Choosing the Model Behind Your AI Agents
Most companies pick their foundation model the way they pick a coffee vendor. Somebody already had an account, so that is what everybody uses.
That works fine at ten seats. It stops working at a hundred and fifty. When teams finally sit down and compare Claude vs GPT pricing, the surprise is almost never the per-token rate. It is the moment the pricing model itself changes underneath them.
Our engineering team just spent an episode of Cirrius Talk taking both platforms apart. The most useful finding had nothing to do with which model is smarter.
The short version
- The surprise is the structure, not the rate. Line the two price sheets up and the dollar figures nearly match at every rung. What you get for those dollars does not.
- Around 150 seats, both vendors stop offering a flat rate. OpenAI tends toward shared credit pools. Anthropic tends toward metered usage. Same headcount, structurally different invoices.
- Reasoning depth is now a dial, not a product. The same model can answer cheaply or think expensively depending on one parameter.
- Top to bottom, the price gap is roughly fortyfold. Most teams route every task through one good model and pay frontier prices for work the cheapest rung handles.
- The tiebreaker is usually access, not capability. Data retention, context window, speed, and what is bundled versus metered decide more deployments than benchmarks do.
Both Vendors Change the Deal at Roughly the Same Point
Anthropic and OpenAI have converged on a strikingly similar shape. Free tier, a twenty dollar individual tier, a hundred dollar power tier, a two hundred dollar tier, then per seat team plans, then enterprise. Line the two price sheets up side by side and the dollar figures nearly match at every rung.
What you get for those dollars does not match. And somewhere around a hundred and fifty seats, both vendors stop offering a flat rate and start doing something else.
They do not do the same something else. That is the whole story.
Below the threshold
Both vendors are predictable.
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Below the threshold
You buy seats, each seat carries an allowance, and your finance team can forecast the bill to the dollar.
Above it, OpenAI pushes up
Toward shared credit pools.
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Above it, OpenAI pushes up
Enterprise agreements move to hybrid arrangements where one heavy user can draw against capacity a light user never touches.
Above it, Anthropic pushes sideways
Toward metered usage.
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Above it, Anthropic pushes sideways
The flat per seat allowance gives way to billing for what you actually consume rather than what is bundled into the seat.
Same headcount, two invoices
Different in structure, not just in price.
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Same headcount, two invoices
Which means they behave differently as your usage grows. That is the decision, not the rate card.
Neither approach is wrong. They favor different companies. A shared pool rewards organizations with lumpy, uneven demand, because the heavy weeks borrow from the light ones. Pure metering rewards organizations that genuinely know their consumption and do not want to prepay for headroom they will not use. A hard per seat cap rewards nobody except the vendor’s forecasting team, which is why it is worth reading the small print on capped tiers before you sign.
Reasoning Depth Became a Setting, Not a Product
The second finding is the one that quietly moves the most money.
Two years ago, choosing more reasoning meant choosing a different, more expensive model. That is over. On both platforms, reasoning effort is now a dial you turn per request. None, low, medium, high, and beyond. The same model can answer cheaply or think expensively depending on one parameter.
Both vendors also ship the same four rungs of capability, and the price gap between the bottom and the top is not subtle. It is roughly fortyfold on output tokens.
Fast and cheap. High volume classification, routing, simple chat. No reasoning dial at all, because the work does not need one.
Balanced. Most production workloads. Drafting, summarization, internal tools, support responses.
Deep. Complex agentic coding and long horizon work where a wrong answer costs more than the tokens.
Frontier. The hardest reasoning and multi day orchestration. Always thinking, always expensive.
Here is where the money leaks. Most teams wire their agents to one model, usually a good one, and route everything through it. Ticket classification, a two sentence summary, a twelve file refactor, all at the same depth and the same price.
“If your agents run every task at the top of the ladder, you are paying frontier prices for work the bottom rung handles perfectly well.”
Cirrius engineering team, Cirrius Talk
Getting this right is not a discount you negotiate. It is an architecture decision. Route by task, not by habit, and the same workload gets dramatically cheaper without anyone noticing a drop in quality.
The Tiebreaker Is Usually Not the Model
When a task genuinely could go either way, and many can, the deciding factor tends to be access rather than capability.
The clearest example is coding agents. Anthropic gates Claude Code behind a paid seat. OpenAI includes Codex in tiers that a developer may already have. If you are staffing a single developer who wants an agent in their terminal today, that difference decides it before anyone benchmarks a model. If you are standardizing two hundred engineers, it barely matters, because you are buying enterprise seats either way and the conversation moves to data retention, context windows, and throughput.
Those quieter factors rarely appear in comparison charts and frequently decide deployments:
Data retention policy
The first question security asks, the last one anyone researches.
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Data retention policy
What is kept, for how long, and whether it can be used for training.
Context window
How much fits in one pass before you build retrieval plumbing.
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Context window
Your codebase, a contract, or a case history. Run out of room and you are maintaining infrastructure you did not plan for.
Speed
A model that reasons beautifully in ninety seconds is the wrong model.
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Speed
For anything a customer is waiting on, latency is the requirement, not a nice-to-have.
Bundled versus metered
Two vendors can quote the same seat price.
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Bundled versus metered
One includes the coding agent. The other bills it separately. That gap does not show up in the comparison chart.
Pick Deliberately, Then Stay Portable
We have argued before that you should own the kernel and not the model. That still holds, and it is not in tension with any of this.
Owning the kernel means your prompts, your context discipline, your tool definitions, and your evaluation suite live in your codebase, not in a vendor’s console. The model is a dependency you can swap.
But swappable does not mean arbitrary. The pricing structure you choose shapes how your team is allowed to use agents, and that is a foundational decision, not a late implementation detail. Pick a shared pool and heavy users get room to work. Pick pure metering and somebody needs to own consumption monitoring from day one. Pick a hard capped tier and your best engineers will hit the ceiling in week three and quietly stop using the tool.
Choose it on purpose, at the foundation. Then build so you can change your mind in a quarter without a rewrite.
What to Do Now
Count your real seats, then look just past that number. If you are anywhere near a hundred and fifty, model your bill on both sides of the threshold before you renew anything. The structure change matters more than the rate.
Inventory what depth your agents actually run at. Pull a week of traffic and sort it by task type. Most teams find a large majority of calls are doing work the cheapest rung handles.
Route by task, not by default. Assign a tier to each job your agents do. This is usually a small change to a config file and the single largest cost lever available to you.
Get your security answers before your pricing answers. Retention, residency, and training use will constrain the choice more than price will. Settle them first so you are not renegotiating after a pilot.
Common Questions
Is Claude or GPT cheaper for AI agents?
Neither is reliably cheaper, and that is why the question misleads people. At small team size the two price sheets nearly match rung for rung. Past roughly a hundred and fifty seats the structures diverge, so the cheaper option depends on whether your usage is steady or lumpy and how much of it you can push to lower capability tiers. Model your own traffic. A generic answer will be wrong for you.
Why does AI pricing change around 150 seats?
Flat per seat allowances only work while a vendor can average heavy and light users against each other. At enterprise scale the variance gets too wide, so both vendors shift to arrangements that track actual consumption, either through a shared pool or through direct metering. Expect the flat rate to end somewhere in that range and plan for it.
Do I have to commit to one model vendor?
No, and you should design so you do not have to. Keep prompts, tool definitions, context handling, and evaluations in your own codebase so the model is a swappable dependency. Choose your primary vendor deliberately, because the pricing structure shapes how your team works, but do not let that choice calcify into an architecture you cannot change.
Where can I see the actual numbers?
Published rates move, so go to the source. Both Anthropic and OpenAI post current pricing. Our full August 2026 breakdown, including the tier by tier comparison and the seat cliff chart, is in the episode artifact.
We Build This
Cirrius runs more than fifty AI agents internally, across both platforms, on real production work. We have made the routing mistake, paid the frontier bill for classification tasks, and fixed it. That is why we can tell you where the money actually goes.
If you are choosing the model behind your agents, or you suspect you are overpaying for the one you already picked, we will sit down and model it with you.
