o1-mini vs QwQ-32B-Preview
o1-mini and QwQ-32B-Preview are closely matched at 10.1 and 9.1 on the LLM Stats Score. QwQ-32B-Preview is 32.3x cheaper per token.
OpenAI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
o1-mini and QwQ-32B-Preview are closely matched on the overall LLM Stats Score at 10.1 and 9.1.
In the 2 individual benchmarks reported for both models, QwQ-32B-Preview wins 2; this is a narrower head-to-head signal than the composite indexes.
On price, QwQ-32B-Preview is roughly 32.3x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
o1-mini also accepts a larger context window (128,000 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 o1-mini
- you process long inputs — it offers a 128,000 token context window
Choose QwQ-32B-Preview
- you value its reported benchmark strengths — it wins 2 of 2 exact shared results
- cost matters — it's about 32.3x cheaper per token
- you want the most recent training data — it shipped Nov 2024
- 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
6 reported for o1-mini · 4 for QwQ-32B-Preview
o1-mini outperforms in 0 benchmarks, while QwQ-32B-Preview is better at 2 benchmarks (GPQA, MATH-500).
QwQ-32B-Preview 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, o1-mini ($3.00/1M tokens) is 20.0x more expensive than QwQ-32B-Preview ($0.15/1M tokens).
For output processing, o1-mini ($12.00/1M tokens) is 60.0x more expensive than QwQ-32B-Preview ($0.20/1M tokens).
In conclusion, o1-mini is more expensive than QwQ-32B-Preview.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
o1-mini accepts 128,000 input tokens compared to QwQ-32B-Preview's 32,768 tokens. o1-mini can generate longer responses up to 65,536 tokens, while QwQ-32B-Preview is limited to 32,768 tokens.
License
Usage and distribution terms
o1-mini is licensed under a proprietary license, while QwQ-32B-Preview 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
o1-mini was released on 2024-09-12, while QwQ-32B-Preview was released on 2024-11-28.
QwQ-32B-Preview is 3 months newer than o1-mini.
Sep 12, 2024
2.0 years ago
Nov 28, 2024
1.8 years ago
2mo newerKnowledge Cutoff
When training data ends
QwQ-32B-Preview has a documented knowledge cutoff of 2024-11-28, while o1-mini's cutoff date is not specified.
We can confirm QwQ-32B-Preview's training data extends to 2024-11-28, but cannot make a direct comparison without o1-mini's cutoff date.
—
Nov 2024
Provider Availability
o1-mini is available from OpenAI, Azure. QwQ-32B-Preview is available from DeepInfra, Hyperbolic, Fireworks, Together.
o1-mini
QwQ-32B-Preview
Outputs Comparison
Judge for yourself.
Run your own prompts against o1-mini and QwQ-32B-Preview side-by-side, then vote on the output you prefer.
FAQ
Common questions about o1-mini vs QwQ-32B-Preview.