+
+
+
+
+
+
+
+
VS
VS
VS
VS
VS
VS
DeepSeek-V2.5 vs Phi 4
Comprehensive side-by-side LLM comparison
Both models show comparable benchmark performance. Phi 4 offers 15.6K more tokens in context window than DeepSeek-V2.5. Both models have similar pricing. DeepSeek-V2.5 is available on 3 providers. Both models have their strengths depending on your specific coding needs.
+
+
+
+
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.
+
+
+
+
Microsoft
Phi-4 was introduced as the fourth generation of Microsoft's small language model series, designed to push the boundaries of what compact models can achieve. Built with advanced training techniques and architectural improvements, it demonstrates continued progress in efficient, high-quality language models.
7 months newer
DeepSeek-V2.5
DeepSeek
2024-05-08
Phi 4
Microsoft
2024-12-12
+
+
+
+
+
+
+
+
+
+
+
+
Pricing Comparison
Cost per million tokens (USD)
+
+
+
+
DeepSeek-V2.5
Input:$0.14
Output:$0.28
+
+
+
+
Phi 4
Input:$0.07
Output:$0.14($0.21 cheaper)
Performance Metrics
Context window and performance specifications
Average performance across 4 common benchmarks
+
+
+
+
DeepSeek-V2.5
Average Score:80.1%
+
+
+
+
Phi 4
Average Score:80.8%(+0.7%)
+
+
+
+
Knowledge Cutoff
Training data recency comparison
Phi 4
2024-06-01
More recent knowledge cutoff means awareness of newer technologies and frameworks
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:80.1%
Providers:3
+
+
+
+
Phi 4
Avg Score:80.8%(+0.7%)
Providers:1