DeepSeek R1 Distill Qwen 1.5B vs DeepSeek R1 Distill Qwen 32B
DeepSeek R1 Distill Qwen 32B leads the LLM Stats Score 13.1 to -3.0.
DeepSeek · DeepSeek · Updated for 2026
Which is better?
DeepSeek R1 Distill Qwen 32B leads the overall LLM Stats Score 13.1 to -3.0, ranking #248 overall.
In the 4 individual benchmarks reported for both models, DeepSeek R1 Distill Qwen 32B wins 4; this is a narrower head-to-head signal than the composite indexes.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek R1 Distill Qwen 1.5B
- you are already invested in the DeepSeek ecosystem
Choose DeepSeek R1 Distill Qwen 32B
- overall performance matters — it scores 13.1 and ranks #248 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 4 of 4 exact shared results
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
4 reported for DeepSeek R1 Distill Qwen 1.5B · 4 for DeepSeek R1 Distill Qwen 32B
DeepSeek R1 Distill Qwen 1.5B outperforms in 0 benchmarks, while DeepSeek R1 Distill Qwen 32B is better at 4 benchmarks (AIME 2024, GPQA, LiveCodeBench, MATH-500).
DeepSeek R1 Distill Qwen 32B significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Model Size
Parameter count comparison
DeepSeek R1 Distill Qwen 32B has 31.0B more parameters than DeepSeek R1 Distill Qwen 1.5B, making it 1742.7% larger.
Context Window
Maximum input and output token capacity
Only DeepSeek R1 Distill Qwen 32B specifies input context (128,000 tokens). Only DeepSeek R1 Distill Qwen 32B specifies output context (128,000 tokens).
License
Usage and distribution terms
Both models are licensed under MIT.
Both models share the same licensing terms, providing consistent usage rights.
MIT
Open weights
MIT
Open weights
Release Timeline
When each model was launched
Both models were released on 2025-01-20.
They likely represent similar generations of model development.
Jan 20, 2025
1.7 years ago
Jan 20, 2025
1.7 years ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek R1 Distill Qwen 1.5B and DeepSeek R1 Distill Qwen 32B side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek R1 Distill Qwen 1.5B vs DeepSeek R1 Distill Qwen 32B.