Context Window

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Nemotron 3 Ultra (free) vs Qwen3 Next 80B A3B Thinking

This page is context-first: how much text each model can take in one request. Full specs adds capabilities and limits; the pricing matrix below is only about $/million tokens from hosts that list both models.

Nvidia

Model

Nemotron 3 Ultra (free)

Tool calling

Context window

1M

1,000,000 tokens · ~750K words

Model page
Alibaba

Model

Qwen3 Next 80B A3B Thinking

Tool calling

Context window

262K

262,144 tokens · ~197K words

Model page

Context window · side by side

Bar length is relative to the larger of the two windows (100% = max of this pair). This is not pricing.

Nemotron 3 Ultra (free)1M
Qwen3 Next 80B A3B Thinking262K

Nemotron 3 Ultra (free) has about 3.8× the context window of the other in this pair.

Token calculator →Memory savings →

Nemotron 3 Ultra (free) has 281% more context capacity (1000K vs 262K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Nemotron 3 Ultra (free). Its 1000K context fits entire documents without chunking (vs 262K).

Full specs

Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.

SpecNemotron 3 Ultra (free)Qwen3 Next 80B A3B Thinking
Context window1,000,000 tokens (1000K)262,144 tokens (262K)
Max output tokens65,536 tokens (65K)65,536 tokens (65K)
Speed tierBalancedFast
VisionNoNo
Function callingYesYes
Extended thinkingYesYes
Prompt cachingNoNo
Batch APINoNo
Release dateJun 2026Sep 2025

Pricing matrix

Dollar rates only: hosts that list both models, per 1M tokens. For how much text fits, use the context section above — not this table.

ProviderNemotron 3 Ultra (free) inNemotron 3 Ultra (free) outQwen3 Next 80B A3B Thinking inQwen3 Next 80B A3B Thinking out
Alibaba Cloud——$0.150/M$1.20/M
Deepinfra——$0.140/M$1.40/M
Fireworks——$0.900/M$0.900/M
Novita——$0.150/M$1.50/M
Together Ai——$0.150/M$1.50/M

Frequently asked questions

Nemotron 3 Ultra (free) has a larger context window: 1000K tokens vs 262K. For long documents, large codebases, or extended agent sessions, the larger context window reduces the need to chunk inputs or summarize history.

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Mem0 gives your AI long-term memory so you stop re-sending context on every call. That means you can use a smaller, faster, cheaper model — and still get better answers.

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Example: a multi-turn chat session

Without Mem0~128K tokens sent
Full history
Repeated info
Old context
With Mem0~20K tokens sent
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Current turn

80% less to send — works with any model