SAMBANOVA MODEL TOKEN COUNTER

DeepSeek-R1-Distill-Llama-70B Token Counter

Count tokens for DeepSeek-R1-Distill-Llama-70B and estimate API cost before you send a request. SambaNova does not publish this tokenizer, so counts are calibrated estimates for planning. Your text never leaves the browser.

PROMPT

Paste text to count DeepSeek-R1-Distill-Llama-70B tokens.

Tokens

0

Words

0

Characters

0

Input cost

$0.00

Output cost

$0.00

Total request

$0.00

131,072 token context window 0%

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DeepSeek-R1-Distill-Llama-70B at a glance

Provider. SambaNova

Context window. 131,072 tokens (about 98,000 English words).

API pricing. $0.70 input / $1.40 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.

FAQ

DeepSeek-R1-Distill-Llama-70B token counter FAQ

How do I count tokens for DeepSeek-R1-Distill-Llama-70B?

Paste your prompt into the counter on this page. SambaNova does not publish the DeepSeek-R1-Distill-Llama-70B 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 DeepSeek-R1-Distill-Llama-70B cost per token?

DeepSeek-R1-Distill-Llama-70B costs $0.70 per 1 million input tokens and $1.40 per 1 million output tokens, based on the community-maintained LiteLLM pricing dataset checked 2026-07-01.

What is the context window of DeepSeek-R1-Distill-Llama-70B?

DeepSeek-R1-Distill-Llama-70B has a context window of 131,072 tokens, which is roughly 98,000 English words of combined prompt and response. The counter above shows how much of that window your text consumes.

Is this DeepSeek-R1-Distill-Llama-70B token count exact?

No. It is a clearly labeled estimate, because SambaNova has not released a public tokenizer for DeepSeek-R1-Distill-Llama-70B. Estimates are usually within a reasonable range for English text but can drift for code, JSON, or non-English languages.