SOAR platforms · October 2026 Edition
Shuffle vs Microsoft Sentinel
Zero of fourteen models named Shuffle first on the direct prompt; zero named Microsoft Sentinel. Shuffle was named by thirteen of the fourteen models and Microsoft Sentinel by fourteen and Shuffle carries 16 labels and Microsoft Sentinel 39, so the shares are not directly comparable.
Named in two categories this edition.
Named in ten categories this edition.
First-choice share18%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate0%8%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#2#7A position in a field of 11; printed, not drawn.
Labels1639A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Shuffle reading right to left. Rank and label count are printed, not drawn.Tines was named alongside these two in eleven of the fourteen direct answers. Tines vs Shuffle · Tines vs Microsoft Sentinel · Shuffle vs Swimlane Turbine
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 SOAR platforms page.
By framing
How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
ShuffleFirst choices, of fourteen modelsMicrosoft Sentinel
Direct00
Paraphrase11
Comparative001 against Microsoft Sentinel
Budget-constrained1001 against Microsoft Sentinel
Scale-constrained00
Negative001 against Microsoft Sentinel
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.
Every model, every framing
The eighty-four answers behind the chart above, one cell each: where Shuffle and Microsoft Sentinel stood in it.
| 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 | | | | |
Shuffle Microsoft Sentinel first choice named as an alternative argued againstblank: not namedEach cell is one answer, Shuffle on the left and Microsoft Sentinel on the right.The direct prompt
The plain question, one answer per model, grouped by where Shuffle and Microsoft Sentinel stood in it.
Neither was the first choice, one was named
9 of 14 modelsThe answer put something else first and named one of the two as an alternative.GPT-5.4 miniTines, Torq alternatives: Cortex XSOAR, Microsoft Sentinel, Splunk SOAR
Grok 4.1 FastSwimlane Turbine alternatives: Microsoft Sentinel, Rapid7 InsightConnect
Mistral SmallSwimlane Turbine, Torq alternatives: Microsoft Sentinel, Splunk SOAR, Tines
DeepSeek V4 FlashTines alternatives: Microsoft Sentinel, Rapid7 InsightConnect, Swimlane Turbine
Qwen 3.7 FlashTines alternatives: Microsoft Sentinel, Swimlane Turbine
Kimi K2Tines alternatives: Microsoft Sentinel, Rapid7 InsightConnect, Swimlane Turbine, Torq
GLM 4.7 FlashXCortex XSOAR, Tines alternatives: Microsoft Sentinel, Splunk SOAR, Swimlane Turbine
MiniMax M2.5Swimlane Turbine alternatives: FortiSOAR, Microsoft Sentinel, Splunk SOAR, Torq
Muse Glimmer 30BSwimlane Turbine, Tines, Torq alternatives: Cortex XSOAR, Microsoft Sentinel, Splunk SOAR
Neither was named
5 of 14 modelsThe answer made no first choice from these two in this category.Claude Haiku 4.5Swimlane Turbine, Torq alternatives: Cortex XSOAR, FortiSOAR, Splunk SOAR, Tines
Gemini 3.5 FlashTines alternatives: BlinkOps, Radiant Security, Swimlane Turbine, Torq
Perplexity SonarD3 Security Smart SOAR alternatives: ManageEngine Log360, ThreatConnect
Llama 4 MaverickTines
GPT-6 LunaTines alternatives: Cortex XSOAR, Microsoft Sentinel's built-in automation, Splunk SOAR
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
The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
Shuffle leads by two points.
Shuffle8%#3 of 11Microsoft Sentinel7%#4 of 11Mid-marketThe figures above
Shuffle leads by sixteen points.
Shuffle18%#2 of 11Microsoft Sentinel2%#7 of 11Enterprise
The order flips: Microsoft Sentinel leads at enterprise.
Microsoft Sentinel6%#4 of 11Shuffle0%#– of 11What the models said about Shuffle
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.
“If I had to pick one for a budget-conscious company with someone technical to manage it, I'd start with Shuffle's self-hosted SOAR.” GPT-6 Luna · budget prompt · first choice
“Shuffle open-source gives full SOAR capabilities at no license cost... It is regularly recommended for budget-limited orgs.” Muse Glimmer 30B · budget prompt · first choice
“| $0 budget, have Linux/DevOps skills | Shuffle (self-hosted) | Full-featured, free, large community |” DeepSeek V4 Flash · budget prompt · first choice
What the models said about Microsoft Sentinel
No label in this category carried a quote.
Also compared
Tines vs ShuffleTines vs Microsoft SentinelShuffle vs Swimlane TurbineShuffle vs TorqShuffle vs Cortex XSOARShuffle vs Rapid7 InsightConnectShuffle vs Splunk SOARSwimlane Turbine vs Microsoft SentinelTorq vs Microsoft SentinelCortex XSOAR vs Microsoft SentinelRapid7 InsightConnect vs Microsoft SentinelMicrosoft Sentinel vs Splunk SOARAll twenty-eight head-to-head pages and the full SOAR standing
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.