Context Window

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Meta Llama 3 70b Instruct vs ReMM SLERP 13B

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

Meta Llama 3 70b Instruct

Context window

8K

8,192 tokens · ~6K words

Model page
Undi95

Model

ReMM SLERP 13B

Context window

6K

6,144 tokens · ~5K 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.

Meta Llama 3 70b Instruct8K
ReMM SLERP 13B6K

Meta Llama 3 70b Instruct has about 1.3× the context window of the other in this pair.

Token calculator →Memory savings →

Meta Llama 3 70b Instruct has 33% more context capacity (8K vs 6K tokens). ReMM SLERP 13B is 60% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Meta Llama 3 70b Instruct. Its 8K context fits entire documents without chunking (vs 6K).

  • RAG / high-volume retrieval

    Use ReMM SLERP 13B. Input tokens are 60% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecMeta Llama 3 70b InstructReMM SLERP 13B
Context window8,192 tokens (8K)6,144 tokens (6K)
Max output tokensN/A4,096 tokens (4K)
Speed tierDeepFast
VisionNoNo
Function callingNoNo
Extended thinkingNoNo
Prompt cachingNoNo
Batch APINoNo
Release dateN/AJul 2023

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.

ProviderMeta Llama 3 70b Instruct inMeta Llama 3 70b Instruct outReMM SLERP 13B inReMM SLERP 13B out
Openrouter——$0.350/M$0.650/M
Together Ai$0.880/M$0.880/M——

Frequently asked questions

Meta Llama 3 70b Instruct has a larger context window: 8K tokens vs 6K. 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.

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