Executive Summary
Scout dominates in breadth-based RAG (massive knowledge bases, multi-source retrieval); Maverick dominates in depth-based RAG (complex reasoning, bounded contexts). Both use mixture-of-experts, activate 17B params, but differ in expert count (16 vs. 128) and context window (10M vs. 1M).
1. Context Window Capability
Scout (10M tokens)
- Processes ~7M words in a single pass
- Eliminates chunking bottlenecks: retrieve 1000+ documents, synthesize without truncation
- Ideal for: legal discovery, research synthesis, massive FAQ bases
Maverick (1M tokens)
- Processes ~670K words in a single pass
- Still 10x larger than Llama 3.1 405B (128K tokens)
- Ideal for: detailed reasoning on focused documents, complex multi-step analysis
Verdict: ✅ Scout wins for knowledge-heavy retrieval; ✅ Maverick wins for reasoning-heavy tasks; ⚠️ Use Scout when Milvus returns 500+ relevant chunks.
2. Expert Architecture & Routing
Scout: 16 Experts
- Broad generalist experts, each handling diverse token types
- Fast routing decision (smaller gating network)
- Better for heterogeneous retrieval (contracts + emails + PDFs mixed)
Maverick: 128 Experts
- Specialized experts (math, language, reasoning, etc.)
- Slower routing decision but more precise expert selection
- Better for homogeneous, complex domains (all code, all papers)
Verdict: 🟢 Scout for diverse document types; 🔷 Maverick for single-domain depth; ✅ Both equally fast at inference despite routing difference.
3. Retrieval Integration with Milvus
| Aspect | Scout | Maverick |
|---|---|---|
| Retrieve volume | 500–5000 chunks | 50–200 chunks |
| Milvus filtering | Light (semantic only) | Heavy (semantic + metadata) |
| Hallucination risk | Lower (all context in-window) | Moderate (context bounded) |
| Processing speed | Fast (sparse routing) | Fast (sparse routing) |
| GPU memory | Same (17B active) | Same (17B active) |
4. Cost & Infrastructure
Both open-weights, self-hosted costs are identical:
- 1x GPU (A100 80GB or equivalent)
- No API fees regardless of context length
- Quantization reduces memory equally
Latency: Scout slower on 10M-token inputs (~5-10s), Maverick faster on 1M-token inputs (~1-2s). Choose by your SLA, not by parameter count.
5. Fine-Tuning & Domain Adaptation
Scout: Fine-tune on domain corpora with 10M context to teach domain-specific synthesis.
Maverick: Fine-tune for expert specialization on niche data (e.g., medical or legal reasoning).
Verdict: ✅ Both fine-tune equally; choose based on your domain’s breadth (Scout) or depth (Maverick).
6. Enterprise RAG Trends (April 2026)
Scout adoption is surging for:
- E-discovery and legal document review
- Research synthesis and literature reviews
- Customer support with massive knowledge bases
- Code understanding from entire repositories
Maverick adoption is steady for:
- Financial analysis and risk assessment
- Medical literature interpretation
- Complex code refactoring with full context
7. Decision Matrix
Choose Scout if:
- Milvus typically returns 500+ relevant documents
- Knowledge base is diverse (many document types)
- Hallucination from truncation is a risk
- You prioritize comprehensive over precise reasoning
Choose Maverick if:
- Milvus retrieves 50–200 targeted documents
- Domain is narrow (single type of content)
- Reasoning quality and expert specialization matter
- Latency is a strict constraint (<3 seconds)
Related Resources
- Milvus Quickstart — benchmark both models on your data
- Agentic RAG with Milvus and LangGraph — adaptive model selection in agentic loops
- Enhance RAG Performance — retrieval strategies for each model