Context Window

Compare

Nemotron 3 Ultra (free) vs Qwen2 5 7b

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

Qwen2 5 7b

Context window

33K

32,768 tokens · ~25K 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
Qwen2 5 7b33K

Nemotron 3 Ultra (free) has about 30.5× the context window of the other in this pair.

Token calculator →Memory savings →

Nemotron 3 Ultra (free) has 2951% more context capacity (1000K vs 32K 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 32K).

  • Long output (reports, code files)

    Use Nemotron 3 Ultra (free). Its 65K max output lets you generate complete artifacts in one request.

Full specs

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

SpecNemotron 3 Ultra (free)Qwen2 5 7b
Context window1,000,000 tokens (1000K)32,768 tokens (32K)
Max output tokens65,536 tokens (65K)32,768 tokens (32K)
Speed tierBalancedFast
VisionNoNo
Function callingYesNo
Extended thinkingYesNo
Prompt cachingNoNo
Batch APINoNo
Release dateJun 2026N/A

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) outQwen2 5 7b inQwen2 5 7b out
Deepinfra——$0.040/M$0.100/M
Novita——$0.070/M$0.070/M

Frequently asked questions

Nemotron 3 Ultra (free) has a larger context window: 1000K tokens vs 32K. 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
Key memories
Current turn

80% less to send — works with any model