Cross-Task Benchmarking and Evaluation of General-Purpose and Code-Specific Large Language Models

• URL: http://arxiv.org/abs/2512.04673v1
• Date: Thu, 04 Dec 2025 11:06:33 GMT
• Title: Cross-Task Benchmarking and Evaluation of General-Purpose and Code-Specific Large Language Models
• Authors: Gunjan Das, Paheli Bhattacharya, Rishabh Gupta,
• Abstract summary: Large Language Models (LLMs) have revolutionized both general natural language processing and domain-specific applications such as code synthesis, legal reasoning, and finance.<n>We present a comprehensive evaluation of five general-purpose and three code-specific state-of-the-art LLMs across six diverse benchmarks.<n>Our findings reveal that models optimized for code (e.g., CodeLLaMA variants) exhibit strong reasoning and syntactic precision, that even for non-coding tasks can show measurable performance gains.
• Score: 3.603673783661375
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have revolutionized both general natural language processing and domain-specific applications such as code synthesis, legal reasoning, and finance. However, while prior studies have explored individual model capabilities, a systematic cross-domain comparison that unifies linguistic, reasoning, and code understanding abilities remains underexplored. In this work, we present a comprehensive evaluation of five general-purpose and three code-specific state-of-the-art LLMs across six diverse benchmarks encompassing linguistic competence, mathematical reasoning, and trustworthiness. Additionally, we analyze model behavior on the CoNaLa dataset for code explanation, comparing natural language and code-specialized LLMs. Our findings reveal that models optimized for code (e.g., CodeLLaMA variants) exhibit strong reasoning and syntactic precision, that even for non-coding tasks can show measurable performance gains, in contrast to general-purpose models like Mistral-7B and Llama-3-8B.

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