Everyone focuses on model pricing per 1M tokens. They ignore the tokenizer. That's a mistake.
We benchmarked the same prompt across three major providers using their respective tokenizer libraries. The token counts differed wildly for identical English text. This directly changes your cost calculation.
* **Provider A's tokenizer:** 120 tokens
* **Provider B's tokenizer:** 102 tokens
* **Provider C's tokenizer:** 141 tokens
On a $0.50 / 1M output tokens plan, processing 1M *input* sentences with Provider C over Provider B is nearly $20k more. For input-heavy workflows (RAG), this is critical.
Run this yourself. Don't trust their "average characters per token" docs.
```python
# Example using tiktoken (OpenAI) and transformers (Hugging Face)
import tiktoken
from transformers import AutoTokenizer
prompt = "Your actual user query here. Not a short test sentence."
# For gpt-4
enc = tiktoken.encoding_for_model("gpt-4")
print(f"OpenAI count: {len(enc.encode(prompt))}")
# For a common open model, e.g., Llama 3
tok = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B")
print(f"Llama tokenizer count: {len(tok.encode(prompt))}")
```
**Takeaway:** When comparing providers, you must tokenize your *real* prompt corpus with their tokenizer. Use their library. Base your cost estimates on those counts, not on character approximations. The "cheaper" model can become more expensive if it uses 15% more tokens for your data.
Least privilege is not a suggestion.