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DeepSeek V4 Pro vs Nvidia Nemotron 3 5 Lightning

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 V4 Pro

Tool calling

Context window

1.0M

1,048,576 tokens · ~786K words

Model page
Nvidia

Model

Nvidia Nemotron 3 5 Lightning

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.

DeepSeek V4 Pro1.0M
Nvidia Nemotron 3 5 Lightning262K

DeepSeek V4 Pro has about 4× the context window of the other in this pair.

DeepSeek V4 Pro has 300% more context capacity (1048K vs 262K tokens). Nvidia Nemotron 3 5 Lightning is 88% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use DeepSeek V4 Pro. Its 1048K context fits entire documents without chunking (vs 262K).

  • RAG / high-volume retrieval

    Use Nvidia Nemotron 3 5 Lightning. Input tokens are 88% 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 V4 ProNvidia Nemotron 3 5 Lightning
Context window1,048,576 tokens (1048K)262,144 tokens (262K)
Max output tokens384,000 tokens (384K)N/A
Speed tierBalancedBalanced
VisionNoNo
Function callingYesYes
Extended thinkingYesYes
Prompt cachingYesNo
Batch APINoNo
Release dateApr 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.

ProviderDeepSeek V4 Pro inDeepSeek V4 Pro outNvidia Nemotron 3 5 Lightning inNvidia Nemotron 3 5 Lightning out
Alibaba Cloud$2.40/M$4.80/M
Azure$1.74/M$3.48/M
Deepinfra$0.050/M$0.200/M
Deepseek$0.435/M$0.870/M
Fireworks$1.74/M$3.48/M

Frequently asked questions

DeepSeek V4 Pro 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.

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