Context Window

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Nemotron 3.5 Super VL vs Openai Gpt 6 1 Terra

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 3.5 Super VL

Image inputTool calling

Context window

1.0M

1,048,576 tokens · ~786K words

Model page
Openai

Model

Openai Gpt 6 1 Terra

Image inputTool calling

Context window

922K

922,000 tokens · ~692K 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 3.5 Super VL1.0M
Openai Gpt 6 1 Terra922K

Nemotron 3.5 Super VL has about 1.1× the context window of the other in this pair.

Token calculator →Memory savings →

Nemotron 3.5 Super VL has 13% more context capacity (1048K vs 922K tokens).

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 922K).

  • Long output (reports, code files)

    Use Nemotron 3.5 Super VL. Its 235K 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 3.5 Super VLOpenai Gpt 6 1 Terra
Context window1,048,576 tokens (1048K)922,000 tokens (922K)
Max output tokens235,929 tokens (235K)128,000 tokens (128K)
Speed tierBalancedBalanced
VisionYesYes
Function callingYesYes
Extended thinkingYesYes
Prompt cachingYesYes
Batch APINoNo
Release dateOct 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 3.5 Super VL inNemotron 3.5 Super VL outOpenai Gpt 6 1 Terra inOpenai Gpt 6 1 Terra out
Aws Bedrock——$1.00/M$5.00/M

Frequently asked questions

Nemotron 3.5 Super VL has a larger context window: 1048K tokens vs 922K. 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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