+
+
+
+
+
+
+
+
VS
VS
VS
VS
VS
VS
DeepSeek R1 Distill Qwen 1.5B vs Llama 3.2 3B Instruct
Comprehensive side-by-side LLM comparison
Both models show comparable benchmark performance. Both models have their strengths depending on your specific coding needs.
+
+
+
+
DeepSeek
DeepSeek-R1-Distill-Qwen-1.5B was created through distillation into an ultra-compact Qwen architecture, designed to enable reasoning capabilities on resource-constrained devices. Built with just 1.5 billion parameters, it brings advanced analytical techniques to edge computing and mobile scenarios.
+
+
+
+
Meta
Llama 3.2 3B was created as an ultra-compact open-source model, designed to enable on-device and edge deployment scenarios. Built with just 3 billion parameters while retaining instruction-following abilities, it brings Meta's language technology to mobile devices, IoT applications, and resource-constrained environments.
3 months newer
Llama 3.2 3B Instruct
Meta
2024-09-25
DeepSeek R1 Distill Qwen 1.5B
DeepSeek
2025-01-20
+
+
+
+
+
+
+
+
+
+
+
+
Performance Metrics
Context window and performance specifications
Average performance across 1 common benchmarks
+
+
+
+
DeepSeek R1 Distill Qwen 1.5B
Average Score:33.8%(+1.0%)
+
+
+
+
Llama 3.2 3B Instruct
Average Score:32.8%
Provider Availability & Performance
Available providers and their performance metrics
+
+
+
+
DeepSeek R1 Distill Qwen 1.5B
0 providers
+
+
+
+
Llama 3.2 3B Instruct
1 providers
DeepInfra
+
+
+
+
DeepSeek R1 Distill Qwen 1.5B
Avg Score:33.8%(+1.0%)
Providers:0
+
+
+
+
Llama 3.2 3B Instruct
Avg Score:32.8%
Providers:1