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DeepSeek-V2.5 vs Phi-3.5-mini-instruct
Comprehensive side-by-side LLM comparison
DeepSeek-V2.5 leads with 22.4% higher average benchmark score. Phi-3.5-mini-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.
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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.
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Microsoft
Phi-3.5 Mini was developed by Microsoft as a small language model designed to deliver impressive performance despite its compact size. Built with efficiency in mind, it demonstrates that capable language understanding and generation can be achieved with fewer parameters, making AI more accessible for edge and resource-constrained deployments.
3 months newer
DeepSeek-V2.5
DeepSeek
2024-05-08
Phi-3.5-mini-instruct
Microsoft
2024-08-23
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Pricing Comparison
Cost per million tokens (USD)
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DeepSeek-V2.5
Input:$0.14
Output:$0.28
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Phi-3.5-mini-instruct
Input:$0.10
Output:$0.10($0.22 cheaper)
Performance Metrics
Context window and performance specifications
Average performance across 5 common benchmarks
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DeepSeek-V2.5
Average Score:83.1%(+22.4%)
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Phi-3.5-mini-instruct
Average Score:60.7%
Provider Availability & Performance
Available providers and their performance metrics
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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
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DeepSeek-V2.5
Avg Score:83.1%(+22.4%)
Providers:3
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Phi-3.5-mini-instruct
Avg Score:60.7%
Providers:1