Llama Token Counter
Llama 3 and Llama 4 models use a 128k-vocabulary tiktoken-style BPE tokenizer. TokenMath counts Llama tokens with a calibrated estimate, typically within ±3%.
Universal AI Token Counter
Zero-latency, client-side BPE tokenization with real-time multi-model benchmarks.
Llama Models: Context Windows & Token Prices
| Model | Context Window | Input / 1M | Output / 1M | Token Count Accuracy |
|---|---|---|---|---|
| Llama 4 Vivas | 1M tokens | $0.25 | $0.85 | Calibrated Llama 3 Tokenizer (±3%) |
How Llama Tokenization Works
Llama models split text with the Llama tiktoken BPE tokenizer, a tiktoken-style BPE scheme with a ~128,000 tokens vocabulary. Common words map to one token, while rare words, identifiers and non-Latin scripts are split into several subword pieces. Read how BPE and SentencePiece tokenization work for the details.
- Hosted Llama pricing varies by inference provider; TokenMath tracks Together AI rates.
- Self-hosting changes the economics entirely; see the API vs GPU calculator.
Once you know your token count, estimate a full monthly bill with the LLM cost calculator, or see every Meta / Together model price.
Llama Token Counter FAQ
How do I count Llama tokens?
Paste your prompt into the counter above with a Llama model selected. TokenMath tokenizes the text in your browser and shows the token count, context window usage and input cost. Accuracy: Calibrated Llama 3 Tokenizer (±3%).
What tokenizer does Llama use?
Llama models are counted with the Llama tiktoken BPE tokenizer (tiktoken-style BPE, ~128,000 tokens vocabulary). Llama 3 and Llama 4 models use a 128k-vocabulary tiktoken-style BPE tokenizer.
How many words is 1,000 Llama tokens?
For typical English prose, 1,000 tokens is roughly 700–750 words. Code, JSON and non-English text use more tokens per word, so count the actual text when budgeting.
How much does Llama 4 Vivas cost per token?
Llama 4 Vivas costs $0.25 per 1M input tokens and $0.85 per 1M output tokens. That's $0.00025 per 1,000 input tokens.