Context Window

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DeepSeek V3.1 vs Nvidia Nemotron 3 5 Lightning 30b A3b

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.

Deepseek

Model

DeepSeek V3.1

Tool calling

Context window

164K

163,840 tokens · ~123K words

Model page
Nvidia

Model

Nvidia Nemotron 3 5 Lightning 30b A3b

Context window

66K

65,536 tokens · ~49K 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.

DeepSeek V3.1164K
Nvidia Nemotron 3 5 Lightning 30b A3b66K

DeepSeek V3.1 has about 2.5× the context window of the other in this pair.

Token calculator →Memory savings →

DeepSeek V3.1 has 150% more context capacity (163K vs 65K tokens). Nvidia Nemotron 3 5 Lightning 30b A3b is 40% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use DeepSeek V3.1. Its 163K context fits entire documents without chunking (vs 65K).

  • RAG / high-volume retrieval

    Use Nvidia Nemotron 3 5 Lightning 30b A3b. Input tokens are 40% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecDeepSeek V3.1Nvidia Nemotron 3 5 Lightning 30b A3b
Context window163,840 tokens (163K)65,536 tokens (65K)
Max output tokens163,840 tokens (163K)N/A
Speed tierBalancedFast
VisionNoNo
Function callingYesNo
Extended thinkingYesNo
Prompt cachingYesYes
Batch APINoNo
Release dateAug 2025N/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.

ProviderDeepSeek V3.1 inDeepSeek V3.1 outNvidia Nemotron 3 5 Lightning 30b A3b inNvidia Nemotron 3 5 Lightning 30b A3b out
Gmi——$0.150/M$1.00/M
Openrouter$0.250/M$0.950/M——

Frequently asked questions

DeepSeek V3.1 has a larger context window: 163K tokens vs 65K. 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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Use a smaller model.
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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