What's a safe default reasoning effort for a new agent?

Start with low or off. Only enable higher effort for the task classes where you've measured that non-reasoning output actually fails — usually multi-file coding diffs, root-cause debugging, complex planning, and legal/financial analysis. Enabling high effort everywhere is the single most common way to 5-30× a production agent's bill without improving output quality on the tasks that dominate the workload.

Reasoning Effort

Written by Max Zeshut

Founder at Agentmelt

A parameter on Reasoning Model APIs that controls how much Test-Time Compute the model spends before answering — OpenAI o-series uses `reasoning_effort` (`low` / `medium` / `high`), Anthropic uses `thinking.budget_tokens`, Google Gemini uses `thinking_config.thinking_budget`. Higher effort improves accuracy on hard tasks but multiplies token cost and latency. The 2026 production discipline is *routing*: pick the reasoning-effort level per-turn based on task class (plan-time turns and irreversible-action turns high, execution and formatting turns low or off) rather than setting a global default. See our [reasoning-models pillar](/blog/reasoning-models-for-ai-agents-2026/).

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What's a safe default reasoning effort for a new agent?: Start with low or off. Only enable higher effort for the task classes where you've measured that non-reasoning output actually fails — usually multi-file coding diffs, root-cause debugging, complex planning, and legal/financial analysis. Enabling high effort everywhere is the single most common way to 5-30× a production agent's bill without improving output quality on the tasks that dominate the workload.

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