Context Window

Compare

Gpt 4o Mini Audio Preview vs Qwen Qwen3 32b

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

Qwen Qwen3 32b

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 Mini Audio Preview128K
Qwen Qwen3 32b131K

Qwen Qwen3 32b has about 1× the context window of the other in this pair.

Token calculator →Memory savings →

Qwen Qwen3 32b has 2% more context capacity (131K vs 128K tokens). Qwen Qwen3 32b is 0% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Qwen Qwen3 32b. Its 131K context fits entire documents without chunking (vs 128K).

Full specs

Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.

SpecGpt 4o Mini Audio PreviewQwen Qwen3 32b
Context window128,000 tokens (128K)131,072 tokens (131K)
Max output tokens16,384 tokens (16K)16,384 tokens (16K)
Speed tierFastBalanced
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 outQwen Qwen3 32b inQwen Qwen3 32b out
Aws Bedrock——$0.150/M$0.600/M
Openai$0.150/M$0.600/M——

Frequently asked questions

Qwen Qwen3 32b 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