Context Window

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Databricks Kimi K2 7 vs Gov Nvidia Nemotron Nano 3 30b

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.

Moonshot

Model

Databricks Kimi K2 7

Image inputTool calling

Context window

262K

262,144 tokens · ~197K words

Model page
Nvidia

Model

Gov Nvidia Nemotron Nano 3 30b

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.

Databricks Kimi K2 7262K
Gov Nvidia Nemotron Nano 3 30b262K

Same context window size for both models.

Token calculator →Memory savings →

Databricks Kimi K2 7 and Gov Nvidia Nemotron Nano 3 30b have identical context windows (262K tokens). Gov Nvidia Nemotron Nano 3 30b is 92% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • RAG / high-volume retrieval

    Use Gov Nvidia Nemotron Nano 3 30b. Input tokens are 92% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Databricks Kimi K2 7. Its 262K 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.

SpecDatabricks Kimi K2 7Gov Nvidia Nemotron Nano 3 30b
Context window262,144 tokens (262K)262,144 tokens (262K)
Max output tokens262,144 tokens (262K)8,192 tokens (8K)
Speed tierBalancedFast
VisionYesNo
Function callingYesYes
Extended thinkingYesNo
Prompt cachingYesNo
Batch APINoNo
Release dateN/AN/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.

ProviderDatabricks Kimi K2 7 inDatabricks Kimi K2 7 outGov Nvidia Nemotron Nano 3 30b inGov Nvidia Nemotron Nano 3 30b out
Aws Bedrock——$0.072/M$0.288/M
Databricks$0.950/M$4.00/M——

Frequently asked questions

Gov Nvidia Nemotron Nano 3 30b has a larger context window: 262K 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
Key memories
Current turn

80% less to send — works with any model