Your prompt is
the cost center.
Researchers asked what drives token spend in large reasoning models running coding tasks. They held task difficulty constant and varied only the instructions. The words you use to ask for help, it turns out, move spend more than the complexity of what you are asking for.
First surfaced in Tandemly Briefing — 2026-08-02.
Everybody optimizes
the wrong thing.
Most cost-reduction work for AI coding agents targets the plumbing: compress context, cache prefixes, cut tool calls. This paper targets the demand side. What if the instructions themselves are the cost?
When reasoning models became available, their token economics changed in a specific way. These models think before they respond. They run an internal deliberation step that generates tokens you never see but absolutely pay for. A difficult task warrants more deliberation. Everyone accepts that. But the implicit assumption underneath most cost discussions is that deliberation tracks task difficulty, not instruction phrasing.
That assumption turns out to be wrong in a measurable and consequential way. The same coding task, asked with different wording, produces wildly different deliberation token counts with no corresponding difference in whether the code works. The model is not doing more useful thinking. It is doing more thinking because the instruction asked it to.
This is a different problem from the one most cost tooling addresses. Context compression, KV-cache tuning, and retrieval filtering all operate on the data that flows through the system. Prompt-induced waste operates on the request itself. You can have perfectly optimized infrastructure and still be burning money on the way you phrase things.
Cost studies on agent systems are easy to confound. Task difficulty, model routing, caching state, and environment variance all move the numbers. The authors preregistered their hypotheses before collecting data, which makes it harder to select the result after the fact. The causal story, prompt phrasing drives cost independent of task difficulty, is more credible here than in a post-hoc analysis.
Same tasks, different
words, 4,643 runs.
The study ran two separate agent harnesses over the same set of coding tasks, varying only the instruction phrasing. Three categories of phrasing were tested. The measurements: deliberation tokens, tool calls, and repeated agent turns. Correctness was held as the control variable.
The setup starts with a fixed task set: coding problems with verifiable correct answers, so you can check whether changing the prompt actually changed the output quality or just the cost. Two different agent harnesses ran the same tasks under each prompt condition. Using two harnesses is a key design decision: if an effect shows up in both, it is probably real and not an artifact of how one specific framework handles context.
The three prompt conditions target different intuitions practitioners commonly act on. The first is the "explore thoroughly" instinct: telling an agent to develop and compare multiple approaches before committing. The second is the generic effort cue: instructions that say to think carefully or reason deeply without specifying what that means. The third is a bounded template: an explicit scope statement, clear acceptance criteria, and a stop condition that tells the model when it has done enough.
Across 4,643 valid runs the study measured deliberation tokens generated before the first code output, the number of tool calls made, and whether the agent repeated agentic turns on the same task. These three measures together capture where the spend actually goes: internal reasoning, external calls, and retry loops.
The bounded template is described as cost-neutral relative to a baseline prompt with no special framing. It does not add overhead. It removes waste. The savings relative to the open-ended exploration condition range from cost-neutral to a halving of deliberation tokens depending on the harness and task type. The correctness numbers do not move.
The words inflate the bill.
Not the task.
The numbers are large enough to matter for anyone running production coding agents at scale. The correctness numbers are flat. The cost numbers are not.
Practitioners add "develop and compare several approaches" to agent prompts with good intentions. It sounds rigorous. It sounds like it should produce better code. Across 4,643 runs it produced code of equivalent correctness at 2.4 to 7.4 times the deliberation token cost. The variation in that range is across harnesses and task types, but in every condition it was worse, never neutral or beneficial.
The "think deeply" family of instructions sits in a middle tier. They inflate deliberation tokens 1.6 to 2.2 times without moving correctness. They are less harmful than explicit exploration instructions, but at scale they represent the same category of waste: paying for deliberation that does not change the answer. The mechanism is similar: the model interprets an open-ended cue as license to reason more broadly, not as a directive toward a specific kind of reasoning.
The bounded template is not just "be more specific." It has three components, and each does work. Scope narrows what the model considers in scope. Acceptance criteria define what done looks like. The stop condition tells the model when deliberating further is no longer productive. All three together produce the cost reduction. A prompt that has scope and acceptance criteria but no explicit stopping signal still allows the model to continue deliberating after the answer is evident, which is where a significant fraction of waste appears in the exploration condition.
The study covers coding tasks with verifiable correct answers across two agent harnesses. It does not cover open-ended creative or analytical tasks where exploration genuinely changes quality. The inflation factors apply to large reasoning models that have a distinct internal deliberation step. Instruction-following models without a separate reasoning phase may respond differently. The bounded template requires knowing the acceptance criteria in advance, which is easier for well-specified coding tasks than for ambiguous or exploratory work.
Audit the words,
not just the pipeline.
Cost reduction for AI coding agents usually starts with infrastructure. This paper suggests the instruction layer deserves the same attention. You can build an optimized agent harness and still be spending 2 to 7 times too much on internal reasoning because of how the prompt is written.