Model Comparison
DeepSeek R1 Distill Qwen 14B vs DeepSeek-V3.2 (Thinking)
DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek R1 Distill Qwen 14B outperforms in 0 benchmarks, while DeepSeek-V3.2 (Thinking) is better at 2 benchmarks (GPQA, LiveCodeBench).
DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Model Size
Parameter count comparison
DeepSeek-V3.2 (Thinking) has 670.2B more parameters than DeepSeek R1 Distill Qwen 14B, making it 4528.4% larger.
Context Window
Maximum input and output token capacity
Only DeepSeek-V3.2 (Thinking) specifies input context (131,072 tokens). Only DeepSeek-V3.2 (Thinking) specifies output context (65,536 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
DeepSeek R1 Distill Qwen 14B was released on 2025-01-20, while DeepSeek-V3.2 (Thinking) was released on 2025-12-01.
DeepSeek-V3.2 (Thinking) is 11 months newer than DeepSeek R1 Distill Qwen 14B.
Jan 20, 2025
1.3 years ago
Dec 1, 2025
5 months ago
10mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Outputs Comparison
Key Takeaways
No standout differentiators in the data we have for this pair.
Detailed Comparison
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FAQ
Common questions about DeepSeek R1 Distill Qwen 14B vs DeepSeek-V3.2 (Thinking).