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OpenAI vs Llama tokenizers: why the same text produces different token counts

Token counts vary by model. For cost-critical applications, benchmark the actual token count with the target model's tokenizer. The built-in token counter on this page gives exact counts for OpenAI and Llama models and reasonable estimates for Claude and Gemini.

A sentence that costs 50 tokens on GPT-5 might cost 55 tokens on Llama 4 or 48 on Claude. The differences come from the tokenizer's vocabulary, its merge rules, and how it handles whitespace, capitalization, and non-English text. Even among OpenAI models, GPT-5 (o200k_base) and GPT-4 (cl100k_base) use different tokenizers with different counts.

By TechCompare · Updated

Knowledge area
Model Comparison
Tokenization across model families
Topic focus
OpenAI vs Llama tokenizer
tokenizer-comparison
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How this is calculated

OpenAI's o200k_base tokenizer has a larger vocabulary (200K tokens) than cl100k_base (100K tokens), which generally means fewer tokens for the same input because more common words and subwords are stored as single tokens. Llama's tokenizer is based on sentencepiece BPE with a 128K vocabulary and tends to be slightly less efficient for English but better for code and multilingual text. Anthropic doesn't publicly release its Claude tokenizer, so all Claude token counts are estimates. The practical impact is on API costs: at GPT-5 pricing, a 10% difference in tokenization efficiency can mean hundreds of dollars per month for high-volume applications.

Verdict

Tokenizer vocabulary drives the divergence. OpenAI's o200k_base holds 200K tokens versus the 100K of cl100k_base, which usually means fewer tokens for the same text on GPT-5. Llama 4 leans on sentencepiece BPE at 128K and lands slightly less efficient for English but better for code and multilingual. At GPT-5 pricing, a 10% efficiency gap can swing hundreds of dollars monthly on high-volume workloads.

More Tokens scenarios

What are tokens
A token is the atomic unit of text that a language model processes.
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Token limits by model
Context window size is the maximum number of tokens a model can process in a single request, including both input and output.
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Prompt optimization
Prompt optimization is the practice of getting the same or better results from an LLM with fewer input tokens.
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GPT-5.4 API pricing
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Llama 4 Scout VRAM estimate
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Frequently asked questions

Why does the same text tokenize differently on GPT and Llama?
Each model family ships its own tokenizer vocabulary and merge rules. OpenAI's o200k_base stores 200K common fragments as single tokens, while Llama's sentencepiece BPE vocabulary holds 128K, so identical text breaks into different pieces. Whitespace, capitalization, and non-English words land differently in each.
Which is more token-efficient, GPT-5 or Llama 4?
For English prose, GPT-5's o200k_base is usually the tighter one because its larger 200K vocabulary covers more words as single tokens. Llama 4 tends to run slightly higher on English but handles code and some multilingual text more evenly. The practical difference runs about 5-10% most of the time.
Does tokenizer efficiency change my API bill?
Directly. A model that turns your prompt into 10% fewer tokens bills you 10% less for the same requests, which at GPT-5 pricing and high volume is hundreds of dollars a month. Benchmark your actual prompts against each candidate model's tokenizer before choosing on per-token price alone.

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