Context Window

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Meta Llama3 2 11b Instruct vs Moonshot V1 Auto

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
Moonshot

Model

Moonshot V1 Auto

Tool calling

Context window

131K

131,072 tokens · ~98K 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
Moonshot V1 Auto131K

Moonshot V1 Auto has about 1× the context window of the other in this pair.

Token calculator →Memory savings →

Moonshot V1 Auto has 2% more context capacity (131K vs 128K tokens). Meta Llama3 2 11b Instruct is 82% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Moonshot V1 Auto. Its 131K context fits entire documents without chunking (vs 128K).

  • RAG / high-volume retrieval

    Use Meta Llama3 2 11b Instruct. Input tokens are 82% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Moonshot V1 Auto. Its 131K 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 InstructMoonshot V1 Auto
Context window128,000 tokens (128K)131,072 tokens (131K)
Max output tokens4,096 tokens (4K)131,072 tokens (131K)
Speed tierFastBalanced
VisionYesNo
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 2 11b Instruct inMeta Llama3 2 11b Instruct outMoonshot V1 Auto inMoonshot V1 Auto out
Aws Bedrock$0.350/M$0.350/M——
Moonshot——$2.00/M$5.00/M

Frequently asked questions

Moonshot V1 Auto has a larger context window: 131K 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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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