Four of fourteen models named Recorded Future Intelligence Cloud first on the direct prompt; two named ThreatConnect. Recorded Future Intelligence Cloud was named by fourteen of the fourteen models and ThreatConnect by thirteen and Recorded Future Intelligence Cloud carries 54 labels and ThreatConnect 37, so the shares are not directly comparable.
Named in one category this edition.
Named in four 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 threat intelligence platforms page.
Across every category in the October 2026 Edition, Recorded Future Intelligence Cloud and ThreatConnect were named in the same answer 107 times, of the 175 answers naming Recorded Future Intelligence Cloud and the 132 naming ThreatConnect. In those answers ThreatConnect took the first choice six times and Recorded Future Intelligence Cloud forty-one.
| 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.
“Verdict: Avoid if you have a limited budget or require high-volume, automated ingestion into many disparate tools without manual tuning.” GLM 4.7 FlashX · negative prompt · hard negative
“one buyer guide flags "standard" TIPs like Recorded Future as something to avoid in some enterprise-fit comparisons” Perplexity Sonar · negative prompt · hard negative
“recommended to avoid if you have a limited budget or need high-volume, automated ingestion across many tools” Mistral Small · negative prompt · hard negative
“For Comprehensive Coverage: If you need to monitor everything from ransomware forums to geopolitical shifts without hiring more analysts, choose Recorded Future.” Qwen 3.7 Flash · comparative prompt · first choice
“Best For: Enterprises needing comprehensive, real-time, and automated threat intelligence with broad integration capabilities.” Mistral Small · comparative prompt · first choice
“Recorded Future is the best all-around choice due to its balance of automation, integration, and actionable intelligence” Mistral Small · 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.
“Verdict: Avoid enterprise-heavy versions unless your team is growing rapidly and has dedicated technical resources” GLM 4.7 FlashX · negative prompt · hard negative
“avoid Recorded Future, Mandiant, ThreatConnect, and Anomali unless you have a specific need” Mistral Small · negative prompt · hard negative
“Enterprise-grade, heavy-duty TIPs (such as ThreatConnect or ThreatQuotient) require dedicated staff to manage and are usually overkill.” Gemini 3.5 Flash · paraphrase prompt · soft negative
“ThreatConnect is the safest strategic bet due to its flexibility and ability to scale with your headcount” Qwen 3.7 Flash · direct prompt · first choice
“The Best Overall Platform: ThreatConnect ... widely considered the "sweet spot" for the mid-market” Qwen 3.7 Flash · paraphrase prompt · first choice
“I'd recommend starting with SOCRadar or ThreatConnect based on your specific needs” Kimi K2 · 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.