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Mistral Large 3 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.

Mistral

Model

Mistral Large 3

Image inputTool calling

Context window

256K

256,000 tokens · ~192K 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.

Mistral Large 3256K
Nvidia Nemotron 3 5 Lightning 30b A3b66K

Mistral Large 3 has about 3.9× the context window of the other in this pair.

Mistral Large 3 has 290% more context capacity (256K vs 65K tokens). Nvidia Nemotron 3 5 Lightning 30b A3b is 70% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Mistral Large 3. Its 256K context fits entire documents without chunking (vs 65K).

  • RAG / high-volume retrieval

    Use Nvidia Nemotron 3 5 Lightning 30b A3b. Input tokens are 70% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecMistral Large 3Nvidia Nemotron 3 5 Lightning 30b A3b
Context window256,000 tokens (256K)65,536 tokens (65K)
Max output tokens8,191 tokens (8K)N/A
Speed tierDeepFast
VisionYesNo
Function callingYesNo
Extended thinkingNoNo
Prompt cachingYesYes
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.

ProviderMistral Large 3 inMistral Large 3 outNvidia Nemotron 3 5 Lightning 30b A3b inNvidia Nemotron 3 5 Lightning 30b A3b out
Azure$0.500/M$1.50/M——
Gmi——$0.150/M$1.00/M
Mistral$0.500/M$1.50/M——

Frequently asked questions

Mistral Large 3 has a larger context window: 256K 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.

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