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DeepSeek-V2.5 vs Phi-3.5-mini-instruct
Comprehensive side-by-side LLM comparison
Phi-3.5-mini-instruct leads with 18.4% 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, Phi-3.5-mini-instruct is the stronger choice for coding tasks.
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DeepSeek
DeepSeek-V2.5 is a language model developed by DeepSeek. It achieves strong performance with an average score of 71.1% across 15 benchmarks. It excels particularly in GSM8k (95.1%), MT-Bench (90.2%), HumanEval (89.0%). The model is available through 3 API providers. Released in 2024, it represents DeepSeek's latest advancement in AI technology.
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Microsoft
Phi-3.5-mini-instruct is a language model developed by Microsoft. The model shows competitive results across 31 benchmarks. It excels particularly in GSM8k (86.2%), ARC-C (84.6%), RULER (84.1%). It supports a 256K token context window for handling large documents. The model is available through 1 API provider. It's licensed for commercial use, making it suitable for enterprise applications. Released in 2024, it represents Microsoft's latest advancement in AI technology.
3 months newer
DeepSeek-V2.5
DeepSeek
2024-05-08
Phi-3.5-mini-instruct
Microsoft
2024-08-23
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 41 common benchmarks
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DeepSeek-V2.5
Average Score:26.0%
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Phi-3.5-mini-instruct
Average Score:44.4%(+18.4%)
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:26.0%
Providers:3
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Phi-3.5-mini-instruct
Avg Score:44.4%(+18.4%)
Providers:1