GROQ MODEL TOKEN COUNTER

kimi-k2-instruct-0905 Token Counter

Count tokens for kimi-k2-instruct-0905 and estimate API cost before you send a request. Groq does not publish this tokenizer, so counts are calibrated estimates for planning. Your text never leaves the browser.

PROMPT

Paste text to count kimi-k2-instruct-0905 tokens.

Expected output

Tokens

Words

Characters

Input cost

$0.00

Output cost

$0.00

Total request

$0.00

262,144 token context window 0%

Need optimization suggestions, a token heatmap, monthly cost projection, or a side-by-side model comparison? Open the full analyzer with kimi-k2-instruct-0905 preselected.

kimi-k2-instruct-0905 at a glance

Provider. Groq

Context window. 262,144 tokens (about 197,000 English words).

API pricing. $1.00 input / $3.00 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 Groq model token counters

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Or browse the full model token counter directory and the LLM pricing comparison table to see how kimi-k2-instruct-0905 prices against other providers.

FAQ

kimi-k2-instruct-0905 token counter FAQ

How do I count tokens for kimi-k2-instruct-0905?

Paste your prompt into the counter on this page. Groq does not publish the kimi-k2-instruct-0905 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 kimi-k2-instruct-0905 cost per token?

kimi-k2-instruct-0905 costs $1.00 per 1 million input tokens and $3.00 per 1 million output tokens, based on the community-maintained LiteLLM pricing dataset checked 2026-07-01.

What is the context window of kimi-k2-instruct-0905?

kimi-k2-instruct-0905 has a context window of 262,144 tokens, which is roughly 197,000 English words of combined prompt and response. The counter above shows how much of that window your text consumes.

Is this kimi-k2-instruct-0905 token count exact?

No. It is a clearly labeled estimate, because Groq has not released a public tokenizer for kimi-k2-instruct-0905. Estimates are usually within a reasonable range for English text but can drift for code, JSON, or non-English languages.