Context Window

Compare

Gpt Realtime 1 5 vs Mistral 7b V0p2

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 1 5

Tool calling

Context window

32K

32,000 tokens · ~24K words

Model page
Mistral

Model

Mistral 7b V0p2

Context window

33K

32,768 tokens · ~25K 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 Realtime 1 532K
Mistral 7b V0p233K

Mistral 7b V0p2 has about 1× the context window of the other in this pair.

Token calculator →Memory savings →

Mistral 7b V0p2 has 2% more context capacity (32K vs 32K tokens). Mistral 7b V0p2 is 95% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Mistral 7b V0p2. Its 32K context fits entire documents without chunking (vs 32K).

  • RAG / high-volume retrieval

    Use Mistral 7b V0p2. Input tokens are 95% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Mistral 7b V0p2. Its 32K 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 Realtime 1 5Mistral 7b V0p2
Context window32,000 tokens (32K)32,768 tokens (32K)
Max output tokens4,096 tokens (4K)32,768 tokens (32K)
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 1 5 inGpt Realtime 1 5 outMistral 7b V0p2 inMistral 7b V0p2 out
Fireworks——$0.200/M$0.200/M
Openai$4.00/M$16.00/M——

Frequently asked questions

Mistral 7b V0p2 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.

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Without Mem0~128K tokens sent
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