Context Window

Compare

Gpt Realtime 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.

Openai

Model

Gpt Realtime

Tool calling

Context window

32K

32,000 tokens · ~24K 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.

Gpt Realtime32K
Mistral Mixtral 8x7b Instruct32K

Same context window size for both models.

Token calculator →Memory savings →

Gpt Realtime and Mistral Mixtral 8x7b Instruct have identical context windows (32K tokens). Mistral Mixtral 8x7b Instruct is 85% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • RAG / high-volume retrieval

    Use Mistral Mixtral 8x7b Instruct. Input tokens are 85% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Mistral Mixtral 8x7b Instruct. 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.

SpecGpt RealtimeMistral Mixtral 8x7b Instruct
Context window32,000 tokens (32K)32,000 tokens (32K)
Max output tokens4,096 tokens (4K)8,191 tokens (8K)
Speed tierBalancedFast
VisionNoNo
Function callingYesNo
Extended thinkingNoNo
Prompt cachingYesNo
Batch APINoNo
Release dateN/AN/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.

ProviderGpt Realtime inGpt Realtime outMistral Mixtral 8x7b Instruct inMistral Mixtral 8x7b Instruct out
Aws Bedrock——$0.590/M$0.910/M
Openai$4.00/M$16.00/M——

Frequently asked questions

Mistral Mixtral 8x7b Instruct has a larger context window: 32K 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.

Powered by Mem0

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