Llama Token Counter
Estimate tokens before running your prompt through Llama 3, 3.1, 3.3, or Llama 4. Whether you self-host or use a managed provider (Groq, Together, Fireworks, Ollama), the same token budget applies.
Keyword density
Top 8Add a prompt to see your top keywords.
How Llama tokenizes
Llama uses a SentencePiece BPE tokenizer with a 128K-token vocabulary (starting with Llama 3). For English the density is similar to ChatGPT’s cl100k_base — roughly 4 characters per token. For code Llama is often slightly more efficient. For non-Latin scripts the difference can be larger.
This tool returns a calibrated heuristic. For exact counts, install the transformers library and load the model’s tokenizer locally.
Llama context windows
| Model | Context window | Notes |
|---|---|---|
| Llama 3 | 8K tokens | Original; tight budget |
| Llama 3.1 | 128K tokens | First long-context Llama |
| Llama 3.3 70B | 128K tokens | Most-used size for production |
| Llama 4 Scout / Maverick | 10M tokens | Frontier-scale context |
The 10M window on Llama 4 sounds enormous, but real-world quality past ~500K tokens degrades significantly. Use long context where you genuinely need it, not because the number on the page is impressive.
Self-hosted vs API
When you self-host (Ollama, vLLM, llama.cpp), your context budget is constrained by your hardware first and the model second. A 70B Llama model with a 128K nominal context might only practically fit 16–32K tokens of context on a single consumer GPU. This counter is useful for planning the input size; your local inference setup will tell you the real limit.