This topic examines which large language models (LLMs) are best suited for scientific reasoning and theorem proving, and how developer and enterprise tooling shapes their practical use. Scientific reasoning and formal proof work require models that combine robust chain‑of‑thought, symbolic manipulation, and reliable tool use; comparisons center on proprietary families (e.g., OpenAI’s GPT‑5.2, Google Gemini) versus fine‑tuned or open models hosted via platforms like Together AI. Relevance and timeliness: by early 2026, demand has grown for LLMs that can produce reproducible, verifiable arguments for research, IP analysis, and market/competitive intelligence. Organizations need pipelines for benchmarking, fine‑tuning, and safe deployment to move experimental strengths into production workflows for AI research tools, competitive intelligence, and market intelligence applications. Key tools and roles: LangChain provides developer APIs and agent patterns to orchestrate model calls, chain reasoning steps, and connect to external solvers; Together AI supplies end‑to‑end training, fine‑tuning, and serverless inference for specialized proof models; Google Gemini offers a multimodal, API‑accessible model family used in enterprise prototyping; IBM watsonx Assistant targets enterprise orchestration and governed assistants for regulated workflows. No‑code/low‑code platforms (StackAI, MindStudio) accelerate building and governing agents, Notion centralizes knowledge and provenance for research workflows, and automation platforms (n8n) link model outputs to databases, proof assistants, and alerting systems. Practical comparisons should evaluate reasoning accuracy, verifiability, latency/cost tradeoffs, and integration with symbolic tools and proof assistants. The landscape favors not just raw model capability but the surrounding tooling for fine‑tuning, benchmarking, deployment, and governance.