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Estimated Token Counts by Model

Why the differences? Each model family uses a different tokenizer. OpenAI uses tiktoken (cl100k_base), DeepSeek uses a custom tokenizer optimized for code, Chinese models use tokenizers with larger CJK vocabularies. These estimates use published tokenizer characteristics.

Why Tokenization Varies by Model

1. Vocabulary Size

Larger vocabularies can represent common words and phrases in fewer tokens. GPT-4o's ~100K vocabulary vs DeepSeek's ~102K may look similar, but the content of those vocabularies differs significantly.

2. Language Optimization

Chinese-focused models (K3, Qwen, Doubao) include more CJK characters in their base vocabulary, requiring fewer tokens per Chinese character. English-optimized models may use 2-3 tokens per Chinese character.

3. Code Optimization

Models trained on more code (DeepSeek, GPT-4o) have better tokenization for programming languages. Common code patterns like indentation, brackets, and keywords are often single tokens.

4. Special Characters

Unicode characters, emoji, and mathematical symbols vary wildly. Some models tokenize emoji as single tokens; others break them into 3-5 tokens. Currency symbols, arrows, and dingbats show similar variance.

Cost Impact of Tokenization Differences

Scenario	GPT-4o	DeepSeek V4	K3	Cost Winner
1,000 English words	~1,333 tokens	~1,250 tokens	~1,280 tokens	DeepSeek V4
1,000 Chinese characters	~1,500 tokens	~1,400 tokens	~1,200 tokens	K3
500 lines Python code	~1,800 tokens	~1,600 tokens	~1,700 tokens	DeepSeek V4
Mixed EN/CN document	~2,000 tokens	~1,850 tokens	~1,700 tokens	K3
JSON API response	~1,200 tokens	~1,100 tokens	~1,150 tokens	DeepSeek V4

* Token estimates are approximations based on published tokenizer characteristics. Actual counts may vary by 5-10%.

Token Efficiency Tips

Use English for system prompts when possible — most tokenizers are optimized for English
Minimize JSON formatting — whitespace and indentation add tokens without value
Avoid repeated prefixes — "The user said: ..." repeated 50 times wastes tokens
Choose the right model for your language — Chinese text is cheaper on K3/Qwen than GPT-4o
Use structured output carefully — JSON Schema adds overhead; only use when needed
Cache system prompts — many APIs support prompt caching for repeated context

Token Pricing at TokenEase (Per 1M Tokens)

Model	Input Price	Output Price	Tokenizer Efficiency
Kimi K3	$0.50	$2.00	Excellent for Chinese & long docs
DeepSeek V4	$0.50	$2.00	Excellent for code & reasoning
GLM-5	$0.50	$2.00	Best for structured JSON output
Qwen-Plus	$1.00	$3.00	Strong multilingual support
Doubao Pro	$0.80	$2.40	Fastest for Chinese NLP
GPT-4o (ref)	$2.50	$10.00	Good general-purpose efficiency

AI Model Tokenizer Comparison