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★ Mistral · token counter

Mistral Token Counter

Mistral publishes its tokenizers through the mistral-common library, but they aren't available as a small browser package. This page gives a labelled estimate with cost per call and context use.

  • Runs in your browser
  • No signup
306
Characters
54
Words
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Tokens / word

Tokens per word uses the o200k_base count. Characters count emoji and accented letters as one each.

Tokens

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≈ Estimate

Estimated with o200k_base as a stand-in. Mistral's own tokenizer isn't available in the browser.

Context window used

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1M tokens window on Mistral Large 4.

Input cost per call

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Per 1,000 calls

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Every model, same text

ModelTokensAccuracyInput / 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 ArgonGoogle77≈ Estimate$0.00015
Gemini 3.8 FlashGoogle77≈ Estimate$0.00006
Gemini 3.1 Flash-LiteGoogle77≈ Estimate$0.00002
Mistral Large 4Mistral…≈ Estimate—
Mistral Small 4Mistral…≈ Estimate—
DeepSeek V4.1 FlashDeepSeek92≈ Estimate$0.00003
DeepSeek V4 ProDeepSeek92≈ 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 Mistral Large 4.

Prompts eating your context window? I build RAG and prompt pipelines that use a fraction of the tokens.

Shrink my prompts
01

How Mistral tokenizes

Newer Mistral models use Tekken, a byte-pair-encoding tokenizer built on tiktoken that Mistral introduced with Mistral NeMo. Mistral says it was trained on more than 100 languages and compresses source code and several languages more efficiently than its older SentencePiece tokenizer.

The tokenizers ship in the mistral-common library, which also handles chat templates and tool calls.

02

Exact or estimate?

Estimate. We count with o200k_base as a stand-in for Tekken. Both are large BPE vocabularies, but the counts will not match exactly.

For an exact number, use Mistral's own tokenizer from mistral-common.

03

Current Mistral models

Context windows and input prices from the pricing data used across these tools.

Mistral models
ModelContext windowInput / 1M
Mistral Large 41M tokens$0.70
Mistral Small 4256K tokens$0.15
04

Quirks worth knowing

  • Chat templates and tool definitions add tokens beyond your text.
  • Older Mistral models use older tokenizers, so counts differ between generations.
  • Mistral Large 4 is listed as Public Preview in the pricing data, so its pricing may change.

Sources: Mistral docs: Tokenization · Mistral AI: Mistral NeMo (Tekken tokenizer)

05

Questions people actually ask

Which tokenizer does Mistral use?
Newer models use Tekken, a tiktoken-based BPE tokenizer introduced with Mistral NeMo and published in the mistral-common library.
Is the Mistral count exact here?
No, it's an estimate made with o200k_base as a stand-in. Mistral's own tokenizer in mistral-common gives the exact count.
Does Tekken use fewer tokens for code?
Mistral says Tekken compresses source code about 30% more efficiently than its previous SentencePiece tokenizer. That is a vendor figure.
Which counts on this page are exact?
OpenAI rows are exact for o200k_base, OpenAI's published tokenizer, counted in your browser. Gemini rows are exact when this site's server has a Gemini key and Google's countTokens endpoint answers. Claude, Llama, DeepSeek and Mistral rows are estimates, and each says how it was made.
Is my text sent anywhere?
The o200k_base count runs entirely in your browser. Only if you edit the text (or ask for it) is the text sent to Google's countTokens endpoint through this site's server for exact Gemini counts, and nothing is stored or logged here.
06

Count by model family

07

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.