NEBIUS MODEL TOKEN COUNTER

Llama-3.3-Nemotron-Super-49B-v1 Token Counter

Count tokens for Llama-3.3-Nemotron-Super-49B-v1 and estimate API cost before you send a request. Nebius does not publish this tokenizer, so counts are calibrated estimates for planning. Your text never leaves the browser.

PROMPT

Paste text to count Llama-3.3-Nemotron-Super-49B-v1 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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Llama-3.3-Nemotron-Super-49B-v1 at a glance

Provider. Nebius

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

API pricing. $0.10 input / $0.40 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

Llama-3.3-Nemotron-Super-49B-v1 token counter FAQ

How do I count tokens for Llama-3.3-Nemotron-Super-49B-v1?

Paste your prompt into the counter on this page. Nebius does not publish the Llama-3.3-Nemotron-Super-49B-v1 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 Llama-3.3-Nemotron-Super-49B-v1 cost per token?

Llama-3.3-Nemotron-Super-49B-v1 costs $0.10 per 1 million input tokens and $0.40 per 1 million output tokens, based on the community-maintained LiteLLM pricing dataset checked 2026-07-01.

What is the context window of Llama-3.3-Nemotron-Super-49B-v1?

Llama-3.3-Nemotron-Super-49B-v1 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 Llama-3.3-Nemotron-Super-49B-v1 token count exact?

No. It is a clearly labeled estimate, because Nebius has not released a public tokenizer for Llama-3.3-Nemotron-Super-49B-v1. Estimates are usually within a reasonable range for English text but can drift for code, JSON, or non-English languages.