Question 1

Why does the "smart" planner/orchestrator pattern end up costing more than a single ReAct agent in this calculator?

Accepted Answer

Because the orchestrator being lean and the system being cheap are two different things. In the planner/worker pattern, the central orchestrator only reads back a compressed summary from each subagent — a few hundred tokens — which makes the orchestrator's OWN context small and keeps it far below any context-window ceiling. But every one of those subagents still runs its own full internal loop of think-act-observe steps to actually get its piece of the task done, and every one of those loop tokens gets generated and billed somewhere, even though the orchestrator never sees them directly. With 3 subagents each running 4 loops at 3,000 tokens per loop, that is 36,000 tokens of subagent work happening off to the side, plus the orchestrator's own planning tokens and the summaries coming back — system-wide spend that a single ReAct agent doing 5 loops of the same task never has to pay, because there is only one loop running, not one orchestrator loop plus three parallel subagent loops.

Question 2

Is tree-of-thought or multi-agent debate ever worth the extra cost?

Accepted Answer

Yes, but the case for paying the multiplier has to rest on something other than raw token cost, because on tokens alone both patterns lose to a single ReAct loop every time. Tree-of-thought earns its 1.8x (or worse, at higher branching or depth) when a task has a genuinely large solution space where premature commitment to one reasoning path is likely to produce a wrong answer that then has to be redone from scratch — the extra branches are insurance against a much more expensive retry, not a free upgrade. Multi-agent debate earns its cost when a single model's own self-consistency is the actual risk, such as high-stakes classification or adversarial review, where having independent agents reach answers separately and then be judged catches errors a solo pass would miss. If the task is well within a single agent's reliable range, neither pattern is worth its multiplier — you are paying for insurance you don't need.

Question 3

Does prompt caching change which architecture is cheapest?

Accepted Answer

It shrinks all four costs by roughly the same proportion without reordering them, because caching discounts repeated input-token prefixes (system prompts, tool schemas, shared context) and every one of these four patterns still needs those prefixes resent on every loop, branch, agent turn or subagent step. ReAct's 5 loops each partly benefit from caching the repeated prefix, and so do tree-of-thought's branches, debate's per-agent rounds, and each planner subagent's internal loops — the discount applies roughly evenly across the board rather than favoring one pattern's shape over another. What caching does not do is change the underlying loop-count arithmetic: a planner spawning 3 subagents that each run 4 loops is still generating far more total generated (output) tokens than a single 5-loop ReAct agent, and output tokens are typically not eligible for the cached-input discount at all, so the architecture-pattern ranking in this calculator holds regardless of your caching setup.

Question 4

How is this different from the agent loop budget calculator and the AI agent framework cost calculator already on this site?

Accepted Answer

All three price agent token spend but at different layers of the same stack. The agent loop budget calculator prices a SINGLE agent's own loop-iteration budget — given a per-loop token cost and a spending ceiling, how many think-act-observe turns can that one agent afford before you have to cut it off — it never compares across architectures, only across how many turns one agent gets. The AI agent framework cost calculator compares the tooling and infrastructure overhead of running a given agent workload on different frameworks, such as CrewAI versus LangGraph versus a custom loop — the same architecture pattern, priced under different plumbing. This calculator sits one layer up from both: it holds the framework and the per-loop cost constant and instead compares four different ARCHITECTURAL PATTERNS for solving the same task — single-agent ReAct, tree-of-thought branching, multi-agent debate, and planner/orchestrator with isolated subagents — to show that the pattern you pick multiplies your token spend before framework choice or loop budgeting ever enters the picture.

マルチエージェントオーケストレーションアーキテクチャのコスト計算ツール

共有ループのコスト

ReAct — 単一エージェントのベースライン

思考の木

マルチエージェントの討論

プランナー / オーケストレーター + 分離されたサブエージェント

同じタスクのアーキテクチャごとのコスト

これが他のツールにどのように接続されるか

この計算機の仕組み

よくある質問

この計算ツールでは、「スマート」プランナー/オーケストレーターパターンが最終的に 1 つの ReAct エージェントよりもコストがかかるのはなぜですか?

思考の木や複数のエージェントによる議論には、追加のコストを支払う価値があるでしょうか?

プロンプトキャッシュによって、どのアーキテクチャが最も安価かは変わりますか?

これは、このサイトにすでにあるエージェントループの予算計算ツールや AI エージェントフレームワークのコスト計算ツールとどう違うのですか?