Smarty vs Melissa
Nine of fourteen models named Smarty first on the direct prompt; zero named Melissa. Both were named by all fourteen models and Smarty carries 61 labels and Melissa 39, so the shares are not directly comparable.
Named in one category this edition.
Rancho Santa Margarita, United States, founded 1985. Named in one category this edition.
Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen models, for a mid-market B2B company; rank is within the category; every quote names the model and the prompt it came from. Both figures come from the address verification tools page.
By framing
SmartyFirst choices, of fourteen modelsMelissa
Across every category in the October 2026 Edition, Smarty and Melissa were named in the same answer 102 times, of the 191 answers naming Smarty and the 110 naming Melissa. In those answers Melissa took the first choice ten times and Smarty sixty-two.
Every model, every framing
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | | | | | ||
| GPT-5.4 mini | | | | | ||
| Gemini 3.5 Flash | | | | | ||
| Perplexity Sonar | | | | | ||
| Grok 4.1 Fast | | | | | | |
| Mistral Small | | | ||||
| DeepSeek V4 Flash | | | | | | |
| Llama 4 Maverick | | | ||||
| Qwen 3.7 Flash | | | | | | |
| Kimi K2 | | | | | | |
| GLM 4.7 FlashX | | | | |||
| MiniMax M2.5 | | | | |||
| GPT-6 Luna | | | | | ||
| Muse Glimmer 30B | | | | |
Smarty Melissa first choice named as an alternative argued againstblank: not namedEach cell is one answer, Smarty on the left and Melissa on the right.The direct prompt
Smarty first, Melissa an alternative
9 of 14 modelsMelissa was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
3 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
2 of 14 modelsThe answer made no first choice from these two in this category.Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.
By buyer segment
Smarty67%#1 of 6Melissa4%#3 of 6What the models said about Smarty
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.
“Smarty (formerly SmartyStreets) is a fast and highly scalable address verification API used heavily in high-volume e-commerce and logistics environments.” Muse Glimmer 30B · comparative prompt · first choice
“I would recommend Smarty if your volume is mostly U.S. addresses and you want a developer-friendly API with strong validation tooling” Perplexity Sonar · paraphrase prompt · first choice
“### 1. Smarty (formerly SmartyStreets) - Best for: US address verification, developer-friendly implementations, e-commerce checkout” Kimi K2 · comparative prompt · first choice
What the models said about Melissa
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of six in this category shown.
“General cautions for all address verification tools (e.g., Smarty, Melissa, Quadient, Shufti Pro, SumSub, Veriff, DataFlow)” Grok 4.1 Fast · negative prompt · soft negative
“Melissa is generally heavier and more complex to implement. It is less of an "add-on" tool for a website” Qwen 3.7 Flash · comparative prompt · soft negative
“Melissa - Offers a comprehensive address verification API with features such as real-time address validation, standardization, and enrichment.” Llama 4 Maverick · paraphrase prompt · first choice
“The Industry Standards (High accuracy, widely used): Smarty (formerly SmartyStreets), Loqate (Onbuka), Melissa (DataBox).” Qwen 3.7 Flash · scale prompt · first choice
“Melissa offers the best free tier for small businesses needing additional validation features” MiniMax M2.5 · budget prompt · first choice
Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.