Context Window

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Kimi K2 7 Code Highspeed vs Nemotron 3.5 Super VL

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

Kimi K2 7 Code Highspeed

Context window

262K

262,144 tokens · ~197K words

Model page
Nvidia

Model

Nemotron 3.5 Super VL

Image inputTool calling

Context window

1.0M

1,048,576 tokens · ~786K 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.

Kimi K2 7 Code Highspeed262K
Nemotron 3.5 Super VL1.0M

Nemotron 3.5 Super VL has about 4× the context window of the other in this pair.

Token calculator →Memory savings →

Nemotron 3.5 Super VL has 300% more context capacity (1048K vs 262K tokens). Nemotron 3.5 Super VL is 84% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Nemotron 3.5 Super VL. Its 1048K context fits entire documents without chunking (vs 262K).

  • RAG / high-volume retrieval

    Use Nemotron 3.5 Super VL. Input tokens are 84% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecKimi K2 7 Code HighspeedNemotron 3.5 Super VL
Context window262,144 tokens (262K)1,048,576 tokens (1048K)
Max output tokensN/A235,929 tokens (235K)
Speed tierBalancedBalanced
VisionNoYes
Function callingNoYes
Extended thinkingNoYes
Prompt cachingYesYes
Batch APINoNo
Release dateN/AOct 2026

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.

ProviderKimi K2 7 Code Highspeed inKimi K2 7 Code Highspeed outNemotron 3.5 Super VL inNemotron 3.5 Super VL out
Gmi$1.90/M$8.00/M——
Nebius——$0.300/M$0.900/M

Frequently asked questions

Nemotron 3.5 Super VL has a larger context window: 1048K 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.

Powered by Mem0

Use a smaller model.
Get better results.

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.

Calculate your savingsLearn about Mem0

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