Context Window

Compare

Meta Llama3 2 11b Instruct vs Ministral 3 14B 2512

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.

Meta

Model

Meta Llama3 2 11b Instruct

Image inputTool calling

Context window

128K

128,000 tokens · ~96K words

Model page
Mistral

Model

Ministral 3 14B 2512

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.

Meta Llama3 2 11b Instruct128K
Ministral 3 14B 2512262K

Ministral 3 14B 2512 has about 2× the context window of the other in this pair.

Token calculator →Memory savings →

Ministral 3 14B 2512 has 104% more context capacity (262K vs 128K tokens). Ministral 3 14B 2512 is 42% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Ministral 3 14B 2512. Its 262K context fits entire documents without chunking (vs 128K).

  • RAG / high-volume retrieval

    Use Ministral 3 14B 2512. Input tokens are 42% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Ministral 3 14B 2512. 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.

SpecMeta Llama3 2 11b InstructMinistral 3 14B 2512
Context window128,000 tokens (128K)262,144 tokens (262K)
Max output tokens4,096 tokens (4K)262,144 tokens (262K)
Speed tierFastFast
VisionYesYes
Function callingYesYes
Extended thinkingNoNo
Prompt cachingNoYes
Batch APINoNo
Release dateN/ADec 2025

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.

ProviderMeta Llama3 2 11b Instruct inMeta Llama3 2 11b Instruct outMinistral 3 14B 2512 inMinistral 3 14B 2512 out
Aws Bedrock$0.350/M$0.350/M——
Openrouter——$0.200/M$0.200/M

Frequently asked questions

Ministral 3 14B 2512 has a larger context window: 262K tokens vs 128K. 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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Use a smaller model.
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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