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MULTI-DIMENSIONAL TASK-ALIGNMENT FRAMEWORK FOR LARGE LANGUAGE MODELS: COMPARATIVE ANALYSIS OF ChatGPT, GEMINI, GROK AND CLAUDE

Iryna Bobreshova Olena Lebedieva
Jul 2026 · European Open Science Space · pp. 95-99 · 0 citations

TL;DR

The Multi- Dimensional Task-Alignment Framework (MTAF) is introduced, a novel seven-criterion evaluation instrument designed to characterize the functional specialization of competing LLMs and translate benchmark performance into domain-specific selection guidance.

Abstract

The proliferation of commercially available large language models (LLMs) has produced a competitive ecosystem in which model selection for specific professional tasks remains insufficiently theorized. These theses introduce the Multi- Dimensional Task-Alignment Framework (MTAF), a novel seven-criterion evaluation instrument designed to characterize the functional specialization of competing LLMs and translate benchmark performance into domain-specific selection guidance. Applying MTAF to four dominant systems – ChatGPT (OpenAI/GPT-4o), Gemini (Google DeepMind), Grok (xAI), and Claude (Anthropic) – we identify distinct competitive profiles: ChatGPT demonstrates leading performance in code generation, Gemini excels in multimodal and real-time grounded tasks, Grok provides unique access to temporally current social-media-derived data and Claude exhibits the highest reliability in long-document processing and complex instruction following. A derived task-model alignment matrix operationalizes these findings for practical decisionmaking across scientific research, software engineering, academic writing, and organizational management contexts.

DOI Semantic Scholar
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