1.48
Claude Opus 5's index: 48% more output tokens than the task median. The most verbose model measured
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The Verbosity Index
Rate cards price tokens; models decide how many tokens your task takes. The Verbosity Index measures that decision: output tokens billed for identical work, across models, re-measured monthly. Edition 1, July 2026.
0.92
Claude Opus 4.8, its same-priced predecessor: 8% under median. The quiet efficiency win
+77%
what verbosity did to the bill: the identical six-task battery cost $0.2018 on Opus 5 vs $0.1140 on Opus 4.8, at identical rates
The Verbosity Index measures how many output tokens each model bills for identical work: six task types, two runs each, identical prompts, no length guidance. Edition 1 (July 2026): Claude Opus 5 indexes at 1.48 (48% above the task median), Claude Fable 5 at 1.14, Sonnet 5 at 1.13, Opus 4.8 at 0.92, Pareto at 0.75, and Haiku 4.5 at 0.61.
Edition 1: the index
| Model | Index | Six-task battery bill | Read |
|---|---|---|---|
| Claude Opus 5 | 1.48 | $0.2018 | most verbose measured; the whole 77% bill gap vs 4.8 is token count |
| Claude Fable 5 | 1.14 | $0.2722 | moderately verbose; the bill is mostly its 2x rate card |
| Claude Sonnet 5 | 1.13 | $0.0633 | verbose for a mid tier, cheap anyway |
| Claude Opus 4.8 | 0.92 | $0.1140 | the concise flagship: under median at frontier quality |
| Pareto | 0.75 | at cost | second most concise; token counts published, bills at cost |
| Claude Haiku 4.5 | 0.61 | $0.0225 | the most concise model measured |
Index = geometric mean of each model's output tokens relative to the per-task median, across all six tasks. 1.00 = median. Raw data: verbosity-index.json. GPT-5.6 models will join the index when we hold metering keys; list-math placeholders are deliberately excluded from a measured index.
Why this number matters more than the rate card
Two models can share a price sheet and produce different bills, because output tokens are the model's choice, not yours. The cleanest demonstration in Edition 1: Claude Opus 5 and Opus 4.8 both cost $5/$25 per million tokens, and the identical battery billed $0.2018 against $0.1140. Nothing about the rates changed between those two numbers. The model just writes more: 5,305 output tokens against 2,772 on the same code-generation prompt, 998 against 526 on the same analysis. Full head-to-head in Opus 4.8 vs Opus 5.
Per-task detail
| Mean output tokens | Code gen | Extraction | Summarize | Analysis | Short answer | |
|---|---|---|---|---|---|---|
| Claude Opus 5 | 5,305 | 319 | 226 | 998 | 782 | 273 |
| Claude Sonnet 5 | 4,032 | 319 | 209 | 970 | 508 | 125 |
| Claude Haiku 4.5 | 3,247 | 297 | 126 | 361 | 311 | 31 |
| Claude Fable 5 | 3,142 | 362 | 225 | 700 | 710 | 136 |
| Pareto | 2,876 | 259 | 113 | 632 | 330 | 81 |
| Claude Opus 4.8 | 2,772 | 319 | 194 | 526 | 474 | 108 |
Notable: on a one-line factual question ("what temperature does water freeze at"), Opus 5 wrote 273 tokens where Haiku wrote 31. Same correct answer, 8.8x the tokens.
What to do with it
Three practical uses. Budgeting: multiply rate-card math by the index before trusting a forecast; our calculator now has a measured-verbosity adjustment built in. Prompting: verbose models respond to explicit conciseness instructions and max-token caps; the index measures defaults, which is what most production traffic runs on. Model selection: a cheaper rate card with a higher index can be a wash; a same-priced upgrade with a higher index is a price increase nobody announced.
Method, fully
Six task types spanning production traffic: code generation, JSON extraction, summarization, quantitative analysis, email drafting, and a short factual answer. Identical prompts to every model, two runs each, default settings, no length guidance anywhere, measured 2026-07-30 via each provider's API with usage taken from the API's own token accounting. The index is the geometric mean of per-task ratios to the median, so no single task dominates. Prompts are published in the data file's companion battery script; if your run disagrees, publish it. Re-measured monthly; editions are archived, never overwritten.
Verbosity Index questions
What is the Verbosity Index?
A monthly measurement of how many output tokens each model bills for identical work: six task types, identical prompts, no length guidance, two runs per model. 1.00 is the per-task median across measured models; Opus 5's 1.48 means 48% more tokens than median for the same tasks.
Why does verbosity matter for cost?
Output tokens are the expensive side of every rate card, and the model chooses how many to write. In Edition 1, two models with identical $5/$25 pricing billed 77% apart on the same battery purely through token count.
Can prompting fix verbosity?
Substantially: explicit conciseness instructions and max-token limits claw back much of the gap. The index measures defaults because defaults are what most production traffic runs on, and because a model's default is a price decision its vendor made for you.
Why aren't GPT-5.6 models in the index?
We only index what we meter directly, and we don't yet hold OpenAI keys on the bench. List-price math without measured token counts would defeat the point. They join the moment we can meter them.
The 0.75 on that table bills at cost. Verify it.
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