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Recommendation

Deploy hybrid_rerank_default (profile balanced, run all_solutions_multihop). Its config is in winner.yaml.

Why

  • hybrid_rerank_default is recommended under the balanced profile (quality first, then the best mix of cost and speed among systems that are equally good): answer score 3.73, $0.00109 per question, p95 latency 3826 ms.
  • hybrid_rerank_default ties with compression_llm_extract and llm_heavy_default on answer score: they are statistically indistinguishable from the best (llm_heavy_default) by a paired bootstrap on the same questions (Holm-adjusted p ≥ 0.05). Quality does not separate them, so it wins on cost and latency ($0.00109 per question against up to $0.00983; p95 latency 3826 ms against up to 8092 ms).
  • hybrid_rerank_default vs llm_heavy_default (the best answer score): -0.11 answer score (95% CI -0.22 to -0.00, significant), 0.1× cost
  • hybrid_rerank_default vs no_retrieval_floor (the cheapest feasible system): +1.82 answer score (95% CI +1.67 to +1.97, significant), 5.2× cost

Ranking

The winner first, then the systems tied with it (best choice first), then the rest by weighted score. Score is the weighted, min-max normalized mix of quality, cost and latency across the feasible systems; tie marks quality that is statistically indistinguishable from the best; Pareto marks systems no other feasible system beats on quality, cost and latency at once.

# System Score Answer score $/Q p95 Ingestion Calls/Q Tie Pareto
1 hybrid_rerank_default 0.91 3.73 $0.00109 3826 ms $0.03301 1.0 tie ✓
2 compression_llm_extract 0.84 3.72 $0.00112 7614 ms $0.03341 2.0 tie
3 llm_heavy_default 0.69 3.83 $0.00983 8092 ms $0.03301 2.0 tie ✓
4 bm25_default 0.91 3.62 $0.00104 2298 ms $0.00000 1.0 ✓
5 rerank_default 0.91 3.65 $0.00108 2784 ms $0.03301 1.0 ✓
6 contextual_default 0.89 3.60 $0.00104 2771 ms $0.97876 1.0 ✓
7 vector_default 0.86 3.48 $0.00106 2105 ms $0.03301 1.0 ✓
8 adaptive_default 0.86 3.57 $0.00088 4143 ms $0.10022 1.5 ✓
9 hierarchical_default 0.86 3.45 $0.00076 2309 ms $0.14719 1.0 ✓
10 vector_mmr 0.85 3.44 $0.00102 2519 ms $0.03301 1.0 ✓
11 decision_gate_default 0.85 3.55 $0.00181 3611 ms $0.03301 2.6 ✓
12 sentence_window_default 0.84 3.44 $0.00051 3254 ms $0.02827 1.0 ✓
13 late_interaction_rerank 0.84 3.41 $0.00076 2546 ms $0.03341 1.0
14 decompose_default 0.84 3.61 $0.00087 6084 ms $0.03341 2.0 ✓
15 raptor_collapsed 0.83 3.36 $0.00076 2245 ms $0.48246 1.0 ✓
16 rag_fusion_default 0.83 3.58 $0.00085 6168 ms $0.03341 2.0 ✓
17 parent_doc_default 0.82 3.40 $0.00123 2905 ms $0.03802 1.0
18 hyde_default 0.82 3.44 $0.00111 4126 ms $0.03301 2.0
19 graph_rag_local 0.79 3.26 $0.00092 2431 ms $2.01740 1.0
20 corrective_default 0.79 3.68 $0.00100 9877 ms $0.03341 2.8 ✓
21 hybrid_default 0.77 3.25 $0.00106 3255 ms $0.03301 1.0
22 full_context_50k 0.72 3.41 $0.00581 3329 ms $0.00000 1.0
23 iterative_default 0.72 3.52 $0.00123 10908 ms $0.03341 3.9
24 agent_search_tools 0.66 3.53 $0.00259 13071 ms $0.03341 5.5
25 agent_search_plain 0.65 3.35 $0.00226 10607 ms $0.03341 5.3
26 grep_agent_default 0.65 3.30 $0.00309 8792 ms $0.00000 6.8
27 decision_gate_strict 0.58 2.60 $0.00154 2374 ms $0.03301 2.1 ✓
28 langchain_default 0.57 2.85 $0.00088 8353 ms $0.00000 1.0
29 llamaindex_default 0.51 2.60 $0.00176 5798 ms $0.00000 3.6
30 haystack_default 0.47 2.71 $0.00115 10978 ms $0.00000 1.0
31 graph_rag_global 0.41 2.04 $0.00061 3087 ms $1.96643 2.0
32 no_retrieval_floor 0.40 1.90 $0.00021 1754 ms $0.00000 1.0 ✓

Winner by category

The best mean quality in each question category among the feasible systems. This is descriptive: categories hold few questions, so a different winner here is a lead worth checking, not a significant result.

Category Winner hybrid_rerank_default compression_llm_extract llm_heavy_default bm25_default rerank_default contextual_default vector_default adaptive_default hierarchical_default vector_mmr decision_gate_default sentence_window_default late_interaction_rerank decompose_default raptor_collapsed rag_fusion_default parent_doc_default hyde_default graph_rag_local corrective_default hybrid_default full_context_50k iterative_default agent_search_tools agent_search_plain grep_agent_default decision_gate_strict langchain_default llamaindex_default haystack_default graph_rag_global no_retrieval_floor
comparison_query compression_llm_extract 3.57 3.69 3.64 3.50 3.45 3.55 3.36 3.48 3.23 3.25 3.10 3.26 3.34 3.52 3.31 3.48 3.41 3.39 3.16 3.37 3.19 3.10 3.28 3.29 3.05 2.92 1.83 3.01 2.28 2.90 1.51 1.12
inference_query compression_llm_extract 3.97 4.15 4.08 3.89 3.95 3.84 3.71 3.96 3.87 3.64 3.98 3.80 3.55 3.91 3.63 3.88 3.60 3.70 3.50 4.08 3.46 3.77 3.76 3.78 3.74 3.52 3.05 3.29 3.42 3.42 2.86 2.68
null_query decision_gate_strict 4.38 3.91 4.51 4.36 4.31 4.35 4.32 4.08 4.34 4.57 4.70 4.50 4.40 3.76 4.28 4.36 4.39 4.29 4.41 4.28 4.37 4.37 4.42 4.38 4.31 4.67 4.84 1.73 2.22 0.59 2.76 3.23
temporal_query llm_heavy_default 3.28 3.06 3.42 3.05 3.18 2.92 2.88 2.88 2.72 2.85 3.01 2.64 2.79 3.23 2.60 2.92 2.58 2.69 2.48 3.27 2.45 2.87 3.06 3.08 2.76 2.82 1.92 2.62 2.11 2.56 1.29 1.27

How this was chosen

  • Quality is the mean answer score over 300 questions. Weights: quality 0.6, cost 0.2, latency 0.2.
  • Constraints: none.
  • Systems whose quality is not significantly worse than the best (paired bootstrap, Holm-adjusted) are tied; the tie is broken on cost and latency by the weights, then on the fewest model calls. See docs/methodology.md#selection.