Three of fourteen models named Illumio Segmentation first on the direct prompt; one named Akamai Guardicore Segmentation. Both were named by all fourteen models and Illumio Segmentation carries 55 labels and Akamai Guardicore Segmentation 51, so the shares are not directly comparable.
Named in one category 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 microsegmentation page.
Across every category in the October 2026 Edition, Illumio Segmentation and Akamai Guardicore Segmentation were named in the same answer 127 times, of the 169 answers naming Illumio Segmentation and the 149 naming Akamai Guardicore Segmentation. In those answers Akamai Guardicore Segmentation took the first choice eight times and Illumio Segmentation forty-six.
| 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 |
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.
“host agent-based solutions (like some older versions of Illumio, Guardicore, ColorTokens, or TrueFort) can be effective, they require deploying and maintaining agents on every workload” Mistral Small · negative prompt · soft negative
“While technologies like Illumio or Guardicore use this effectively, you should be cautious if your environment has strict stability requirements” Qwen 3.7 Flash · negative prompt · soft negative
“Illumio's core enforcement still requires an agent on every workload... unmanaged OT and IoT devices only receiving monitoring, not enforcement.” Claude Haiku 4.5 · negative prompt · soft negative
“If you want workload-centric segmentation across hybrid cloud with label-based policy and no inline appliances, Illumio is the reference.” Muse Glimmer 30B · comparative prompt · first choice
“Illumio (or Cisco Secure Workload if you're already Cisco‑shop) gives you the most granular, automated policy model.” MiniMax M2.5 · comparative prompt · first choice
“Choose Illumio if you want the clearest policy-by-label model and strong visual mapping of workload communication.” Perplexity Sonar · comparative 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.
“Avoid first if budget is tight: Akamai Guardicore, because pricing can be a constraint.” GPT-5.4 mini · budget prompt · hard negative
“some users report agent overhead, integration bugs, complex maps/visuals, and accuracy limits in risk detection” Grok 4.1 Fast · negative prompt · soft negative
“Initial mapping complexity and operational overhead... can be overwhelming to organize manually” Gemini 3.5 Flash · negative prompt · soft negative
“Akamai Guardicore Segmentation is a top choice due to its strong east-west visibility, ease of deployment, and support for hybrid environments” Mistral Small · paraphrase prompt · first choice
“Illumio and Akamai Guardicore Segmentation are generally considered the top contenders for this segment” Qwen 3.7 Flash · direct prompt · first choice
“For most mid-sized B2B companies, I'd recommend Akamai Guardicore Segmentation as your primary choice.” GLM 4.7 FlashX · paraphrase 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.