Qdrant vs Milvus
Six of fourteen models named Qdrant first on the direct prompt; zero named Milvus. Both were named by all fourteen models and Qdrant carries 73 labels and Milvus 43, so the shares are not directly comparable.
Named in one category 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 vector databases page.
By framing
QdrantFirst choices, of fourteen modelsMilvus
Across every category in the November 2026 Edition, Qdrant and Milvus were named in the same answer ninety-nine times, of the 215 answers naming Qdrant and the 114 naming Milvus. In those answers Milvus took the first choice two times and Qdrant twenty-five.
Every model, every framing
| 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 | | | | | | |
Qdrant Milvus first choice named as an alternative argued againstblank: not namedEach cell is one answer, Qdrant on the left and Milvus on the right.The direct prompt
Qdrant first, Milvus an alternative
6 of 14 modelsMilvus was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
8 of 14 modelsThe answer put something else first and named one of the two as an alternative.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.
By buyer segment
Qdrant29%#2 of 9Milvus1%#6 of 9What the models said about Qdrant
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five in this category shown.
“reported with a serious flaw in the same round up” Muse Glimmer 30B · negative prompt · soft negative
“Filtering is where Qdrant stands out — the database supports rich JSON-based filters that integrate with vector search efficiently. This makes it ideal for B2B use cases requiring complex filtering.” Claude Haiku 4.5 · direct prompt · first choice
“Qdrant consistently receives the most favorable reviews across testing ... If you need a dedicated vector database, it's often the safer choice.” DeepSeek V4 Flash · negative prompt · first choice
“Most teams should evaluate Qdrant (for performance and flexibility) or Weaviate (for AI-native features) as their primary options” GLM 4.7 FlashX · comparative prompt · first choice
What the models said about Milvus
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Milvus for small or moderate workloads: ... it can require substantially more memory and operational complexity than lighter alternatives” Perplexity Sonar · negative prompt · soft negative
“If you need a more scalable, distributed solution later, but it's overkill for early stages.” GLM 4.7 FlashX · budget prompt · soft negative
“Handles massive scale (10B+ vectors) but higher memory use (up to 2.6GB in benchmarks)” Grok 4.20 · budget prompt · soft negative
“If you have a platform/DevOps team: Evaluate Milvus or Qdrant self-hosted for cost control at scale” Kimi K2 · scale prompt · first choice
“Open-source, battle-tested at scale (used by big tech), and offers managed cloud (Zilliz Cloud).” MiniMax M2.5 · direct prompt · alternative
“Expect a large distributed deployment or need extensive index choices? Evaluate Milvus.” GPT-6 Luna · comparative 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.