Context Window

Compare

Deepseek V3 2 251201 vs Ministral 8b Latest

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 V3 2 251201

Tool calling

Context window

98K

98,304 tokens · ~74K words

Model page
Mistral

Model

Ministral 8b Latest

Image inputTool 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 V3 2 25120198K
Ministral 8b Latest262K

Ministral 8b Latest has about 2.7× the context window of the other in this pair.

Token calculator →Memory savings →

Ministral 8b Latest has 166% more context capacity (262K vs 98K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Ministral 8b Latest. Its 262K context fits entire documents without chunking (vs 98K).

  • Long output (reports, code files)

    Use Ministral 8b Latest. Its 262K 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.

SpecDeepseek V3 2 251201Ministral 8b Latest
Context window98,304 tokens (98K)262,144 tokens (262K)
Max output tokens32,768 tokens (32K)262,144 tokens (262K)
Speed tierBalancedFast
VisionNoYes
Function callingYesYes
Extended thinkingYesNo
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.

ProviderDeepseek V3 2 251201 inDeepseek V3 2 251201 outMinistral 8b Latest inMinistral 8b Latest out
Mistral——$0.150/M$0.150/M
Volcengine————

Frequently asked questions

Ministral 8b Latest has a larger context window: 262K tokens vs 98K. 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