+
+
+
+
+
+
+
+
VS
VS
VS
VS
VS
VS
DeepSeek-V2.5 vs Llama 3.2 3B Instruct
Comprehensive side-by-side LLM comparison
DeepSeek-V2.5 leads with 20.4% higher average benchmark score. Llama 3.2 3B Instruct offers 239.6K more tokens in context window than DeepSeek-V2.5. Both models have similar pricing. DeepSeek-V2.5 is available on 3 providers. Overall, DeepSeek-V2.5 is the stronger choice for coding tasks.
+
+
+
+
DeepSeek
DeepSeek-V2.5 was developed as an enhanced iteration of the DeepSeek-V2 architecture, designed to incorporate improvements in model quality and efficiency. Built to advance the DeepSeek foundation model series, it provides refined capabilities for general-purpose language understanding and generation tasks.
+
+
+
+
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.
4 months newer
DeepSeek-V2.5
DeepSeek
2024-05-08
Llama 3.2 3B Instruct
Meta
2024-09-25
+
+
+
+
+
+
+
+
+
+
+
+
Pricing Comparison
Cost per million tokens (USD)
+
+
+
+
DeepSeek-V2.5
Input:$0.14
Output:$0.28
+
+
+
+
Llama 3.2 3B Instruct
Input:$0.01
Output:$0.02($0.39 cheaper)
Performance Metrics
Context window and performance specifications
Average performance across 3 common benchmarks
+
+
+
+
DeepSeek-V2.5
Average Score:83.4%(+20.4%)
+
+
+
+
Llama 3.2 3B Instruct
Average Score:63.0%
Provider Availability & Performance
Available providers and their performance metrics
+
+
+
+
DeepSeek-V2.5
3 providers
DeepInfra
Throughput: 63 tok/s
Latency: 0.5ms
DeepSeek
Throughput: 100 tok/s
Latency: 0.5ms
Hyperbolic
Throughput: 100 tok/s
Latency: 0.5ms
+
+
+
+
+
+
+
DeepSeek-V2.5
Avg Score:83.4%(+20.4%)
Providers:3
+
+
+
+
Llama 3.2 3B Instruct
Avg Score:63.0%
Providers:1