Context Window

Compare

Gpt 4o Mini Audio Preview vs Kimi K2 Thinking 251104

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 Mini Audio Preview

Tool calling

Context window

128K

128,000 tokens · ~96K words

Model page
Moonshot

Model

Kimi K2 Thinking 251104

Tool calling

Context window

229K

229,376 tokens · ~172K 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 Mini Audio Preview128K
Kimi K2 Thinking 251104229K

Kimi K2 Thinking 251104 has about 1.8× the context window of the other in this pair.

Token calculator →Memory savings →

Kimi K2 Thinking 251104 has 79% more context capacity (229K vs 128K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Kimi K2 Thinking 251104. Its 229K context fits entire documents without chunking (vs 128K).

  • Long output (reports, code files)

    Use Kimi K2 Thinking 251104. Its 32K 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 Mini Audio PreviewKimi K2 Thinking 251104
Context window128,000 tokens (128K)229,376 tokens (229K)
Max output tokens16,384 tokens (16K)32,768 tokens (32K)
Speed tierFastDeep
VisionNoNo
Function callingYesYes
Extended thinkingNoYes
Prompt cachingNoNo
Batch APIYesNo
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.

ProviderGpt 4o Mini Audio Preview inGpt 4o Mini Audio Preview outKimi K2 Thinking 251104 inKimi K2 Thinking 251104 out
Openai$0.150/M$0.600/M——
Volcengine————

Frequently asked questions

Kimi K2 Thinking 251104 has a larger context window: 229K 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.
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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