Context Window

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Mistral 7b Instruct 4k 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 7b Instruct 4k

Context window

33K

32,768 tokens · ~25K 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 7b Instruct 4k33K
Phi 3 Vision 128k32K

Mistral 7b Instruct 4k has about 1× the context window of the other in this pair.

Token calculator →Memory savings →

Mistral 7b Instruct 4k has 2% more context capacity (32K vs 32K tokens). Phi 3 Vision 128k is 0% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Mistral 7b Instruct 4k. Its 32K context fits entire documents without chunking (vs 32K).

  • Long output (reports, code files)

    Use Mistral 7b Instruct 4k. 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 7b Instruct 4kPhi 3 Vision 128k
Context window32,768 tokens (32K)32,064 tokens (32K)
Max output tokens32,768 tokens (32K)32,064 tokens (32K)
Speed tierFastBalanced
VisionNoNo
Function callingNoNo
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 7b Instruct 4k inMistral 7b Instruct 4k outPhi 3 Vision 128k inPhi 3 Vision 128k out
Fireworks$0.200/M$0.200/M$0.200/M$0.200/M

Frequently asked questions

Mistral 7b Instruct 4k has a larger context window: 32K 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
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Repeated info
Old context
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