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DeepSeek-V2.5 vs Llama 3.1 405B Instruct
Comprehensive side-by-side LLM comparison
Llama 3.1 405B Instruct leads with 1.9% higher average benchmark score. Llama 3.1 405B Instruct offers 239.6K more tokens in context window than DeepSeek-V2.5. DeepSeek-V2.5 is $1.36 cheaper per million tokens. Llama 3.1 405B Instruct is available on 8 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 405B was developed as Meta's largest open-source language model, designed to provide frontier-level capabilities with 405 billion parameters. Built to demonstrate that open-source models can match proprietary systems in capability, it enables researchers and developers to experiment with and deploy a powerful foundation model without licensing restrictions.
2 months newer
DeepSeek-V2.5
DeepSeek
2024-05-08
Llama 3.1 405B 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($1.36 cheaper)
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Llama 3.1 405B Instruct
Input:$0.89
Output:$0.89
Performance Metrics
Context window and performance specifications
Average performance across 4 common benchmarks
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DeepSeek-V2.5
Average Score:84.8%
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Llama 3.1 405B Instruct
Average Score:86.7%(+1.9%)
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:84.8%
Providers:3
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Llama 3.1 405B Instruct
Avg Score:86.7%(+1.9%)
Providers:8