o4-mini vs Qwen3 32B
o4-mini leads the LLM Stats Score 27.7 to 18.6. Qwen3 32B is 12.8x cheaper per token.
OpenAI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
o4-mini leads the overall LLM Stats Score 27.7 to 18.6, ranking #133 overall.
In the 2 individual benchmarks reported for both models, o4-mini wins 2; this is a narrower head-to-head signal than the composite indexes.
On price, Qwen3 32B is roughly 12.8x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
o4-mini also accepts a larger context window (200,000 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose o4-mini
- overall performance matters — it scores 27.7 and ranks #133 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 2 of 2 exact shared results
- you process long inputs — it offers a 200,000 token context window
Choose Qwen3 32B
- cost matters — it's about 12.8x cheaper per token
- you want the most recent training data — it shipped Apr 2025
- you need open weights you can self-host or fine-tune
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
14 reported for o4-mini · 9 for Qwen3 32B
o4-mini outperforms in 2 benchmarks (AIME 2024, AIME 2025), while Qwen3 32B is better at 0 benchmarks.
o4-mini significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, o4-mini ($1.10/1M tokens) is 11.0x more expensive than Qwen3 32B ($0.10/1M tokens).
For output processing, o4-mini ($4.40/1M tokens) is 14.7x more expensive than Qwen3 32B ($0.30/1M tokens).
In conclusion, o4-mini is more expensive than Qwen3 32B.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
o4-mini accepts 200,000 input tokens compared to Qwen3 32B's 128,000 tokens. Qwen3 32B can generate longer responses up to 128,000 tokens, while o4-mini is limited to 100,000 tokens.
Input capabilities
Documented input modalities across available providers
o4-mini supports multimodal inputs, whereas Qwen3 32B does not.
o4-mini can handle both text and other forms of data like images, making it suitable for multimodal applications.
o4-mini
Qwen3 32B
License
Usage and distribution terms
o4-mini is licensed under a proprietary license, while Qwen3 32B uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
Proprietary
Closed source
Apache 2.0
Open weights
Release Timeline
When each model was launched
o4-mini was released on 2025-04-16, while Qwen3 32B was released on 2025-04-29.
Qwen3 32B is 0 month newer than o4-mini.
Apr 16, 2025
1.4 years ago
Apr 29, 2025
1.4 years ago
1w newerKnowledge Cutoff
When training data ends
o4-mini has a documented knowledge cutoff of 2024-05-31, while Qwen3 32B's cutoff date is not specified.
We can confirm o4-mini's training data extends to 2024-05-31, but cannot make a direct comparison without Qwen3 32B's cutoff date.
May 2024
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Provider Availability
o4-mini is available from OpenAI. Qwen3 32B is available from DeepInfra, Novita, Sambanova.
o4-mini
Qwen3 32B
Outputs Comparison
Judge for yourself.
Run your own prompts against o4-mini and Qwen3 32B side-by-side, then vote on the output you prefer.
FAQ
Common questions about o4-mini vs Qwen3 32B.