Context Window

Compare

Gpt Realtime 1 5 vs Mistral Pixtral Large 2502

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 Pixtral Large 2502

Tool calling

Context window

128K

128,000 tokens · ~96K 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 Pixtral Large 2502128K

Mistral Pixtral Large 2502 has about 4× the context window of the other in this pair.

Token calculator →Memory savings →

Mistral Pixtral Large 2502 has 300% more context capacity (128K vs 32K tokens). Mistral Pixtral Large 2502 is 50% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Mistral Pixtral Large 2502. Its 128K context fits entire documents without chunking (vs 32K).

  • RAG / high-volume retrieval

    Use Mistral Pixtral Large 2502. Input tokens are 50% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecGpt Realtime 1 5Mistral Pixtral Large 2502
Context window32,000 tokens (32K)128,000 tokens (128K)
Max output tokens4,096 tokens (4K)4,096 tokens (4K)
Speed tierBalancedDeep
VisionNoNo
Function callingYesYes
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 Pixtral Large 2502 inMistral Pixtral Large 2502 out
Aws Bedrock——$2.00/M$6.00/M
Openai$4.00/M$16.00/M——

Frequently asked questions

Mistral Pixtral Large 2502 has a larger context window: 128K 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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Use a smaller model.
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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