Context Window

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Mistral Nemo vs Phi 3 Vision 128k

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 Nemo

Tool calling

Context window

131K

131,072 tokens · ~98K words

Model page
Microsoft

Model

Phi 3 Vision 128k

Context window

32K

32,064 tokens · ~24K 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 Nemo131K
Phi 3 Vision 128k32K

Mistral Nemo has about 4.1× the context window of the other in this pair.

Token calculator →Memory savings →

Mistral Nemo has 308% more context capacity (131K vs 32K tokens). Mistral Nemo is 25% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Mistral Nemo. Its 131K context fits entire documents without chunking (vs 32K).

  • RAG / high-volume retrieval

    Use Mistral Nemo. Input tokens are 25% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Phi 3 Vision 128k. Its 32K 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.

SpecMistral NemoPhi 3 Vision 128k
Context window131,072 tokens (131K)32,064 tokens (32K)
Max output tokens4,096 tokens (4K)32,064 tokens (32K)
Speed tierBalancedBalanced
VisionNoNo
Function callingYesNo
Extended thinkingNoNo
Prompt cachingNoNo
Batch APINoNo
Release dateJul 2024N/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 Nemo inMistral Nemo outPhi 3 Vision 128k inPhi 3 Vision 128k out
Azure$0.150/M$0.150/M——
Fireworks——$0.200/M$0.200/M
Novita$0.040/M$0.170/M——

Frequently asked questions

Mistral Nemo has a larger context window: 131K tokens vs 32K. 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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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