incident.io vs Squadcast
Eleven of fourteen models named incident.io first on the direct prompt; zero named Squadcast. incident.io was named by fourteen of the fourteen models and Squadcast by twelve and incident.io carries 66 labels and Squadcast 20, so the shares are not directly comparable.
Named in one category 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 incident management and on-call page.
By framing
incident.ioFirst choices, of fourteen modelsSquadcast
Across every category in the November 2026 Edition, incident.io and Squadcast were named in the same answer thirty-four times, of the 180 answers naming incident.io and the 40 naming Squadcast. In those answers Squadcast took the first choice two times and incident.io twenty.
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 | | | | |
incident.io Squadcast first choice named as an alternative argued againstblank: not namedEach cell is one answer, incident.io on the left and Squadcast on the right.The direct prompt
incident.io first, Squadcast an alternative
11 of 14 modelsSquadcast was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
2 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
1 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
incident.io39%#1 of 14Squadcast2%#8 of 14incident.io47%#1 of 13Squadcast4%#5 of 13incident.io18%#2 of 12Squadcast0%#– of 12What the models said about incident.io
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of six in this category shown.
“G2 reviews for incident.io mention setup complexity and limited status-page flexibility in some cases.” GPT-5.4 mini · negative prompt · soft negative
“Slack-centered coordination can be insufficient when Slack or identity systems are unavailable.” Perplexity Sonar · negative prompt · soft negative
“incident.io: A Slack-native platform that eliminates the "coordination tax" through automated channel creation, timeline capture, and AI-powered post-mortems.” Llama 4 Maverick · negative prompt · first choice
“For a small team with limited budget and Slack as a core communication tool: Incident.io (free tier) or Rootly (free tier).” GLM 4.7 FlashX · budget prompt · first choice
“For SRE & Slack-Native Engineering Teams: Look at modern workflow platforms like incident.io, Rootly, or All Quiet.” Gemini 3.5 Flash · negative prompt · first choice
What the models said about Squadcast
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.
“Squadcast offers excellent value if you need more users and advanced features at a low price point.” Claude Haiku 4.5 · budget prompt · first choice
“I recommend Squadcast as the top choice for on-call scheduling and alerting.” Grok 4.20 · paraphrase prompt · first choice
“Good budget option ... Specifically noted for mid-market teams wanting unified SLO tracking” DeepSeek V4 Flash · 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.