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AI Pricing and Access

AI pricing and access covers the cost structures, subscription models, and institutional access considerations that researchers face when adopting AI tools.

Context & Background

AI tools use various pricing models:

  • Free tiers: Limited access to basic models (ChatGPT free, Claude free)
  • Pro subscriptions: $20-30/month for enhanced access (ChatGPT Plus, Claude Pro)
  • API pricing: Pay-per-token for programmatic access (varies by model and provider)
  • Institutional licenses: University-negotiated access (increasingly common)
  • Usage-based: Tools like Claude Code with metered billing based on actual usage

Access tier as a research-capability constraint

Ortoleva and Sandomirskiy (Markus Academy 166-3) argue the tier you can afford now materially affects what research you can do, and give a recommendation at each price point as of late July 2026: Pro (~$200/mo) — GPT-5.6 Pro in the browser plus Sol Ultra in ChatGPT Work; $20/mo — OpenAI Plus, narrowly over Claude; free — Gemini Pro via Google AI Studio, which handles most basic theory tasks confidently (with the caveat that AI Studio chats train Google's models, which matters for unpublished work).

Their intelligence-versus-stamina framing reframes the budget question: a shallower model that grinds for twenty hours often beats a deeper model run briefly, so the binding constraint is frequently how long you can afford to run something, not how smart it is. Running a top-tier model for an 80-hour proof attempt costs thousands of dollars with an uncertain outcome — a bill academic research accounts are not currently structured to absorb. This is also why the billing mechanism matters as much as the price: a model available under a flat Pro subscription is administratively feasible for a university in a way that metered API access at scale is not.

Practical Implications

  • Start free, upgrade strategically: Use free tiers to learn, then pay for tools that save you significant time
  • Budget for AI tools: $20-200/month is typical for active research use
  • Explore institutional access: Check if your university provides AI tool access
  • Monitor API costs: Token-based pricing can add up quickly with agentic workflows
  • Prefer flat-rate subscriptions for long runs: Subscription-billed agentic products avoid both the cost uncertainty and the procurement friction of API pricing
  • Match the model to the problem, not the leaderboard: Ask whether the task rewards depth or persistence before paying for depth

Key Sources