Two of fourteen models named Salt Security first on the direct prompt; one named Akamai API Security. Both were named by all fourteen models and Salt Security carries 43 labels and Akamai API Security 39, so the shares are not directly comparable.
Named in two 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 API security platforms page.
Across every category in the October 2026 Edition, Salt Security and Akamai API Security were named in the same answer ninety-six times, of the 133 answers naming Salt Security and the 125 naming Akamai API Security. In those answers Akamai API Security took the first choice eleven times and Salt Security twenty-eight.
| 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.
“Another platform frequently mentioned as having significant setup and operational overhead.” Mistral Small · negative prompt · hard negative
“Avoid Salt Security ($100k+/year)” Kimi K2 · direct prompt · hard negative
“Salt Security is frequently cited as a category pioneer for AI-powered behavioral threat detection and API discovery... also note typical enterprise caveats: shift-left and SIEM logging integrations are still maturing in places, and smaller teams report sales-led buying and opaque pricing.” Muse Glimmer 30B · negative prompt · soft negative
“If you want to stop complex logic abuse (like BOLA) in runtime without adding any latency to your production traffic, select Salt Security or Traceable AI.” Gemini 3.5 Flash · comparative prompt · first choice
“dedicated purpose-built solutions like Salt Security, Traceable AI, Wallarm, Akamai API Security, and Cequence Security” Grok 4.1 Fast · comparative prompt · first choice
“Purpose-built API security platform that combines API discovery, posture governance, and runtime threat detection in a single product.” Muse Glimmer 30B · 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.
“If you use cloud-delivery platforms (like Cloudflare, Fastly, or Akamai) on their basic, entry-level, or pro tiers, do not assume your APIs are safe.” Gemini 3.5 Flash · negative prompt · soft negative
“exercise caution with these due to recurring user-reported drawbacks ... Steep learning curve and complex initial setup” Grok 4.1 Fast · negative prompt · soft negative
“premium enterprise API security platforms (like Salt Security, Akamai/Noname, or Traceable) are usually out of reach” Gemini 3.5 Flash · budget prompt · soft negative
“For most mid-sized B2B companies, Akamai API Security stands out due to its comprehensive discovery and protection features” Mistral Small · paraphrase prompt · first choice
“A strong enterprise shortlist candidate for complex, hybrid estates and teams wanting broad governance and testing.” GPT-6 Luna · comparative prompt · first choice
“Akamai API Security is a very strong default choice if you want a purpose-built platform with broad capability” GPT-5.4 mini · 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.