Compare

Gemma 4 26B A4B vs Gpt Realtime

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.

Google

Model

Gemma 4 26B A4B

Image inputTool calling

Context window

262K

262,144 tokens · ~197K words

Model page
Openai

Model

Gpt Realtime

Tool calling

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.

Gemma 4 26B A4B262K
Gpt Realtime32K

Gemma 4 26B A4B has about 8.2× the context window of the other in this pair.

Gemma 4 26B A4B has 719% more context capacity (262K vs 32K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Gemma 4 26B A4B. Its 262K context fits entire documents without chunking (vs 32K).

  • Long output (reports, code files)

    Use Gemma 4 26B A4B. Its 262K 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.

SpecGemma 4 26B A4BGpt Realtime
Context window262,144 tokens (262K)32,000 tokens (32K)
Max output tokens262,144 tokens (262K)4,096 tokens (4K)
Speed tierBalancedBalanced
VisionYesNo
Function callingYesYes
Extended thinkingYesNo
Prompt cachingNoYes
Batch APINoNo
Release dateApr 2026N/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.

ProviderGemma 4 26B A4B inGemma 4 26B A4B outGpt Realtime inGpt Realtime out
Openai$4.00/M$16.00/M

Frequently asked questions

Gemma 4 26B A4B has a larger context window: 262K 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.

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

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