Context Window

Compare

Databricks Meta Llama 3 70b vs Llama 2 70b Chat

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

Databricks Meta Llama 3 70b

Context window

128K

128,000 tokens · ~96K words

Model page
Meta

Model

Llama 2 70b Chat

Context window

4K

4,096 tokens · ~3K 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.

Databricks Meta Llama 3 70b128K
Llama 2 70b Chat4K

Databricks Meta Llama 3 70b has about 31.3× the context window of the other in this pair.

Token calculator →Memory savings →

Databricks Meta Llama 3 70b has 3025% more context capacity (128K vs 4K tokens). Llama 2 70b Chat is 0% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Databricks Meta Llama 3 70b. Its 128K context fits entire documents without chunking (vs 4K).

  • Long output (reports, code files)

    Use Databricks Meta Llama 3 70b. Its 128K 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.

SpecDatabricks Meta Llama 3 70bLlama 2 70b Chat
Context window128,000 tokens (128K)4,096 tokens (4K)
Max output tokens128,000 tokens (128K)4,096 tokens (4K)
Speed tierDeepDeep
VisionNoNo
Function callingNoNo
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.

ProviderDatabricks Meta Llama 3 70b inDatabricks Meta Llama 3 70b outLlama 2 70b Chat inLlama 2 70b Chat out
Anyscale——$1.00/M$1.00/M
Databricks$1.00/M$3.00/M——

Frequently asked questions

Databricks Meta Llama 3 70b has a larger context window: 128K 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.

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Use a smaller model.
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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