Context Window

Compare

Gemini Exp 1206 vs GPT-4o Search 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.

Google

Model

Gemini Exp 1206

Image inputTool calling

Context window

2.1M

2,097,152 tokens · ~1.6M words

Model page
Openai

Model

GPT-4o Search Preview

Image inputTool 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.

Gemini Exp 12062.1M
GPT-4o Search Preview128K

Gemini Exp 1206 has about 16.4× the context window of the other in this pair.

Token calculator →Memory savings →

Gemini Exp 1206 has 1538% more context capacity (2097K vs 128K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Gemini Exp 1206. Its 2097K context fits entire documents without chunking (vs 128K).

  • Long output (reports, code files)

    Use GPT-4o Search Preview. Its 16K 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.

SpecGemini Exp 1206GPT-4o Search Preview
Context window2,097,152 tokens (2097K)128,000 tokens (128K)
Max output tokens8,192 tokens (8K)16,384 tokens (16K)
Speed tierFastBalanced
VisionYesYes
Function callingYesYes
Extended thinkingNoNo
Prompt cachingNoYes
Batch APINoYes
Release dateN/AMar 2025

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.

ProviderGemini Exp 1206 inGemini Exp 1206 outGPT-4o Search Preview inGPT-4o Search Preview out
Google————
Openai——$2.50/M$10.00/M

Frequently asked questions

Gemini Exp 1206 has a larger context window: 2097K 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.

Powered by Mem0

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.

Calculate your savingsLearn about Mem0

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