Context Window

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Mistral Large 2407 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.

Mistral

Model

Mistral Large 2407

Tool calling

Context window

128K

128,000 tokens · ~96K 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.

Mistral Large 2407128K
ReMM SLERP 13B6K

Mistral Large 2407 has about 20.8× the context window of the other in this pair.

Token calculator →Memory savings →

Mistral Large 2407 has 1983% more context capacity (128K vs 6K tokens). ReMM SLERP 13B is 37% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Mistral Large 2407. Its 128K context fits entire documents without chunking (vs 6K).

  • RAG / high-volume retrieval

    Use ReMM SLERP 13B. Input tokens are 37% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecMistral Large 2407ReMM SLERP 13B
Context window128,000 tokens (128K)6,144 tokens (6K)
Max output tokens4,096 tokens (4K)4,096 tokens (4K)
Speed tierDeepFast
VisionNoNo
Function callingYesNo
Extended thinkingNoNo
Prompt cachingYesNo
Batch APINoNo
Release dateNov 2024Jul 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.

ProviderMistral Large 2407 inMistral Large 2407 outReMM SLERP 13B inReMM SLERP 13B out
Azure$2.00/M$6.00/M——
Google Vertex$2.00/M$6.00/M——
Mistral$3.00/M$9.00/M——
Openrouter——$1.88/M$1.88/M

Frequently asked questions

Mistral Large 2407 has a larger context window: 128K 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
Full history
Repeated info
Old context
With Mem0~20K tokens sent
Key memories
Current turn

80% less to send — works with any model