Context Window

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Gpt 4o Mini Audio Preview vs Qwen3 VL 30B A3B Instruct

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 VL 30B A3B Instruct

Image inputTool 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
Qwen3 VL 30B A3B Instruct131K

Qwen3 VL 30B A3B Instruct has about 1× the context window of the other in this pair.

Token calculator →Memory savings →

Qwen3 VL 30B A3B Instruct has 2% more context capacity (131K vs 128K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Qwen3 VL 30B A3B Instruct. Its 131K context fits entire documents without chunking (vs 128K).

  • Long output (reports, code files)

    Use Qwen3 VL 30B A3B Instruct. 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 PreviewQwen3 VL 30B A3B Instruct
Context window128,000 tokens (128K)131,072 tokens (131K)
Max output tokens16,384 tokens (16K)32,768 tokens (32K)
Speed tierFastFast
VisionNoYes
Function callingYesYes
Extended thinkingNoNo
Prompt cachingNoNo
Batch APIYesNo
Release dateN/AOct 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 Mini Audio Preview inGpt 4o Mini Audio Preview outQwen3 VL 30B A3B Instruct inQwen3 VL 30B A3B Instruct out
Openai$0.150/M$0.600/M——

Frequently asked questions

Qwen3 VL 30B A3B Instruct 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