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In a Stanford Digital Economy Lab analysis of coding agents on SWE-bench Verified, agentic tasks used far more tokens than code chat or code reasoning, but higher token use did not reliably produce higher accuracy. That is evidence about the evaluated models and coding tasks—not proof that every agent team wastes tokens or that extra computation never helps. To judge whether a team is worth its cost, compare task quality with actual token use across repeated runs.
What the Stanford study found
The Stanford Digital Economy Lab working paper, dated April 14, 2026, analyzes trajectories from eight frontier language models on SWE-bench Verified and examines how well models predict token costs. Its summary reports several findings about agentic coding in that benchmark setting.
- Agentic tasks used as much as 1,000 times more tokens than code reasoning and code chat. The summary identifies input tokens, rather than output tokens, as the main driver of overall cost.
- Total token use differed by as much as 30 times between runs of the same task. That is a maximum run-to-run difference, not a typical multiplier for every task.
- On the same evaluated tasks, Kimi-K2 and Claude-Sonnet-4.5 used over 1.5 million more tokens on average than GPT-5.
- Models’ predicted and actual token costs had a correlation of up to 0.39; the study summary describes the relationship as weak to moderate and says models systematically underestimated actual costs.
These results come from the Stanford Digital Economy Lab working-paper summary. They should not be generalized to every agent framework, task, model, or deployment.
Did more tokens produce better results?
Not consistently in the evaluated coding tasks. The Stanford summary says accuracy often peaked at an intermediate token cost and then saturated at higher costs. Token consumption alone, therefore, was not a dependable measure of task quality in this setting.
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This finding does not establish that more computation never improves an answer, or that multi-agent systems are always less accurate than single-agent systems. It shows that spending more tokens was not a reliable route to higher accuracy across the study’s evaluated runs.
Why can agentic coding consume so many tokens?
The measured cost was driven mainly by input tokens. An agentic workflow may repeatedly send task context, code, tool results, and prior interaction history to a model as it works through a task. The Stanford summary establishes the input-token finding, but does not identify one universal workflow pattern as the cause; how much context is resent will depend on the system.
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That also helps explain why a model may underestimate its eventual bill: the cost depends on the full sequence of interactions, not just the final answer. In the Stanford analysis, models’ estimates were weakly to moderately related to actual costs and tended to be low.
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Evaluate cost and quality together on the tasks that matter to you. A fair comparison should keep the task set and, where possible, the model budget comparable; otherwise, a quality difference may reflect a different budget or workload rather than the team design.
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- Choose a quality measure. For coding work, use task success or accuracy against a defined test set or acceptance criteria, rather than judging output length or apparent effort.
- Record actual usage. Track input and output tokens for each run. Since input tokens drove overall cost in the Stanford summary, do not monitor output tokens alone.
- Repeat tasks. Record multiple runs and report the spread as well as the average. A single run may not reflect token use reliably when the same task can vary substantially between runs.
- Compare architectures fairly. When comparing a single agent with a team, use the same tasks and comparable model and token budgets where possible. Report quality and total token use side by side.
- Check forecasts against bills. Compare estimated token costs with actual usage before using estimates for budgets or limits; the studied models tended to underestimate costs.
This is a practical evaluation approach based on the study’s measured dimensions, not a protocol reported by the researchers. Teams may still be worthwhile if they deliver a meaningful improvement on the tasks you care about at a cost you accept; token totals alone cannot answer that question.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the collaboration evidence does—and does not—show
A 2026 Nature Machine Intelligence article titled “Capable language models can outgrow the benefits of collaboration” is relevant context: its available summary says it examined single-agent and multi-agent architectures across model families and agentic benchmarks, and notes that token-based communication creates latency and cost floors. The full article was not accessible for review, so detailed effect sizes or broader conclusions cannot be established from that summary. It is a caution against assuming collaboration always adds value, not a quantified rule for choosing between one agent and a team.
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