This comparison focuses on applying leading large language models—GPT-5.2, Claude Opus 4.5, and Gemini 3—to technical, mathematical and scientific tasks and how the broader tooling ecosystem supports real-world use. By late 2025 these LLMs are the go-to proprietary choices for high‑capacity reasoning, code synthesis and domain-specific analysis, while an active open‑source stack (Code Llama, WizardLM/WizardCoder/WizardMath) and specialized platforms fill deployment, governance and self‑hosting needs. Practical adoption now depends less on raw model size and more on integration: AI code assistants (Tabnine, Replit, Cline) and developer platforms (GPTConsole, Replit Agents) provide IDE integration, multi‑step planning and execution, testing and lifecycle management. Enterprise deployments lean on private/self‑hosted solutions and data platforms (Tabnine, IBM watsonx Assistant) to meet governance, data residency and audit requirements. Code‑focused models (Code Llama, WizardCoder) and instruction‑tuned variants (WizardMath) remain important for reproducible code generation and numeric reasoning, while agent frameworks enable chaining LLM calls with tools, datasets and compute. The topic is timely because 2025 emphasizes production readiness: model selection now balances accuracy on math/science tasks, tool and API support, compute/cost tradeoffs, and compliance. Comparison pages should therefore evaluate model reasoning on technical problems, available integrations (IDE, API, agent frameworks), and operational concerns (privacy, versioning, testing). For teams working on scientific computing, data platforms and generative code, this ecosystem view—models plus marketplaces, code assistants, code generation tools, and data infrastructure—helps match capabilities to project constraints without relying on marketing claims.