MISTRAL MODEL TOKEN COUNTER

mistral-tiny Token Counter

Count tokens for mistral-tiny and estimate API cost before you send a request. Mistral does not publish this tokenizer, so counts are calibrated estimates for planning. Your text never leaves the browser.

PROMPT

Paste text to count mistral-tiny tokens.

Tokens

0

Words

0

Characters

0

Input cost

$0.00

Output cost

$0.00

Total request

$0.00

32,000 token context window 0%

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mistral-tiny at a glance

Provider. Mistral

Context window. 32,000 tokens (about 24,000 English words).

API pricing. $0.25 input / $0.25 output per 1M tokens.

Tokenizer. Not public; counts on this page are calibrated estimates.

Pricing source. The community-maintained LiteLLM pricing dataset, refreshed 2026-07-01. Community-tracked rates can lag provider changes; confirm with the provider before budgeting.

Other Mistral model token counters

See the Mistral Token Counter for all Mistral models in one counter. Or browse the full model token counter directory and the LLM pricing comparison table to see how mistral-tiny prices against other providers.

FAQ

mistral-tiny token counter FAQ

How do I count tokens for mistral-tiny?

Paste your prompt into the counter on this page. Mistral does not publish the mistral-tiny tokenizer, so the count is a calibrated estimate based on text length, language, and structure. Treat it as planning guidance rather than an exact billing number.

How much does mistral-tiny cost per token?

mistral-tiny costs $0.25 per 1 million input tokens and $0.25 per 1 million output tokens, based on the community-maintained LiteLLM pricing dataset checked 2026-07-01.

What is the context window of mistral-tiny?

mistral-tiny has a context window of 32,000 tokens, which is roughly 24,000 English words of combined prompt and response. The counter above shows how much of that window your text consumes.

Is this mistral-tiny token count exact?

No. It is a clearly labeled estimate, because Mistral has not released a public tokenizer for mistral-tiny. Estimates are usually within a reasonable range for English text but can drift for code, JSON, or non-English languages.