Context Window

Compare

Meta Llama3 1 70b Instruct vs Ministral 3b

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 1 70b Instruct

Tool calling

Context window

128K

128,000 tokens · ~96K words

Model page
Mistral

Model

Ministral 3b

Tool calling

Context window

128K

128,000 tokens · ~96K 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 1 70b Instruct128K
Ministral 3b128K

Same context window size for both models.

Token calculator →Memory savings →

Meta Llama3 1 70b Instruct and Ministral 3b have identical context windows (128K tokens). Ministral 3b is 95% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • RAG / high-volume retrieval

    Use Ministral 3b. Input tokens are 95% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Ministral 3b. Its 4K 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 1 70b InstructMinistral 3b
Context window128,000 tokens (128K)128,000 tokens (128K)
Max output tokens2,048 tokens (2K)4,096 tokens (4K)
Speed tierDeepFast
VisionNoNo
Function callingYesYes
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.

ProviderMeta Llama3 1 70b Instruct inMeta Llama3 1 70b Instruct outMinistral 3b inMinistral 3b out
Aws Bedrock$0.990/M$0.990/M——
Azure——$0.040/M$0.040/M

Frequently asked questions

Ministral 3b has a larger context window: 128K 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.

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