TaxJar vs Numeral
Zero of fourteen models named TaxJar first on the direct prompt; zero named Numeral. TaxJar was named by fourteen of the fourteen models and Numeral by ten and TaxJar carries 59 labels and Numeral 17, so the shares are not directly comparable.
Named in four categories this edition.
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 sales tax automation page.
By framing
TaxJarFirst choices, of fourteen modelsNumeral
Across every category in the November 2026 Edition, TaxJar and Numeral were named in the same answer thirty-nine times, of the 177 answers naming TaxJar and the 47 naming Numeral. In those answers Numeral took the first choice two times and TaxJar eight.
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 4.20 | | | | | | |
| 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 | | | | |
TaxJar Numeral first choice named as an alternative argued againstblank: not namedEach cell is one answer, TaxJar on the left and Numeral on the right.The direct prompt
Neither was the first choice, one was named
5 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
9 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
TaxJar2%#5 of 11Numeral2%#7 of 11TaxJar0%#10 of 10Numeral0%#– of 10What the models said about TaxJar
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of five in this category shown.
“Common issues: Poor customer support ... failure to properly handle exempt customers ... inaccurate calculations or reports that require manual cross-checks” Grok 4.20 · negative prompt · hard negative
“post-acquisition quality has declined significantly... the software openly admits its reporting is broken” DeepSeek V4 Flash · negative prompt · hard negative
“TaxJar is not built for complex B2B scenarios, multi-entity structures, or audit defense situations” Claude Haiku 4.5 · direct prompt · hard negative
“Small company → TaxJar; ... TaxJar stands out from Avalara with higher reviewer satisfaction across ease of use and support” Muse Glimmer 30B · comparative prompt · first choice
“TaxJar is the easiest, most affordable, and most reliable choice.” DeepSeek V4 Flash · comparative prompt · first choice
What the models said about Numeral
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of six in this category shown.
“Numeral's reported filing reliability issues are a concern for businesses with real compliance obligations” Claude Haiku 4.5 · negative prompt · soft negative
“Quote-only | Usage overages” Kimi K2 · negative prompt · soft negative
“Numeral is often the best sales tax solution for ecommerce and SaaS companies, wholesalers, and manufacturers.” Llama 4 Maverick · budget prompt · first choice
“Excellent for growing SaaS/ecommerce or simpler needs. AI-powered calculation, filing, and expert review at potentially lower cost.” Grok 4.20 · direct prompt · alternative
“Numeral is a true full-service solution for DTC and marketplace sellers, subscription software, and B2B sellers” Claude Haiku 4.5 · direct prompt · alternative
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.