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Modern frontier models are trained with strong conversational reinforcement (RLHF), causing them to generate polite introductions, repetitive apologies, and extensive markdown commentary around simple answers. Because output tokens cost 3x to 5x more than input tokens, this verbosity represents the largest portion of any enterprise LLM bill. Curtly’s Output Brevity Governor injects optimized generation directives into model context, forcing models to produce direct, complete, and dense answers with zero conversational fluff.

The Output Token Economy

When the Output Brevity Governor is active, the conversational wrapper (up to 65% of the response) is eliminated, leaving only the essential code and logic.

Brevity Operational Levels

Curtly supports three configurable output brevity levels:

Live A/B Benchmark Results

In live benchmarks across frontier models (Claude Fable 5.1, Claude Opus 5.5, GPT-6 Astra, Grok 4.7, DeepSeek-V4.1-Flash), the Brevity Engine achieves its highest net savings on Claude Fable 5.1 (up to 81% to 83% output reduction by stripping verbose agentic preambles and runaway CoT scaffolding). Typical cross-model benchmark results on core coding tasks:

Example Output Comparison

Baseline Output (Without Curtly: 771 tokens)

“Certainly! Below is a generic TypeScript implementation of a binary search function with comprehensive error handling, strong type annotations, and detailed step-by-step comments explaining how the algorithm partitions the search space…”
“Here is how you can use this function in your application. First create an array, then pass the target element. Make sure the input array is sorted beforehand…”

Curtly Output (With Brevity Engine: 195 tokens, 75% Savings)


Enabling & Configuring Brevity

Via Drop-in Proxy Header

Set the X-Curtly-Brevity header on your proxy requests:

Via Official TypeScript SDK