Model verbosity: Save 91–94% on LLM API Costs
Does model verbosity save money on LLM API costs?
Yes, for the right workload shape: 91–94% based on Measured verbosityIndex spans 0.63×–11.31× across 23 graded models. Swapping the most verbose model for a median one (index 1.00×) saves the low end; swapping it for the least verbose covered model saves the high end. It does not apply universally — see the applicability and tradeoff sections below before using it.
How it works
Output tokens, not input tokens, are the lever here. We run every priced model against the same graded prompts and measure how many output tokens it actually spends per answer; the ratio to the group median across models that ran the same test is its verbosityIndex. A model at 1.6× talks 60% more than the median model to answer the identical question — and every one of those extra tokens is billed at the output rate, which is usually 3-5× the input rate. Swapping a high-verbosity model for a lower one on the same job cuts the output-cost side of the bill directly, without changing volume, caching, or batching.
Where it applies
Any output-token-heavy workload. Does nothing for jobs bottlenecked on input tokens (large retrieved context, long documents) since verbosity only scales the output side.
Verbosity range by provider
| Provider | Measured models | Verbosity range |
|---|---|---|
| OpenAI | 2 | 0.79×–1.04× |
| Anthropic | 1 | 0.96×–0.96× |
| 2 | 0.63×–0.88× | |
| xAI | 1 | 1.42×–1.42× |
| DeepSeek | 2 | 2.60×–3.31× |
| Mistral | 4 | 0.79×–0.93× |
| Groq | 4 | 0.77×–2.93× |
| Cerebras | 2 | 2.33×–7.55× |
| Amazon | 2 | 0.76×–0.92× |
| Z.ai | 1 | 3.87×–3.87× |
The tradeoff
A less verbose model is not free to swap to — it is a different model, with a different accuracy profile on your specific task. The verbosity index measures how many tokens a model spends per answer, not whether that answer is correct; a terser model that gets more graded runs wrong costs you in rework even if its raw token bill is lower. Verify accuracy on your own task before swapping, using the graded results on /best-llm-for, not the cost table alone. Verbosity is also measured per task family — a model terse on summarization can still be verbose on code generation, so the index for one job does not transfer to another.
Worked example
For llm chatbot (growing conversation history in, short reply out) at 200,000 calls/month, GPT-5.4 Mini runs $675/month at list price and $675/month once verbosity is priced in. See the full ranked table for this workload →