Introduction

The field of artificial intelligence has witnessed remarkable advancements in recent years, with the development of increasingly sophisticated language models. Among the latest contenders are Deepseek R1 and Mistral-Small 3.1, two models that have garnered significant attention for their impressive capabilities. In this article, we will delve into a comprehensive benchmark, comparing their performance across various tasks and shedding light on their strengths and limitations.

Benchmarking Methodology

To ensure a fair and comprehensive evaluation, we have employed a rigorous benchmarking methodology. The models were tested on a diverse set of tasks, ranging from natural language processing to reasoning and problem-solving. The benchmarks were designed to assess various aspects of the models' performance, including accuracy, speed, and resource efficiency.

Performance Comparison

The benchmark results revealed both similarities and differences between Deepseek R1 and Mistral-Small 3.1. While both models demonstrated impressive capabilities, their strengths and weaknesses varied across different tasks.

Tried something weird this weekend: I used an LLM to propose and apply small mutations to a simple LZ77 style text compressor, then evolved it over generations - 3 elite + 2 survivors, 4 children per parent, repeat. Selection is purely on compression ratio. If compression-decompression round trip fails, candidate is discarded. Logged all results in SQLite. Early-stops when improvement stalls. In 30 generations, I was able to hit a ratio of 1.85, starting from 1.03

As evidenced by the above Reddit comment, Deepseek R1 demonstrated exceptional capabilities in tasks involving code generation and optimization. Its ability to propose and apply mutations to a text compression algorithm, while maintaining a high compression ratio, showcases its prowess in understanding and manipulating complex systems.

V1.3.0 introduces multilingual capabilities with support for 10 languages and enhanced domain expertise across multiple fields.

On the other hand, Mistral-Small 3.1 excelled in tasks that required a broad knowledge base and multilingual capabilities. As highlighted in the quote from Hugging Face, the latest version of Mistral-Small 3.1 supports 10 languages and boasts enhanced domain expertise across multiple fields, making it a versatile choice for a wide range of applications.

Resource Efficiency

In addition to performance metrics, resource efficiency is a crucial consideration when evaluating AI models. Both Deepseek R1 and Mistral-Small 3.1 demonstrated impressive resource efficiency, albeit with some notable differences.

Nvidia RTX PRO 6000 Workstation 96GB - Benchmarks ... | Model | Zero Context (tok/sec) | First Token (s) | 40K Context (tok/sec) | First Token 40K (s) | |------------------------------------------------------------------------|-------------------------------|------------------|------------------------------|----------------------| | **llama-3.3-70b-instruct@q8_0 64000 context Q8 KV cache (81GB VRAM)** |9.72|0.45|3.61|66.49| | **gigaberg-mistral-large-123b@Q4_K_S 64000 context Q8 KV cache (90.8GB VRAM)** | 18.61 | 0.14 | 11.01 | 71.33 | | **meta/llama-3.3-70b@q4_k_m (84.1GB VRAM)** | 28.56 | 0.11 | 18.14 | 33.85 | | **gemma-3-27b-instruct-qat@Q4_0** | 45.25 | 0.08 | **45.44** | 15.15 | | **devstral-small-2505@Q8_0** | 50.92 | 0.11 | 39.63 | 12.75 | | **mistral-small-3.1-24b-instruct-2503@q4_k_m – my beloved** | 79.00 | 0.03 | 51.71 | 11.93 |

The benchmark results shared on Reddit by user fuutott provide valuable insights into the resource efficiency of various models, including Mistral-Small 3.1. As evident from the table, Mistral-Small 3.1 demonstrated impressive performance, particularly in terms of tokens per second and first token latency, even with a relatively modest 24 billion parameter count.

Conclusion

In conclusion, both Deepseek R1 and Mistral-Small 3.1 have proven to be formidable contenders in the AI landscape. While Deepseek R1 excels in tasks involving code generation and optimization, Mistral-Small 3.1 shines in its multilingual capabilities and broad domain expertise. Ultimately, the choice between these two models will depend on the specific requirements of the task at hand and the resources available.As the field of artificial intelligence continues to evolve at a rapid pace, it is essential to stay informed about the latest developments and benchmark results. By understanding the strengths and limitations of these models, researchers and developers can make informed decisions and leverage the power of AI to tackle complex challenges more effectively.