Qdrant vs Elasticsearch
Six of fourteen models named Qdrant first on the direct prompt; zero named Elasticsearch. Qdrant was named by fourteen of the fourteen models and Elasticsearch by eight and Qdrant carries 73 labels and Elasticsearch 11, so the shares are not directly comparable.
Named in one category this edition.
Elasticsearch
accepted challengerNamed in eight 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 vector databases page.
By framing
QdrantFirst choices, of fourteen modelsElasticsearchAcross every category in the November 2026 Edition, Qdrant and Elasticsearch were named in the same answer thirty-two times, of the 215 answers naming Qdrant and the 86 naming Elasticsearch. In those answers Elasticsearch took the first choice one time and Qdrant seven.
Every model, every framing
| Model | DirectEL | ParaphraseEL | ComparativeEL | Budget-constrainedEL | Scale-constrainedEL | NegativeEL |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | | | | | ||
| GPT-5.4 mini | | | | | ||
| Gemini 3.5 Flash | | | | | | |
| Perplexity Sonar | | EL | EL | | | |
| Grok 4.20 | | | EL | | | |
| Mistral Small | | | | |||
| DeepSeek V4 Flash | | | | | EL | |
| Llama 4 Maverick | | EL | | |||
| Qwen 3.7 Flash | | | | | | |
| Kimi K2 | | | | | | |
| GLM 4.7 FlashX | | | | | ||
| MiniMax M2.5 | | | | | | |
| GPT-6 Luna | | | | | EL | |
| Muse Glimmer 30B | | | | | | |
QdrantEL Elasticsearch first choice named as an alternative argued againstblank: not namedEach cell is one answer, Qdrant on the left and Elasticsearch on the right.The direct prompt
Qdrant first, Elasticsearch not the choice
6 of 14 modelsElasticsearch 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 9Qdrant34%#1 of 11Qdrant20%#2 of 13What 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 Elasticsearch
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of three in this category shown.
“1. Elasticsearch: Best for combining vector, full-text, and log search.” Llama 4 Maverick · paraphrase prompt · first choice
“Your existing search platform—for example, Elasticsearch—if you already use it and need lexical search, filters, and vector retrieval in one system.” GPT-6 Luna · scale prompt · alternative
“Elasticsearch / OpenSearch | Best if you already run it for search; good hybrid search capabilities” DeepSeek V4 Flash · scale 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.