Estimate tokens for Meta Llama models, including Llama 4 Scout with its 10-million-token context window. Llama models are open weight, so there is no fixed API price; the counter focuses on token volume and context fit for self-hosted or provider-hosted deployments.
PROMPT
Count Llama tokens live.
Tokens
0
Words
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Characters
0
Input cost
$0.00
Output cost
$0.00
Total request
$0.00
10,000,000 token context windowEstimated count0%
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How Llama tokenization works
Llama 4 uses a tiktoken-style BPE tokenizer with a large vocabulary; counts here are estimates tuned for English text.
Because Llama is open weight, cost depends on where you run it: your own GPUs, a cloud endpoint, or an inference provider, each with different per-token or per-hour pricing.
Llama 4 Scout supports a 10-million-token context window, the largest in this catalog by far.
Token counts still matter for self-hosting: more tokens means more KV-cache memory and slower responses, even when no one bills you per token.
Pricing refreshed 2026-08-11 from official provider sources. Compare against other providers on the LLM pricing table or browse the full model directory.
FAQ
Llama token counter FAQ
Why is there no price on the Llama token counter?
Meta releases Llama as open-weight models rather than a paid API, so cost depends on your hosting: local GPUs, cloud instances, or third-party inference providers. Token counts remain essential for context planning and for comparing hosted-provider quotes, which are usually quoted per million tokens.
How big is the Llama 4 context window?
Llama 4 Scout supports up to 10 million tokens of context — roughly 7.5 million English words — and Llama 4 Maverick supports 1 million tokens. Practical limits depend on the memory of the hardware serving the model.
Are these Llama token counts exact?
Llama 3 family models are counted with the official published Llama 3 tokenizer in your browser, so those are exact. Llama 4 uses a newer tokenizer that is not yet ported, so Llama 4 counts are labeled estimates.