Model Comparison
DeepSeek-R1 vs QwQ-32B-PreviewWhich is better in 2026?
Comparing DeepSeek-R1 and QwQ-32B-Preview across benchmarks, pricing, and capabilities.
Verdict: DeepSeek-R1 vs QwQ-32B-Preview — which is better?
DeepSeek-R1 (by DeepSeek) and QwQ-32B-Preview (by Alibaba Cloud / Qwen Team) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.
On price, QwQ-32B-Preview is roughly 5.9x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-R1 also accepts a larger context window (131,072 input tokens), making it the stronger choice for long documents and large codebases.
Choose DeepSeek-R1 if…
- you process long inputs — it offers a 131,072 token context window
- you want the most recent training data — it shipped Jan 2025
Choose QwQ-32B-Preview if…
- cost matters — it's about 5.9x cheaper per token
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-R1 and QwQ-32B-Previewdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-R1 ($0.55/1M tokens) is 3.7x more expensive than QwQ-32B-Preview ($0.15/1M tokens).
For output processing, DeepSeek-R1 ($2.19/1M tokens) is 10.9x more expensive than QwQ-32B-Preview ($0.20/1M tokens).
In conclusion, DeepSeek-R1 is more expensive than QwQ-32B-Preview.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-R1 has 638.5B more parameters than QwQ-32B-Preview, making it 1964.6% larger.
Context Window
Maximum input and output token capacity
DeepSeek-R1 accepts 131,072 input tokens compared to QwQ-32B-Preview's 32,768 tokens. DeepSeek-R1 can generate longer responses up to 131,072 tokens, while QwQ-32B-Preview is limited to 32,768 tokens.
License
Usage and distribution terms
DeepSeek-R1 is licensed under MIT, while QwQ-32B-Preview uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
DeepSeek-R1 was released on 2025-01-20, while QwQ-32B-Preview was released on 2024-11-28.
DeepSeek-R1 is 2 months newer than QwQ-32B-Preview.
Jan 20, 2025
1.4 years ago
1mo newerNov 28, 2024
1.6 years ago
Knowledge Cutoff
When training data ends
QwQ-32B-Preview has a documented knowledge cutoff of 2024-11-28, while DeepSeek-R1'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 DeepSeek-R1's cutoff date.
—
Nov 2024
Provider Availability
DeepSeek-R1 is available from DeepSeek, DeepInfra, Together, Fireworks. QwQ-32B-Preview is available from DeepInfra, Hyperbolic, Fireworks, Together.
DeepSeek-R1
QwQ-32B-Preview
Outputs Comparison
Key Takeaways
DeepSeek-R1
View detailsDeepSeek
QwQ-32B-Preview
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against DeepSeek-R1 and QwQ-32B-Preview side-by-side, then vote on the output you prefer.
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FAQ
Common questions about DeepSeek-R1 vs QwQ-32B-Preview.