Context Window

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Grok 2 Vision 1212 vs Llama4 Maverick

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.

Xai

Model

Grok 2 Vision 1212

Image inputTool calling

Context window

33K

32,768 tokens · ~25K words

Model page
Meta

Model

Llama4 Maverick

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.

Grok 2 Vision 121233K
Llama4 Maverick128K

Llama4 Maverick has about 3.9× the context window of the other in this pair.

Token calculator →Memory savings →

Llama4 Maverick has 290% more context capacity (128K vs 32K tokens). Llama4 Maverick is 88% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Llama4 Maverick. Its 128K context fits entire documents without chunking (vs 32K).

  • RAG / high-volume retrieval

    Use Llama4 Maverick. Input tokens are 88% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Grok 2 Vision 1212. Its 32K 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.

SpecGrok 2 Vision 1212Llama4 Maverick
Context window32,768 tokens (32K)128,000 tokens (128K)
Max output tokens32,768 tokens (32K)16,384 tokens (16K)
Speed tierBalancedBalanced
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.

ProviderGrok 2 Vision 1212 inGrok 2 Vision 1212 outLlama4 Maverick inLlama4 Maverick out
Snowflake——$0.240/M$0.970/M
Xai$2.00/M$10.00/M——

Frequently asked questions

Llama4 Maverick has a larger context window: 128K tokens vs 32K. 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.
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.

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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