The cheap answer changes the moment your agents stop being a demo and start working eight hours a day. I ran the numbers for the workflow I actually care about: Product Owner → Tech Lead → Developer → Reviewer → QA → Stakeholder.
I started with what looked like a simple question: if I am going to orchestrate several coding agents, should I pay for Claude or OpenAI APIs, or should I lean on a subscription product such as Codex or Cursor CLI? For a small experiment the answer barely matters. A few dollars of API usage can get you surprisingly far. But the economics change when the agents are reading a repository, planning, editing, compiling, testing, reviewing diffs, fixing failures, and looping again for an entire month. That is the point where “which model is cheaper?” becomes the wrong question. The real question is which execution model is cheaper for the kind of agent workload I am running? Short version: subscriptions are often attractive for an individual developer doing intensive interactive agent work. APIs remain the cleaner foundation for autonomous orchestration, production workloads, model routing, observability, and predictable scaling. The cheapest architecture is usually hybrid. The workflow I am pricing I am not talking about one chat window generating a class. I mean a workflow that looks closer to a small software delivery loop: GOAL │ ▼ Product Owner │ ▼ Tech Lead │ ▼ Developer ───────┐ │ │ ▼ │ Compiler / Tests │ │ │ ▼ │ Reviewer │ │ │ ▼ │ QA ───── FAIL ───┘ │ ▼ Stakeholder │ ▼ DONE Once every role is an LLM call, cost grows faster than most demos make obvious. The Developer may need eight iterations. The Reviewer may inspect the same diff three times. QA may send failures back into the loop. Every pass can bring repository context, system instructions, tool results, and previous decisions with it. First mistake: comparing only subscription prices Cursor currently lists individual plans at $20/month for Pro, $60 for Pro+, and $200 for Ultra. Cursor recommends Pro+ for daily Agent users and Ultra for agent power users. But every plan still represents a defined amount of model usage. Once included usage is consumed, on-demand usage can continue and is billed separately. Codex has a similar nuance. Codex is available through ChatGPT plans, but OpenAI now accounts for Codex consumption using token-based credits for most customers. OpenAI explicitly notes that the actual cost depends on model choice, concurrent instances, automations, task complexity, context, tools, reasoning level, and speed. Their current guidance says Codex averages roughly $100–$200 per developer per month , while emphasizing that real consumption can vary significantly. A $200 subscription is not the same thing as $200 of unlimited autonomous inference. That distinction matters if you intend to turn a CLI into a backend worker and keep it busy around the clock. Second mistake: putting the strongest model behind every agent A Product Owner agent does not necessarily need the same reasoning budget as the Developer. A deterministic test runner does not need an LLM at all. A QA agent validating a JSON contract may be able to use a much cheaper model than the one debugging a concurrency problem. ORCHESTRATOR │ ┌─────────────────┼──────────────────┐ │ │ │ ▼ ▼ ▼ Deterministic Cheap model Strong model code │ │ │ │ │ git diff classify implement compiler summarize debug tests validate refactor Sonar route hard review This is where API-based orchestration becomes powerful: I can route each step to the cheapest model that is good enough and reserve expensive reasoning for failures or escalations. A simple monthly model Imagine 500 tasks in a month. Each task triggers about 18 agent interactions across planning, implementation, review, and QA. That is roughly 9,000 agent interactions. If an average interaction consumes 20K input tokens and 3K output tokens, the month is approximately: 180 million input tokens 27 million output tokens Using current public API pricing as examples, Claude Sonnet 5 is $2 per million input tokens and $10 per million output tokens. Claude Opus 5 starts at $5 input and $25 output per million tokens. The important point is not the exact invoice — caching, retries, context size, batch processing, and routing all change it — but the shape of the curve. Approach Cost behavior Best fit Primary risk Claude / OpenAI API Scales with tokens and model choice Autonomous orchestration Heavy loops can become expensive Codex Plan usage + token-based credits / limits Developer-centric coding agents Not an unlimited inference backend Cursor CLI / Agent Subscription pools + on-demand usage Interactive development and power users Third-party model usage can burn pool quickly Hybrid Fixed-ish developer tooling + metered API routing Labs and high-volume personal workflows More architecture to operate Where Cursor becomes attractive Cursor gives me much more than an LLM endpoint: repository-aware context, Agent, Cloud Agents, MCP, skills, hooks, editor integration, and CLI support. That infrastructure has real value when I am the person driving the workflow. Cursor also maintains separate usage pools, including one for its own models and another for third-party models. Third-party models consume usage based on their API pricing, so choosing an expensive model can drain the included pool faster. For one engineer living in the IDE all day, that can still be an excellent deal because I am paying for the entire working environment rather than only raw inference. Where Codex becomes attractive Codex is even closer to the shape of a coding worker. It can operate through the CLI, IDE, desktop, or web, and the same ecosystem is built around performing software tasks rather than simply returning completions. If my agent's job is: read repo ↓ implement ↓ compile ↓ test ↓ inspect diff ↓ fix ↓ repeat then a coding agent can remove a lot of orchestration code I would otherwise have to build myself. For a personal lab or an engineer running many tasks throughout the day, that is compelling. What I would not do is quietly turn a personal subscription into the assumed economics of a production multi-agent platform. Product limits, credit pools, concurrency behavior, and pricing policies can change independently of my application. Why APIs still win for a real orchestrator With an API-first orchestrator I own the execution policy. agent.run( model = route(task), maxBudget = budgetFor(task), tools = allowedTools(task), timeout = timeoutFor(task), retries = retryPolicy(task), cache = cachePolicy(task), traceId = executionId ); That gives me explicit control over model routing, token budgets, retries, structured output, parallelism, context, caching, fallbacks, telemetry, cost attribution, and security boundaries. More importantly, it lets me stop treating every SDLC step as an AI problem. The cheapest agent is the one I do not call This became the most important part of the exercise. I can spend hours optimizing Sonnet versus GPT pricing and still waste more money by asking an LLM to do work that Git, Gradle, Maven, the compiler, JUnit, Sonar, or a schema validator can do deterministically. DON'T: Developer → LLM Reviewer → LLM QA → LLM Tests → LLM Diff → LLM Rules → LLM DO: Developer → strong coding model Reviewer → model only when judgment is required QA → cheap model + deterministic gates Tests → test runner Diff → git Rules → code Build → Gradle / Maven That design change can matter more than switching providers. The architecture I would use for intensive monthly usage For a lab I am running heavily every day, I would not choose one provider and forc...