Context Window

Compare

Llama 2 13b Chat vs Meta Llama 3 8b

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 13b Chat

Context window

4K

4,096 tokens · ~3K words

Model page
Meta

Model

Meta Llama 3 8b

Context window

8K

8,192 tokens · ~6K 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 13b Chat4K
Meta Llama 3 8b8K

Meta Llama 3 8b has about 2× the context window of the other in this pair.

Token calculator →Memory savings →

Meta Llama 3 8b has 100% more context capacity (8K vs 4K tokens). Meta Llama 3 8b is 88% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Meta Llama 3 8b. Its 8K context fits entire documents without chunking (vs 4K).

  • RAG / high-volume retrieval

    Use Meta Llama 3 8b. Input tokens are 88% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Meta Llama 3 8b. Its 8K 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 13b ChatMeta Llama 3 8b
Context window4,096 tokens (4K)8,192 tokens (8K)
Max output tokens4,096 tokens (4K)8,192 tokens (8K)
Speed tierFastFast
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.

ProviderLlama 2 13b Chat inLlama 2 13b Chat outMeta Llama 3 8b inMeta Llama 3 8b out
Anyscale$0.250/M$0.250/M$0.150/M$0.150/M
Deepinfra——$0.030/M$0.060/M

Frequently asked questions

Meta Llama 3 8b has a larger context window: 8K 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