DeepSeek AI has emerged as a significant player in the open-source AI model landscape, offering a range of models designed for diverse applications, from complex mathematical problem-solving to code generation. This article provides an in-depth look at the various DeepSeek models available, their strengths, and how they compare to other leading models in the industry. All DeepSeek models are available through OpenRouter, which offers a unified API for accessing different AI models.
DeepSeek AI distinguishes itself by creating open-source models that rival the performance of closed-source alternatives like OpenAI's models. Their commitment to open reasoning tokens and freely available technical reports fosters collaboration and innovation within the AI community.
The DeepSeek R1 model is a significant achievement, boasting performance on par with OpenAI's o1 model. Key features include:
This model is a game-changer for developers seeking powerful, open-source AI solutions.
DeepSeek's R1 Distill models leverage knowledge distillation techniques to create smaller, more efficient models based on the DeepSeek R1 architecture. These models are fine-tuned using outputs from DeepSeek R1, resulting in competitive performance across various benchmarks.
This model is distilled from Llama-3.1-8B-Instruct and fine-tuned using DeepSeek R1's outputs, for competitive performance on common Large Language Model benchmarks. Key Highlights:
This is a highly efficient model distilled from Qwen 2.5 Math 1.5B, exceeding the GPT-4o 0513 model on Math Benchmarks. Key Highlights:
Based on Qwen 2.5 32B, this model outperforms OpenAI's o1-mini across various benchmarks, setting new state-of-the-art results for dense models. Key Highlights:
This model, built on Qwen 2.5 14B, also surpasses OpenAI's o1-mini in benchmark testing to achieve state-of-the-art results. Key Highlights:
This model distilled from Llama-3.3-70B-Instruct achieves top level benchmark scores, with competitive performance. Key Highlights:
DeepSeek-V3 is the latest model from the DeepSeek team. This model is pre-trained on nearly 15 trillion tokens, outperforming open-source models and rivaling leading closed-source alternatives. Check out the launch announcement for more information.
DeepSeek-Coder-V2 is an open-source Mixture-of-Experts (MoE) code language model pre-trained with an additional 6 trillion tokens. The model was was trained from scratch on 2T tokens, with a composition of 87% code and 13% natural language in both English and Chinese.
DeepSeek-V2.5 combines the strengths of DeepSeek-V2-Chat and DeepSeek-Coder-V2-Instruct, offering a unified model with enhanced general and coding capabilities.
OpenRouter simplifies the integration process for DeepSeek models, providing a unified API. This allows developers to easily switch between different models and optimize their applications for performance and cost.
DeepSeek AI's innovative approach to open-source AI model development is democratizing access to powerful AI tools. By offering models like DeepSeek R1 and its Distill variants, as well as specialized models like DeepSeek-Coder-V2, DeepSeek is empowering developers and researchers to build cutting-edge AI applications.
