Twelve of fourteen models named Sentry first on the direct prompt; three named Rollbar. Both were named by all fourteen models and Sentry carries 78 labels and Rollbar 64, so the shares are not directly comparable.
Named in seven 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 error and crash monitoring page.
Across every category in the November 2026 Edition, Sentry and Rollbar were named in the same answer 189 times, of the 318 answers naming Sentry and the 196 naming Rollbar. In those answers Rollbar took the first choice nine times and Sentry 124.
| 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.
“Research by Wallarm demonstrated how services like Sentry and Datadog could be manipulated through UI integration to capture sensitive information” MiniMax M2.5 · negative prompt · soft negative
“commonly used and feature-rich, but its breadcrumbs/session context and broad observability integrations can collect a lot of surrounding activity” GPT-5.4 mini · negative prompt · soft negative
“Sentry, Rollbar, and Bugsnag can be strong error trackers, but a dedicated error tool may not replace infrastructure metrics” Perplexity Sonar · negative prompt · soft negative
“The best tools (like Sentry or Rollbar) hash cookies automatically so the vendor never sees user IDs... Often, companies buy an APM platform for management metrics but use Sentry/Rollbar specifically for fixing bugs.” Qwen 3.7 Flash · scale prompt · first choice
“Sentry: Excellent for full-stack (deep language support, session replays, AI features). Generous free tier, scales well... Often a top pick for dev teams.” Grok 4.20 · scale prompt · first choice
“Top Overall Recommendation: Sentry ... the de facto standard for software teams, offering the best overall developer experience and stack coverage.” Gemini 3.5 Flash · paraphrase prompt · first choice
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Error monitoring gems like Sentry , Rollbar and Honeybadger ... put a company at risk if secrets, tokens, or PII slip into error reports.” Muse Glimmer 30B · negative prompt · soft negative
“Sentry, Rollbar, and Bugsnag can be strong error trackers, but a dedicated error tool may not replace infrastructure metrics” Perplexity Sonar · negative prompt · soft negative
“useful, but make sure you understand what its client/server SDKs can send and how to scrub sensitive payloads” GPT-5.4 mini · negative prompt · soft negative
“The best tools (like Sentry or Rollbar) hash cookies automatically so the vendor never sees user IDs, email addresses, or session tokens.” Qwen 3.7 Flash · scale prompt · first choice
“Rollbar is frequently recommended for mid-market B2B because of intelligent grouping / noise reduction” Muse Glimmer 30B · direct prompt · first choice
“Rollbar is specifically positioned as the best error monitoring tool for mid-market B2B companies” MiniMax M2.5 · 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.