Six of fourteen models named Auth0 first on the direct prompt; five named WorkOS AuthKit. Auth0 was named by fourteen of the fourteen models and WorkOS AuthKit by twelve and Auth0 carries 69 labels and WorkOS AuthKit 29, so the shares are not directly comparable.
Named in five categories this edition.
Named in two categories 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 customer identity and access management page.
Across every category in the November 2026 Edition, Auth0 and WorkOS AuthKit were named in the same answer seventy-seven times, of the 300 answers naming Auth0 and the 87 naming WorkOS AuthKit. In those answers WorkOS AuthKit took the first choice seventeen times and Auth0 twenty-five.
| 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 |
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.
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Post‑acquisition pricing increases (2–3× at renewal) reported by long‑term customers... Developer sentiment shows sharp declines in support quality” GLM 4.7 FlashX · negative prompt · hard negative
“its price scales steeply once you leave the free tier... For budget-conscious growth, alternative providers or open-source solutions generally offer a more predictable cost trajectory.” Gemini 3.5 Flash · budget prompt · soft negative
“Sudden Cost Escalation: Auth0 and Okta are market leaders with excellent developer experiences, but their pricing models can scale exponentially” Gemini 3.5 Flash · negative prompt · soft negative
“Excellent for developers, strong in B2C with passwordless, social login, and customization. Great docs and extensibility. Often tops B2C/developer rankings.” Grok 4.20 · scale prompt · first choice
“Auth0 remains the strongest default because of its maturity, enterprise SSO support (critical for B2B), multi-tenant organizations model” DeepSeek V4 Flash · paraphrase prompt · first choice
“Start with Auth0 if you want the most flexible, feature-rich solution with excellent developer experience and strong B2B capabilities.” GLM 4.7 FlashX · paraphrase prompt · first choice
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.
“WorkOS documented a major service disruption on July 16, 2026 with elevated latency, timeouts, and 5xxs across AuthKit, SSO, and user-management flows.” GPT-5.4 mini · negative prompt · soft negative
“a massive drop in recommendation acceptance for platforms like WorkOS (falling from 22% to 4%)... such a sharp deviation warrants close scrutiny” Qwen 3.7 Flash · negative prompt · soft negative
“Modern developer-first options with good UX, but verify their long-term viability and security maturity” DeepSeek V4 Flash · negative prompt · soft negative
“WorkOS stands out as the best customer identity and access management (CIAM) platform for most mid-market B2B companies” Grok 4.20 · direct prompt · first choice
“Best Overall for Modern B2B SaaS: WorkOS ... is often the top recommendation.” Gemini 3.5 Flash · paraphrase prompt · first choice
“WorkOS – *Best Overall for Enterprise Readiness & Developer Experience*” Gemini 3.5 Flash · direct 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.