Context Window

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Gpt 4o Realtime Preview 2025 06 03 vs Mistral Medium 3.5

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 4o Realtime Preview 2025 06 03

Tool calling

Context window

128K

128,000 tokens · ~96K words

Model page
Mistral

Model

Mistral Medium 3.5

Image inputTool calling

Context window

262K

262,144 tokens · ~197K 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 4o Realtime Preview 2025 06 03128K
Mistral Medium 3.5262K

Mistral Medium 3.5 has about 2× the context window of the other in this pair.

Token calculator →Memory savings →

Mistral Medium 3.5 has 104% more context capacity (262K vs 128K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Mistral Medium 3.5. Its 262K context fits entire documents without chunking (vs 128K).

Full specs

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

SpecGpt 4o Realtime Preview 2025 06 03Mistral Medium 3.5
Context window128,000 tokens (128K)262,144 tokens (262K)
Max output tokens4,096 tokens (4K)N/A
Speed tierBalancedBalanced
VisionNoYes
Function callingYesYes
Extended thinkingNoYes
Prompt cachingYesNo
Batch APIYesNo
Release dateN/AApr 2026

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 4o Realtime Preview 2025 06 03 inGpt 4o Realtime Preview 2025 06 03 outMistral Medium 3.5 inMistral Medium 3.5 out
Openai$5.00/M$20.00/M——

Frequently asked questions

Mistral Medium 3.5 has a larger context window: 262K tokens vs 128K. 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