Milvus vs Elasticsearch
Zero of fourteen models named Milvus first on the direct prompt; zero named Elasticsearch. Milvus was named by fourteen of the fourteen models and Elasticsearch by eight and Milvus carries 43 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
MilvusFirst choices, of fourteen modelsElasticsearchAcross every category in the November 2026 Edition, Milvus and Elasticsearch were named in the same answer seventeen times, of the 114 answers naming Milvus and the 86 naming Elasticsearch. In those answers Elasticsearch took the first choice one time and Milvus zero.
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 | | | | |
MilvusEL Elasticsearch first choice named as an alternative argued againstblank: not namedEach cell is one answer, Milvus on the left and Elasticsearch on the right.The direct prompt
Neither was the first choice, one was named
3 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
11 of 14 modelsThe answer made no first choice from these two in this category.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
Milvus2%#5 of 11What 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
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.