DeepSeek-V2.5: A Comprehensive Overview
DeepSeek-V2.5, an upgraded version of DeepSeek, combines the general and coding abilities of DeepSeek-V2-Chat and DeepSeek-Coder-V2-Instruct. This article explores the key features and benchmarks of DeepSeek-V2.5, comparing it to its predecessors and competitors.
Key Features
DeepSeek-V2.5 integrates the capabilities of DeepSeek-V2-Chat and DeepSeek-Coder-V2-Instruct, offering enhanced performance in both general and coding tasks. The model supports 338 programming languages and extends the context length to 128K, making it highly versatile and capable of handling a variety of coding challenges.
Benchmark Performance
DeepSeek-V2.5 demonstrates significant improvements in various benchmarks. It achieves a 50.5% score on AlpacaEval 2.0, a 76.2% score on ArenaHard, and an 8.04% score on AlignBench. Additionally, it attains a 9.02% score on MT-Bench, an 89% score on HumanEval Python, and a 41.8% score on LiveCodeBench (January-September).
API Performance
DeepSeek-V2.5 offers competitive pricing with an input token price of $0.14 per 1M tokens and an output token price of $0.28 per 1M tokens. The model has a median output speed and a latency that makes it suitable for various applications.
Comparison to Competitors
DeepSeek-V2.5 outperforms several closed-source models, including GPT4-Turbo, Claude 3 Opus, and Gemini 1.5 Pro, particularly in coding and math benchmarks. The model is available with 236 billion parameters, based on the DeepSeek MoE framework.
Conclusion
DeepSeek-V2.5 offers extensive support for programming languages and an extended context length, making it a valuable tool for developers and AI enthusiasts. Its performance in coding and mathematical reasoning tasks, combined with competitive pricing and robust API performance, make it a notable option in the field of code intelligence.
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