Context Window

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ReMM SLERP 13B vs Snowflake Llama 3 1 405b

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.

Undi95

Model

ReMM SLERP 13B

Context window

6K

6,144 tokens · ~5K words

Model page
Meta

Model

Snowflake Llama 3 1 405b

Context window

8K

8,000 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.

ReMM SLERP 13B6K
Snowflake Llama 3 1 405b8K

Snowflake Llama 3 1 405b has about 1.3× the context window of the other in this pair.

Token calculator →Memory savings →

Snowflake Llama 3 1 405b has 30% more context capacity (8K vs 6K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Snowflake Llama 3 1 405b. Its 8K context fits entire documents without chunking (vs 6K).

  • Long output (reports, code files)

    Use Snowflake Llama 3 1 405b. 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.

SpecReMM SLERP 13BSnowflake Llama 3 1 405b
Context window6,144 tokens (6K)8,000 tokens (8K)
Max output tokens4,096 tokens (4K)8,192 tokens (8K)
Speed tierFastDeep
VisionNoNo
Function callingNoNo
Extended thinkingNoNo
Prompt cachingNoNo
Batch APINoNo
Release dateJul 2023N/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.

ProviderReMM SLERP 13B inReMM SLERP 13B outSnowflake Llama 3 1 405b inSnowflake Llama 3 1 405b out
Openrouter$1.88/M$1.88/M——
Snowflake————

Frequently asked questions

Snowflake Llama 3 1 405b 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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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
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Repeated info
Old context
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