Context Window

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Llama 3.3 70B Instruct (free) vs Mistral Mixtral 8x7b Instruct

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 3.3 70B Instruct (free)

Tool calling

Context window

66K

65,536 tokens · ~49K words

Model page
Mistral

Model

Mistral Mixtral 8x7b Instruct

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 3.3 70B Instruct (free)66K
Mistral Mixtral 8x7b Instruct32K

Llama 3.3 70B Instruct (free) has about 2× the context window of the other in this pair.

Token calculator →Memory savings →

Llama 3.3 70B Instruct (free) has 104% more context capacity (65K vs 32K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Llama 3.3 70B Instruct (free). Its 65K context fits entire documents without chunking (vs 32K).

Full specs

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

SpecLlama 3.3 70B Instruct (free)Mistral Mixtral 8x7b Instruct
Context window65,536 tokens (65K)32,000 tokens (32K)
Max output tokensN/A8,191 tokens (8K)
Speed tierDeepFast
VisionNoNo
Function callingYesNo
Extended thinkingNoNo
Prompt cachingNoNo
Batch APINoNo
Release dateDec 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 3.3 70B Instruct (free) inLlama 3.3 70B Instruct (free) outMistral Mixtral 8x7b Instruct inMistral Mixtral 8x7b Instruct out
Aws Bedrock——$0.590/M$0.910/M

Frequently asked questions

Llama 3.3 70B Instruct (free) has a larger context window: 65K tokens vs 32K. 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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Example: a multi-turn chat session

Without Mem0~128K tokens sent
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