Five of fourteen models named Azure Machine Learning first on the direct prompt; two named Google Vertex AI. Both were named by all fourteen models and Azure Machine Learning carries 49 labels and Google Vertex AI 57, so the shares are not directly comparable.
Named in two categories this edition.
Named in three 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 ML platforms page.
Across every category in the October 2026 Edition, Azure Machine Learning and Google Vertex AI were named in the same answer 125 times, of the 159 answers naming Azure Machine Learning and the 173 naming Google Vertex AI. In those answers Google Vertex AI took the first choice fourteen times and Azure Machine Learning fifteen.
| 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.
“models suggesting caution or avoidance unless you have the necessary expertise and budget” Mistral Small · negative prompt · hard negative
“challenges such as data privacy, vendor lock-in, and cost unpredictability remain persistent concerns” Muse Glimmer 30B · negative prompt · soft negative
“Enterprise Cloud Platforms for Small Teams/Startups (e.g., SageMaker, Vertex AI, Azure ML)” Grok 4.1 Fast · negative prompt · soft negative
“Small team, tight budget, general ML: Azure Machine Learning or SageMaker AI are solid picks.” GPT-5.4 mini · budget prompt · first choice
“If you want a default pick and you're already Microsoft-centric, I'd choose Azure Machine Learning.” GPT-6 Luna · paraphrase prompt · first choice
“Azure Machine Learning and Google Vertex AI are the most broadly suitable platform choices” Perplexity Sonar · 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.
“it is often recommended to avoid for small teams or startups due to complexity and cost concerns” Mistral Small · negative prompt · hard negative
“Some AI models advise against using this service for enterprise tasks until stability improves.” Llama 4 Maverick · negative prompt · hard negative
“be cautious if you are not already on Google Cloud, have a limited budget, or need a small-scale/simple setup” Perplexity Sonar · negative prompt · soft negative
“the most recommended MLOps platforms for training and deploying models are Vertex AI and Amazon SageMaker” Mistral Small · paraphrase prompt · first choice
“Azure Machine Learning and Google Vertex AI are the most broadly suitable platform choices” Perplexity Sonar · direct prompt · first choice
“I'd usually recommend Amazon SageMaker or Google Vertex AI as the best default choices” GPT-5.4 mini · 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.