Context Window

Compare

Llama 3 70B Instruct vs Mistral Large 2402

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 3 70B Instruct

Context window

8K

8,192 tokens · ~6K words

Model page
Mistral

Model

Mistral Large 2402

Tool calling

Context window

32K

32,000 tokens · ~24K 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 3 70B Instruct8K
Mistral Large 240232K

Mistral Large 2402 has about 3.9× the context window of the other in this pair.

Token calculator →Memory savings →

Mistral Large 2402 has 290% more context capacity (32K vs 8K tokens). Llama 3 70B Instruct is 87% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Mistral Large 2402. Its 32K context fits entire documents without chunking (vs 8K).

  • RAG / high-volume retrieval

    Use Llama 3 70B Instruct. Input tokens are 87% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecLlama 3 70B InstructMistral Large 2402
Context window8,192 tokens (8K)32,000 tokens (32K)
Max output tokens8,000 tokens (8K)N/A
Speed tierDeepDeep
VisionNoNo
Function callingNoYes
Extended thinkingNoNo
Prompt cachingNoNo
Batch APINoNo
Release dateApr 2024N/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 3 70B Instruct inLlama 3 70B Instruct outMistral Large 2402 inMistral Large 2402 out
Azure——$8.00/M$24.00/M
Mistral——$4.00/M$12.00/M
Novita$0.510/M$0.740/M——
Openrouter$0.590/M$0.790/M——
Replicate$0.650/M$2.75/M——

Frequently asked questions

Mistral Large 2402 has a larger context window: 32K tokens vs 8K. 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