Ten of fourteen models named Secure Code Warrior Learning Platform first on the direct prompt; zero named Security Journey. Secure Code Warrior Learning Platform was named by fourteen of the fourteen models and Security Journey by eleven and Secure Code Warrior Learning Platform carries 63 labels and Security Journey 32, 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 secure code training page.
Across every category in the November 2026 Edition, Secure Code Warrior Learning Platform and Security Journey were named in the same answer ninety-nine times, of the 194 answers naming Secure Code Warrior Learning Platform and the 106 naming Security Journey. In those answers Security Journey took the first choice six times and Secure Code Warrior Learning Platform forty-five.
| 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 |
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.
“Avoid premium platforms such as Secure Code Warrior or SecureFlag unless their stronger language coverage, labs, and analytics justify the higher cost” Perplexity Sonar · budget prompt · hard negative
“What to Avoid ... Enterprise-focused; typically high per-user cost and requires annual contracts.” DeepSeek V4 Flash · budget prompt · hard negative
“Be most cautious about Secure Code Warrior given its consistently poor developer reception” DeepSeek V4 Flash · negative prompt · hard negative
“Secure Code Warrior, now the developer training engine behind Black Duck Developer Security Training, is a strong core fit for mid-size B2B” Muse Glimmer 30B · paraphrase prompt · first choice
“Choose Secure Code Warrior if you want high engagement, gamification, skills assessments, and broad rollout to many developers.” GPT-5.4 mini · comparative prompt · first choice
“If you want the biggest hands-on challenge library and enterprise benchmarking, Secure Code Warrior is the consensus #1.” Muse Glimmer 30B · direct 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.
“often compared unfavorably to competitors in evaluations... Video-heavy content that some users feel lacks depth and interactivity” MiniMax M2.5 · negative prompt · soft negative
“often more expensive and formal, suitable for organizations that prefer traditional e-learning structures over hands-on labs” Qwen 3.7 Flash · comparative prompt · soft negative
“Security Journey offers Mid-Cost training, but a Mid-Cost solution might not be the best fit for you.” Muse Glimmer 30B · budget prompt · soft negative
“Start with Secure Code Warrior or Security Journey for a structured enterprise program” GPT-6 Luna · comparative prompt · first choice
“or Security Journey (for budget flexibility)” MiniMax M2.5 · paraphrase prompt · first choice
“a stronger structured option for small organizations, with role-based paths and compliance-oriented reporting, but pricing should be confirmed directly” Perplexity Sonar · budget 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.