Context Window

Compare

Gpt 4o Realtime Preview 2025 06 03 vs gpt-oss-20b (free)

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
Openai

Model

gpt-oss-20b (free)

Tool calling

Context window

131K

131,072 tokens · ~98K 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
gpt-oss-20b (free)131K

gpt-oss-20b (free) has about 1× the context window of the other in this pair.

Token calculator →Memory savings →

gpt-oss-20b (free) has 2% more context capacity (131K vs 128K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use gpt-oss-20b (free). Its 131K context fits entire documents without chunking (vs 128K).

  • Long output (reports, code files)

    Use gpt-oss-20b (free). Its 131K 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.

SpecGpt 4o Realtime Preview 2025 06 03gpt-oss-20b (free)
Context window128,000 tokens (128K)131,072 tokens (131K)
Max output tokens4,096 tokens (4K)131,072 tokens (131K)
Speed tierBalancedBalanced
VisionNoNo
Function callingYesYes
Extended thinkingNoYes
Prompt cachingYesNo
Batch APIYesNo
Release dateN/AAug 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.

ProviderGpt 4o Realtime Preview 2025 06 03 inGpt 4o Realtime Preview 2025 06 03 outgpt-oss-20b (free) ingpt-oss-20b (free) out
Openai$5.00/M$20.00/M——

Frequently asked questions

gpt-oss-20b (free) has a larger context window: 131K 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