DeepSeek-Coder-V2 vs. DeepSeekMath vs. Java vs. Qwen2.5-Coder Comparison Chart

DeepSeekMath is a specialized 7B parameter language model developed by DeepSeek-AI, designed to push the boundaries of mathematical reasoning in open-source language models. It starts from the DeepSeek-Coder-v1.5 7B model and undergoes further pre-training with 120B math-related tokens sourced from Common Crawl, alongside natural language and code data. DeepSeekMath has demonstrated remarkable performance, achieving a 51.7% score on the competition-level MATH benchmark without external tools or voting techniques, closely competing with the likes of Gemini-Ultra and GPT-4. The model's capabilities are enhanced by a meticulous data selection pipeline and the introduction of Group Relative Policy Optimization (GRPO), which optimizes both mathematical reasoning and memory usage. DeepSeekMath is available in base, instruct, and RL versions, supporting both research and commercial use, and is aimed at those looking to explore or apply advanced mathematical problem-solving in AI contexts.

Qwen2.5-Coder-32B-Instruct has become the current SOTA open source code model, matching the coding capabilities of GPT-4o. While demonstrating strong and comprehensive coding abilities, it also possesses good general and mathematical skills. As of now, Qwen2.5-Coder has covered six mainstream model sizes to meet the needs of different developers. We explore the practicality of Qwen2.5-Coder in two scenarios, including code assistants and artifacts, with some examples showcasing the potential applications of Qwen2.5-Coder in real-world scenarios. Qwen2.5-Coder-32B-Instruct, as the flagship model of this open source release, has achieved the best performance among open source models on multiple popular code generation benchmarks and has competitive performance with GPT-4o. Code repair is an important programming skill. Qwen2.5-Coder-32B-Instruct can help users fix errors in their code, making programming more efficient.