Context Window

Compare

Gpt 4o Mini Audio Preview vs Qwen3 5 Plus

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
Alibaba

Model

Qwen3 5 Plus

Image inputTool calling

Context window

992K

991,808 tokens · ~744K 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
Qwen3 5 Plus992K

Qwen3 5 Plus has about 7.7× the context window of the other in this pair.

Token calculator →Memory savings →

Qwen3 5 Plus has 674% more context capacity (991K vs 128K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Qwen3 5 Plus. Its 991K context fits entire documents without chunking (vs 128K).

  • Long output (reports, code files)

    Use Qwen3 5 Plus. Its 65K 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 PreviewQwen3 5 Plus
Context window128,000 tokens (128K)991,808 tokens (991K)
Max output tokens16,384 tokens (16K)65,536 tokens (65K)
Speed tierFastBalanced
VisionNoYes
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 outQwen3 5 Plus inQwen3 5 Plus out
Alibaba Cloud————
Openai$0.150/M$0.600/M——

Frequently asked questions

Qwen3 5 Plus has a larger context window: 991K 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.

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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