(recommendations) if recommendations else "Conversation is well-optimized."
Test the analyzer with a sample conversation
analyzer = ConversationTokenAnalyzer()
sample_conversation = [
{"role": "system", "content": "You are an expert Python programmer."},
{"role": "user", "content": "How do I sort a list in Python?"},
{"role": "assistant", "content": "You can use the sorted() function or the list's sort() method."},
{"role": "user", "content": "What's the difference between them?"},
{"role": "assistant", "content": "sorted() returns a new sorted list, while sort() modifies the list in place."},
{"role": "user", "content": "Can you show me an example?"},
{"role": "assistant", "content": """
# sorted() example
original = [3, 1, 4, 1, 5]
sorted_list = sorted(original)
print(sorted_list) # [1, 1, 3, 4, 5]
print(original) # [3, 1, 4, 1, 5] - original unchanged
# sort() example
original.sort()
print(original) # [1, 1, 3, 4, 5] - original modified
"""}
]
result = analyzer.analyze_conversation(sample_conversation, "gpt-4.1")
print("=== Conversation Analysis ===")
print(f"Total Messages: {result['total_messages']}")
print(f"Total Tokens: {result['total_tokens']}")
print(f"Context Usage: {result['context_window_usage_pct']}%")
print("\nRole Distribution:")
for role, tokens in result['role_distribution'].items():
print(f" {role}: {tokens} tokens")
print("\nOptimizations:")
for opt in result['optimizations']:
print(f" [{opt['severity'].upper()}] {opt['message']}")
print(f"\n{result['recommendation']}")Scenario 2: Handling Multilingual Token Calculation
Multilingual applications face unique token calculation challenges because different languages have vastly different token-per-character ratios. Chinese text typically requires fewer tokens per character than English, while some languages (like German compound words) may require more. Here is a robust solution for calculating tokens across languages:
import unicodedata
import re
class MultilingualTokenCalculator:
"""
Handles token calculation for text in any language.
Accounts for character encoding, Unicode normalization, and language-specific patterns.
"""
def __init__(self):
self.tokenizer = tiktoken.get_encoding("cl100k_base")
# Language-specific token ratios (approximate, based on empirical testing)
self.language_ratios = {
"en": 0.25, # English: ~4 characters per token
"zh": 0.50, # Chinese: ~2 characters per token (often 1:1 or 2:1)
"ja": 0.40, # Japanese: ~2.5 characters per token
"ko": 0.45, # Korean: ~2.2 characters per token
"de": 0.22, # German: ~4.5 characters per token (compound words)
"es": 0.24, # Spanish: ~4.2 characters per token
"fr": 0.23, # French: ~4.3 characters per token
"ar": 0.30, # Arabic: ~3.3 characters per token
"ru": 0.28, # Russian: ~3.6 characters per token
}
def detect_language(self, text: str) -> str:
"""Detect the primary language of the text."""
# Simple language detection based on character ranges
if re.search(r'[\u4e00-\u9fff]', text):
return "zh" # Chinese
elif re.search(r'[\u3040-\u309f\u30a0-\u30ff]', text):
return "ja" # Japanese
elif re.search(r'[\uac00-\ud7af]', text):
return "ko" # Korean
elif re.search(r'[\u0600-\u06ff]', text):
return "ar" # Arabic
elif re.search(r'[\u0400-\u04ff]', text):
return "ru" # Russian
else:
return "en" # Default to English
def normalize_text(self, text: str) -> str:
"""Normalize Unicode text for consistent tokenization."""
# NFKC normalization for consistent character representation
return unicodedata.normalize('NFKC', text)
def calculate_tokens(self, text: str, accurate: bool = True) -> dict:
"""
Calculate tokens for text in any language.
Args:
text: The input text to tokenize
accurate: If True, use actual tokenizer; if False, use ratio estimation
Returns:
Dictionary with token counts and language information
"""
# Normalize text
normalized = self.normalize_text(text)
if accurate:
# Use actual tokenizer (most accurate)
token_ids = self.tokenizer.encode(normalized)