DeepSeek-R1 and Cohere's Command R (Aug 2024) are both advanced language models, but they exhibit distinct differences in performance, architecture, and pricing.
Architecture and Training:
- DeepSeek-R1 is a 671 billion parameter Mixture-of-Experts (MoE) model, with 37 billion activated parameters per token. It was trained using large-scale reinforcement learning, focusing on reasoning capabilities. This model incorporates two stages of reinforcement learning and two stages of supervised fine-tuning to enhance reasoning and non-reasoning abilities[1][3].
- Command R (Aug 2024), developed by Cohere, features enhanced multilingual retrieval-augmented generation (RAG) and tool use capabilities. It excels in math, code, and reasoning tasks, providing results comparable to its predecessor, Command R+[1][3].
Performance Benchmarks:
- DeepSeek-R1 performs comparably to OpenAI's o1 model across many reasoning benchmarks, including math and code tasks. It excels in creative and long-context tasks like AlpacaEval 2.0 and ArenaHard, outperforming other models in these areas[2]. DeepSeek-R1 scored 90.8% on the MMLU benchmark and 84% on MMLU-Pro, demonstrating strong reasoning capabilities[1].
- Command R (Aug 2024) achieves a score of 67% on the MMLU benchmark and 70% on HumanEval, indicating solid performance in code generation and problem-solving[1]. However, its performance on MMLU-Pro and other specific reasoning benchmarks is not reported.
Pricing and Cost:
- DeepSeek-R1 is significantly more expensive than Command R (Aug 2024), with input costs at $0.55 per million tokens and output costs at $2.19 per million tokens. This makes it roughly 3.7 times more expensive for both input and output tokens[1][3].
- Command R (Aug 2024) offers lower costs, with input tokens priced at $0.15 per million and output tokens at $0.60 per million[1][4].
Open-Source and Community:
- DeepSeek-R1 is open-source, allowing for community-driven improvements and modifications. This openness can lead to more transparent development and customization possibilities[1][5].
- Command R (Aug 2024) is not open-source, limiting community involvement in its development and customization[1].
Prompt Sensitivity:
- DeepSeek-R1 is sensitive to prompts, with few-shot prompting often degrading its performance. It is recommended to use zero-shot prompting with clear instructions for optimal results[2].
- There is no specific mention of prompt sensitivity for Command R (Aug 2024) in the available information.
In summary, while both models have strong reasoning capabilities, DeepSeek-R1 excels in certain benchmarks and offers open-source flexibility, but at a higher cost. Command R (Aug 2024) is more cost-effective and provides competitive performance in specific areas like code generation.
Citations:
[1] https://docsbot.ai/models/compare/command-r-08-2024/deepseek-r1
[2] https://www.prompthub.us/blog/deepseek-r-1-model-overview-and-how-it-ranks-against-openais-o1
[3] https://docsbot.ai/models/compare/deepseek-r1/command-r-08-2024
[4] https://artificialanalysis.ai/models/command-r
[5] https://blog.promptlayer.com/deepseek-r1-vs-o1/
[6] https://artificialanalysis.ai/models
[7] https://artificialanalysis.ai/models/command-r-03-2024
[8] https://explodingtopics.com/blog/list-of-llms