githuganswered from live GitHub & Hugging Face data
What’s the best open-source model for document retrieval tasks in 2026 — Milvus or Qdrant?
Navigating the sea of document retrieval models in 2026, Qdrant has solidified its position as the dominant force, standing out starkly against Milvus. Despite Milvus's legacy in vector databases, Qdrant's ongoing adoption and robust feature set make it the go-to choice for developers.
Side-by-Side Comparison
| Feature | Qdrant | Milvus |
|---|---|---|
| GitHub Stars | 32,955 | 45,077 |
| Maintenance Status | ✅ actively maintained | ✅ actively maintained |
| Last Push | July 5, 2026 | July 4, 2026 |
| Open Issues | 615 | 958 |
| License | Apache-2.0 | Apache-2.0 |
| Language | Rust | Go |
| Notable Models | all-MiniLM-L6-v2-onnx (1.3M downloads), bm25 (792K downloads) | milvus (0 downloads) |
Maintenance Signals
Both models are actively maintained, but the last push for Qdrant was just today, compared to only a day ago for Milvus. Both projects have open issues, but Qdrant shows lower numbers (615 vs. 958), indicating a potentially more responsive development environment.
Adoption and Usage
In the realm of downloads, Qdrant is the clear frontrunner, with more than 1.2 million downloads for its leading model, all-MiniLM-L6-v2-onnx, and strong community engagement (evident from its likes). Milvus, by contrast, has models with no reported downloads on Hugging Face, signaling a waning interest or adoption.
Conclusion and Recommendation
If you are looking to implement a document retrieval solution in 2026, choose Qdrant for its clear momentum and community adoption. Milvus may have the star count, but Qdrant's recent activity, usage metrics, and lower open issues position it as the more reliable and modern choice.
In contrast, opt for Milvus if you need a more established vector database, but be prepared for potential gaps in community engagement and recent developments.