Context Window

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Command R Plus vs Gpt 4o Realtime Preview

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.

Cohere

Model

Command R Plus

Tool calling

Context window

128K

128,000 tokens · ~96K words

Model page
Openai

Model

Gpt 4o Realtime Preview

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.

Command R Plus128K
Gpt 4o Realtime Preview128K

Same context window size for both models.

Token calculator →Memory savings →

Command R Plus and Gpt 4o Realtime Preview have identical context windows (128K tokens). Command R Plus is 50% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • RAG / high-volume retrieval

    Use Command R Plus. 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.

SpecCommand R PlusGpt 4o Realtime Preview
Context window128,000 tokens (128K)128,000 tokens (128K)
Max output tokens4,096 tokens (4K)4,096 tokens (4K)
Speed tierBalancedBalanced
VisionNoNo
Function callingYesYes
Extended thinkingNoNo
Prompt cachingNoYes
Batch APINoYes
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.

ProviderCommand R Plus inCommand R Plus outGpt 4o Realtime Preview inGpt 4o Realtime Preview out
Azure$3.00/M$15.00/M——
Cohere$2.50/M$10.00/M——
Openai——$5.00/M$20.00/M

Frequently asked questions

Gpt 4o Realtime Preview has a larger context window: 128K 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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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.

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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