Context Window

Compare

Llama 3 2 11b Vision vs Meta Llama3 1 70b Instruct

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

Llama 3 2 11b Vision

Image inputTool calling

Context window

128K

128,000 tokens · ~96K words

Model page
Meta

Model

Meta Llama3 1 70b Instruct

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.

Llama 3 2 11b Vision128K
Meta Llama3 1 70b Instruct128K

Same context window size for both models.

Token calculator →Memory savings →

Llama 3 2 11b Vision and Meta Llama3 1 70b Instruct have identical context windows (128K tokens). Llama 3 2 11b Vision is 95% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • RAG / high-volume retrieval

    Use Llama 3 2 11b Vision. Input tokens are 95% cheaper — critical when sending large retrieved contexts.

Full specs

Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.

SpecLlama 3 2 11b VisionMeta Llama3 1 70b Instruct
Context window128,000 tokens (128K)128,000 tokens (128K)
Max output tokens2,048 tokens (2K)2,048 tokens (2K)
Speed tierFastDeep
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.

ProviderLlama 3 2 11b Vision inLlama 3 2 11b Vision outMeta Llama3 1 70b Instruct inMeta Llama3 1 70b Instruct out
Aws Bedrock——$0.990/M$0.990/M
Azure$0.370/M$0.370/M——
Deepinfra$0.049/M$0.049/M——
Ibm Watsonx$0.350/M$0.350/M——

Frequently asked questions

Meta Llama3 1 70b Instruct 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