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Openai Gpt 4o Mini vs Qwen3 Coder 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

Openai Gpt 4o Mini

Context window

128K

128,000 tokens · ~96K words

Model page
Alibaba

Model

Qwen3 Coder Plus

Tool calling

Context window

998K

997,952 tokens · ~748K 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.

Openai Gpt 4o Mini128K
Qwen3 Coder Plus998K

Qwen3 Coder Plus has about 7.8× the context window of the other in this pair.

Qwen3 Coder Plus has 679% more context capacity (997K vs 128K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Qwen3 Coder Plus. Its 997K 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.

SpecOpenai Gpt 4o MiniQwen3 Coder Plus
Context window128,000 tokens (128K)997,952 tokens (997K)
Max output tokensN/A65,536 tokens (65K)
Speed tierFastBalanced
VisionNoNo
Function callingNoYes
Extended thinkingNoYes
Prompt cachingNoYes
Batch APIYesNo
Release dateN/ASep 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.

ProviderOpenai Gpt 4o Mini inOpenai Gpt 4o Mini outQwen3 Coder Plus inQwen3 Coder Plus out
Alibaba Cloud
Gradient
Openrouter$1.00/M$5.00/M

Frequently asked questions

Qwen3 Coder Plus has a larger context window: 997K 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.

Powered by Mem0

Use a smaller model.
Get better results.

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.

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