Effort
A parameter in large language model reasoning systems that controls the depth of internal chain-of-thought computation before an answer is generated. Commonly offered at Low, Medium, and High levels, it directly affects output quality, token consumption, and response latency.
Core Facts
Timeline (last 90 days)
The Effort adjustment mechanism dynamically controls the depth of the model's reasoning chain, allowing low-Effort settings to respond quickly and high-Effort settings to trigger deeper reasoning, thereby balancing speed and accuracy.
Effort setting is an adjustment knob introduced by Anthropic in the extended thinking feature, allowing users to specify how much compute the model invests in internal reasoning before generating a response.
Haiku 5.5 is the first Haiku model with an adjustable effort setting.
Effort control is a parameter provided at the Claude Opus 5.5 API level that allows specifying how much computational resource the model invests in reasoning at call time, with the default value being Medium.
Low effort is suitable for deterministic tasks such as information retrieval and format conversion, while high effort is suitable for complex analysis requiring multi-step reasoning and weighing multiple solutions.
Lightweight model + Max is suitable for large-scale batch tasks that are budget-sensitive, require high throughput, and can tolerate slight quality fluctuations.
The core of token cost optimization is dynamically matching the combination of model tier and Effort to task difficulty, rather than simply seeking the cheapest model.
For everyday Q&A, the Medium or High tier of a lightweight model is sufficient, and in most cases it is unnecessary to go up to Max.
Effort (reasoning effort level) controls how much reasoning the AI invests when processing a problem; the higher the Effort is set, the more reasoning tokens the model generates.
Raising Effort for simple tasks yields almost no benefit, while simultaneously driving up both cost and latency.
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All Facts (13)
Haiku 5.5 is the first Haiku model with an adjustable effort setting.
70%UnverifiedThe Effort adjustment mechanism dynamically controls the depth of the model's reasoning chain, allowing low-Effort settings to respond quickly and high-Effort settings to trigger deeper reasoning, thereby balancing speed and accuracy.
50%UnverifiedEffort setting is an adjustment knob introduced by Anthropic in the extended thinking feature, allowing users to specify how much compute the model invests in internal reasoning before generating a response.
50%UnverifiedEffort control is a parameter provided at the Claude Opus 5.5 API level that allows specifying how much computational resource the model invests in reasoning at call time, with the default value being Medium.
50%UnverifiedLow effort is suitable for deterministic tasks such as information retrieval and format conversion, while high effort is suitable for complex analysis requiring multi-step reasoning and weighing multiple solutions.
50%UnverifiedLightweight model + Max is suitable for large-scale batch tasks that are budget-sensitive, require high throughput, and can tolerate slight quality fluctuations.
50%UnverifiedThe core of token cost optimization is dynamically matching the combination of model tier and Effort to task difficulty, rather than simply seeking the cheapest model.
50%UnverifiedFor everyday Q&A, the Medium or High tier of a lightweight model is sufficient, and in most cases it is unnecessary to go up to Max.
50%UnverifiedEffort (reasoning effort level) controls how much reasoning the AI invests when processing a problem; the higher the Effort is set, the more reasoning tokens the model generates.
50%UnverifiedRaising Effort for simple tasks yields almost no benefit, while simultaneously driving up both cost and latency.
50%UnverifiedThe combination of a lightweight model plus maximum Effort can often approach the perceived performance of a flagship model plus medium Effort.
50%UnverifiedEffort is an amplifier, while model tier is the capability ceiling; adding thinking budget to a smaller model cannot bridge the capability gap inherent to the model itself.
50%UnverifiedIn the field of document parsing, simply increasing reasoning effort does not noticeably enhance the native capability to read PDFs.
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