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DeepSeek-V2.5 vs Llama 3.1 70B Instruct
Comprehensive side-by-side LLM comparison
DeepSeek-V2.5 leads with 2.7% higher average benchmark score. Llama 3.1 70B Instruct offers 239.6K more tokens in context window than DeepSeek-V2.5. Both models have similar pricing. Llama 3.1 70B Instruct is available on 9 providers. Both models have their strengths depending on your specific coding needs.
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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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Meta
Llama 3.1 70B was created as a balanced open-source model, designed to provide strong performance with 70 billion parameters while remaining practical for many deployment scenarios. Built to serve as a versatile foundation for fine-tuning and application development, it combines capability with accessibility in the open-source ecosystem.
2 months newer
DeepSeek-V2.5
DeepSeek
2024-05-08
Llama 3.1 70B Instruct
Meta
2024-07-23
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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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Llama 3.1 70B Instruct
Input:$0.20
Output:$0.20($0.02 cheaper)
Performance Metrics
Context window and performance specifications
Average performance across 2 common benchmarks
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DeepSeek-V2.5
Average Score:84.7%(+2.7%)
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Llama 3.1 70B Instruct
Average Score:82.0%
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:84.7%(+2.7%)
Providers:3
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Llama 3.1 70B Instruct
Avg Score:82.0%
Providers:9