DeepSeek-V3.1 vs QwQ-32B-Preview
DeepSeek-V3.1 leads the LLM Stats Score 22.1 to 9.0. QwQ-32B-Preview is 2.6x cheaper per token.
DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
DeepSeek-V3.1 leads the overall LLM Stats Score 22.1 to 9.0, ranking #184 overall.
In the 3 individual benchmarks reported for both models, DeepSeek-V3.1 wins 3; this is a narrower head-to-head signal than the composite indexes.
On price, QwQ-32B-Preview is roughly 2.6x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V3.1 also accepts a larger context window (163,840 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 DeepSeek-V3.1
- overall performance matters — it scores 22.1 and ranks #184 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 3 of 3 exact shared results
- you process long inputs — it offers a 163,840 token context window
- you want the most recent training data — it shipped Jan 2025
Choose QwQ-32B-Preview
- cost matters — it's about 2.6x cheaper per token
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
16 reported for DeepSeek-V3.1 · 4 for QwQ-32B-Preview
DeepSeek-V3.1 outperforms in 3 benchmarks (AIME 2024, GPQA, LiveCodeBench), while QwQ-32B-Preview is better at 0 benchmarks.
DeepSeek-V3.1 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, DeepSeek-V3.1 ($0.25/1M tokens) is 1.7x more expensive than QwQ-32B-Preview ($0.15/1M tokens).
For output processing, DeepSeek-V3.1 ($0.95/1M tokens) is 4.7x more expensive than QwQ-32B-Preview ($0.20/1M tokens).
In conclusion, DeepSeek-V3.1 is more expensive than QwQ-32B-Preview.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V3.1 has 638.5B more parameters than QwQ-32B-Preview, making it 1964.6% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V3.1 accepts 163,840 input tokens compared to QwQ-32B-Preview's 32,768 tokens. DeepSeek-V3.1 can generate longer responses up to 163,840 tokens, while QwQ-32B-Preview is limited to 32,768 tokens.
License
Usage and distribution terms
DeepSeek-V3.1 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-V3.1 was released on 2025-01-10, while QwQ-32B-Preview was released on 2024-11-28.
DeepSeek-V3.1 is 1 month newer than QwQ-32B-Preview.
Jan 10, 2025
1.7 years ago
1mo newerNov 28, 2024
1.8 years ago
Knowledge Cutoff
When training data ends
QwQ-32B-Preview has a documented knowledge cutoff of 2024-11-28, while DeepSeek-V3.1'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-V3.1's cutoff date.
—
Nov 2024
Provider Availability
DeepSeek-V3.1 is available from DeepInfra, Novita. QwQ-32B-Preview is available from DeepInfra, Hyperbolic, Fireworks, Together.
DeepSeek-V3.1
QwQ-32B-Preview
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V3.1 and QwQ-32B-Preview side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V3.1 vs QwQ-32B-Preview.