Claude Code Boosts AI Benchmark Research with New Amplifying Techniques

Claude Code’s latest benchmark underscores a shift from third‑party dependency to in‑house engineering. Amplifying’s systematic survey of 2,430 tool selections—drawn from three Claude Code models (Sonnet 4.5, Opus 4.5, Opus 4.6) across four greenfield projects (Next.js SaaS, Python API, React SPA, Node CLI)—found that agents “build, not buy” in 12 of the 20 categories surveyed. Custom, DIY implementations accounted for 12 % of all primary picks (252 of 2,073 selections), making them the single most common recommendation, according to the report. When Claude Code does defer to external packages, the choices converge on a narrow “default stack”: Vercel for hosting, PostgreSQL for databases, Stripe for payments, Tailwind CSS and shadcn/ui for UI, pnpm for package management, GitHub Actions for CI/CD, Sentry for observability, Resend for email, and Zustand for state, plus ecosystem‑specific tools such as Drizzle (JS) or SQLModel (Python) for ORMs, NextAuth.js for auth, and Vitest or pytest for testing.

The data also reveal a de‑facto monopoly in several sub‑domains. GitHub Actions captured 94 % of CI/CD selections, shadcn/ui owned 90 % of UI component choices, and Stripe commanded 91 % of payment‑tool picks, Amplifying notes. Across the three model variants, intra‑ecosystem consensus was striking: 90 % of the time the models agreed on the top tool within a given stack, and in 18 of the 20 categories all three models selected the same leading solution. Only the Caching and Real‑time categories displayed genuine cross‑ecosystem disagreement; the remaining three “disagreements” were artifacts of mixing JavaScript and Python results.

Contextual factors proved more decisive than prompt phrasing. When the same category was queried across different repositories, Claude Code switched tools—opting for Vercel in a Next.js project but Railway in a Python API—yet it remained stable across five distinct phrasings of the open‑ended prompt, with an average phrasing stability of 76 %, Amplifying reports. The methodology involved resetting the repository state (git clean ‑fd) between each of the 100 prompts per project, ensuring that each tool selection reflected a fresh decision rather than residual artifacts.

These findings arrive amid an intensifying “coding wars” narrative. VentureBeat reported that OpenAI’s rollout of GPT‑5.3‑Codex—a “most capable coding agent to date”—coincides with Anthropic’s upgrade of Claude, positioning the two firms as primary rivals for enterprise AI‑assisted development. The Verge, meanwhile, highlighted Claude’s recent momentum and questioned whether the model can sustain its advantage as competitors accelerate. Amplifying’s benchmark suggests that Claude Code’s influence extends beyond performance metrics; by dictating which tools become default, the model can shape market share in a way that rivals traditional marketing spend or conference exposure.

For tool vendors, the implications are stark: failure to appear in Claude Code’s recommendation set effectively renders a product invisible to a growing segment of greenfield projects. Developers, in turn, may find their technology stacks increasingly defined by the model’s training data rather than independent research. As Amplifying concludes, understanding the distribution channel created by AI agents is no longer optional—it is essential competitive intelligence for both providers and adopters in a landscape where the gatekeeper’s choices can dictate the next generation of software infrastructure.