Context Window

Compare

Llama 2 70b Chat vs Llama 4 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.

Meta

Model

Llama 2 70b Chat

Context window

4K

4,096 tokens · ~3K words

Model page
Meta

Model

Llama 4 Maverick

Image inputTool calling

Context window

1.0M

1,048,576 tokens · ~786K 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 2 70b Chat4K
Llama 4 Maverick1.0M

Llama 4 Maverick has about 256× the context window of the other in this pair.

Token calculator →Memory savings →

Llama 4 Maverick has 25500% more context capacity (1048K vs 4K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Llama 4 Maverick. Its 1048K context fits entire documents without chunking (vs 4K).

  • Long output (reports, code files)

    Use Llama 4 Maverick. Its 16K 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.

SpecLlama 2 70b ChatLlama 4 Maverick
Context window4,096 tokens (4K)1,048,576 tokens (1048K)
Max output tokens4,096 tokens (4K)16,384 tokens (16K)
Speed tierDeepBalanced
VisionNoYes
Function callingNoYes
Extended thinkingNoNo
Prompt cachingNoNo
Batch APINoNo
Release dateN/AApr 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.

ProviderLlama 2 70b Chat inLlama 2 70b Chat outLlama 4 Maverick inLlama 4 Maverick out
Anyscale$1.00/M$1.00/M——

Frequently asked questions

Llama 4 Maverick has a larger context window: 1048K tokens vs 4K. 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