★ Llama · token counter
Llama Token Counter
Llama is open-weights, so its tokenizer ships with the model files, but it's too large to load in a page like this. Here you get a labelled estimate and an explanation of how close it is likely to be.
- Runs in your browser
- No signup
- 306
- Characters
- 54
- Words
- —
- Tokens / word
Tokens per word uses the o200k_base count. Characters count emoji and accented letters as one each.
Tokens
…
Estimated with o200k_base as a stand-in. Llama's own tokenizer isn't available in the browser.
Context window used
—
Llama context limits depend on the model and host.
Input cost per call
—
Per 1,000 calls
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Every model, same text
| Model | Tokens | Accuracy | Input / call |
|---|---|---|---|
| Claude Fable 5.1Anthropic | … | ≈ Estimate | — |
| Claude Opus 5.5Anthropic | … | ≈ Estimate | — |
| Claude Sonnet 5.5Anthropic | … | ≈ Estimate | — |
| Claude Haiku 5.5Anthropic | … | ≈ Estimate | — |
| GPT-6 AstraOpenAI | … | ✓ Exact | — |
| GPT-6.1 SolOpenAI | … | ✓ Exact | — |
| GPT-6 LunaOpenAI | … | ✓ Exact | — |
| Gemini 4 ArgonGoogle | 77 | ≈ Estimate | $0.00015 |
| Gemini 3.8 FlashGoogle | 77 | ≈ Estimate | $0.00006 |
| Gemini 3.1 Flash-LiteGoogle | 77 | ≈ Estimate | $0.00002 |
| Mistral Large 4Mistral | … | ≈ Estimate | — |
| Mistral Small 4Mistral | … | ≈ Estimate | — |
| DeepSeek V4.1 FlashDeepSeek | 92 | ≈ Estimate | $0.00003 |
| DeepSeek V4 ProDeepSeek | 92 | ≈ Estimate | $0.00012 |
| Llama (open weights)Meta | … | ≈ Estimate | — |
OpenAI rows use o200k_base, exact for that tokenizer. tiktoken hasn't mapped GPT-6 model names yet, so OpenAI hasn't confirmed GPT-6 uses it. Input cost is list price for your text alone, before system prompts, caching or output.
See the tokens
o200k_base
Loading the tokenizer…
Your tokens on Llama (open weights).
Prompts eating your context window? I build RAG and prompt pipelines that use a fraction of the tokens.
How Llama tokenizes
Llama 3 moved to a tokenizer with a vocabulary of 128,256 tokens, up from 32,000 in Llama 2, according to Hugging Face's Llama 3 launch post. A larger vocabulary means fewer tokens for the same text.
Because the weights are open, hosts can serve Llama with different context limits and prices. Check your provider for both.
Exact or estimate?
Estimate. We count with o200k_base, another large-vocabulary BPE tokenizer, as a stand-in. Llama's real count will differ, more so for code and non-English text.
For an exact number, load the model's own tokenizer from its official files and count locally.
Quirks worth knowing
- Different Llama generations use different tokenizers. Count with the one that matches your model.
- Chat templates add special tokens around each message.
- There's no single first-party API price: cost depends on where you host it.
Sources: Hugging Face: Welcome Llama 3
Questions people actually ask
Can I get exact Llama token counts here?
Why is there no price for Llama?
Do Llama 2 and Llama 3 count the same way?
Which counts on this page are exact?
Is my text sent anywhere?
Count by model family
- Claude token counter
- GPT token counter
- Gemini token counter
- DeepSeek token counter
- Mistral token counter
Or compare every family at once on the main token counter.
More from the workbench
Fine print
Tools: on the house.
Engineer: by the hour.
Counting tokens was free. Spending fewer of them is a project.
I'm Himanshu, an AI and full-stack engineer. I build MVPs, AI agents and the growth plumbing around them for early-stage startups.