Context Window

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Nemotron 3 Ultra (batch) vs Qwen3 235B A22B

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 (batch)

Tool calling

Context window

512K

512,288 tokens · ~384K words

Model page
Alibaba

Model

Qwen3 235B A22B

Tool calling

Context window

41K

40,960 tokens · ~31K 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 (batch)512K
Qwen3 235B A22B41K

Nemotron 3 Ultra (batch) has about 12.5× the context window of the other in this pair.

Token calculator →Memory savings →

Nemotron 3 Ultra (batch) has 1150% more context capacity (512K vs 40K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Nemotron 3 Ultra (batch). Its 512K context fits entire documents without chunking (vs 40K).

Full specs

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

SpecNemotron 3 Ultra (batch)Qwen3 235B A22B
Context window512,288 tokens (512K)40,960 tokens (40K)
Max output tokensN/A40,960 tokens (40K)
Speed tierBalancedBalanced
VisionNoNo
Function callingYesYes
Extended thinkingYesYes
Prompt cachingYesNo
Batch APINoNo
Release dateJun 2026Apr 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 (batch) inNemotron 3 Ultra (batch) outQwen3 235B A22B inQwen3 235B A22B out
Deepinfra——$0.180/M$0.540/M
Fireworks——$0.220/M$0.880/M
Hyperbolic——$2.00/M$2.00/M
Nebius——$0.200/M$0.600/M

Frequently asked questions

Nemotron 3 Ultra (batch) has a larger context window: 512K tokens vs 40K. 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