Context Window

Compare

Mistral Large 2402 vs Nemotron Nano V2 12b 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.

Mistral

Model

Mistral Large 2402

Tool calling

Context window

32K

32,000 tokens · ~24K words

Model page
Nvidia

Model

Nemotron Nano V2 12b Vl

Context window

4K

4,096 tokens · ~3K 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 240232K
Nemotron Nano V2 12b Vl4K

Mistral Large 2402 has about 7.8× the context window of the other in this pair.

Token calculator →Memory savings →

Mistral Large 2402 has 681% more context capacity (32K vs 4K tokens). Nemotron Nano V2 12b Vl is 97% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Mistral Large 2402. Its 32K context fits entire documents without chunking (vs 4K).

  • RAG / high-volume retrieval

    Use Nemotron Nano V2 12b Vl. Input tokens are 97% 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 2402Nemotron Nano V2 12b Vl
Context window32,000 tokens (32K)4,096 tokens (4K)
Max output tokensN/A4,096 tokens (4K)
Speed tierDeepFast
VisionNoNo
Function callingYesNo
Extended thinkingNoNo
Prompt cachingNoNo
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 2402 inMistral Large 2402 outNemotron Nano V2 12b Vl inNemotron Nano V2 12b Vl out
Azure$8.00/M$24.00/M——
Fireworks——$0.100/M$0.100/M
Mistral$4.00/M$12.00/M——

Frequently asked questions

Mistral Large 2402 has a larger context window: 32K tokens vs 4K. 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