Context Window

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GPT-4o-mini Search Preview vs Qwen3 30B A3B

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 Search Preview

Image inputTool calling

Context window

128K

128,000 tokens · ~96K words

Model page
Alibaba

Model

Qwen3 30B A3B

Tool calling

Context window

129K

129,024 tokens · ~97K 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 Search Preview128K
Qwen3 30B A3B129K

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

Token calculator →Memory savings →

Qwen3 30B A3B has 0% more context capacity (129K vs 128K tokens). Qwen3 30B A3B is 46% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Qwen3 30B A3B. Its 129K context fits entire documents without chunking (vs 128K).

  • RAG / high-volume retrieval

    Use Qwen3 30B A3B. Input tokens are 46% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecGPT-4o-mini Search PreviewQwen3 30B A3B
Context window128,000 tokens (128K)129,024 tokens (129K)
Max output tokens16,384 tokens (16K)16,384 tokens (16K)
Speed tierFastFast
VisionYesNo
Function callingYesYes
Extended thinkingNoYes
Prompt cachingYesNo
Batch APIYesNo
Release dateMar 2025Apr 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 Search Preview inGPT-4o-mini Search Preview outQwen3 30B A3B inQwen3 30B A3B out
Alibaba Cloud————
Deepinfra——$0.080/M$0.290/M
Fireworks——$0.150/M$0.600/M
Nebius——$0.100/M$0.300/M
Openai$0.150/M$0.600/M——

Frequently asked questions

Qwen3 30B A3B has a larger context window: 129K 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