5-Lens Empirical Quadrants, Pareto Frontier & 17-Facet AI Catalog
Replaces subjective analyst pay-to-play quadrants with reproducible telemetry across LLMs, CPU/Edge SLMs, 3D/Multimodal, Time-Series Foundation Models (Chronos-Bolt, TimesFM 2.0, Moirai-MoE, TimeGPT, PatchTST, Toto), Agent SDKs/Orchestrators, 7-Stage RAG, DSPy, Embeddings, MCP + A2A, Security, Tracing vs Evaluation, Memory Policies vs Vector/SQL Substrates, and Durable Execution.
All 17 Facets (12) agent_development_orchestration ai_security_and_guardrails automation_ipaas_dag_durable embeddings_and_rerankers evaluation_and_regression_testing llm_frontier_cloud llm_open_weights_cloud memory_behavior_policies multimodal_vision_audio_3d observability_tracing_monitoring programmatic_prompt_workflow_optimization protocols_mcp_and_a2a rag_context_pipeline slm_cpu_edge_quantized teleon_team_simulation_provenance timeseries_foundation_models vector_and_transactional_storage
5 Orthogonal Ranking Lenses
- Lens 1 (Empirical 2x2 Quadrant): X = Verified Execution Reliability vs Y = Architectural Composability and Governance Fit.
- Lens 2 (Constraint-Weighted Wave): Custom workload weights plus Tversky hard vetoes (
spdx_license, blast class, CPU-only). - Lens 3 (Exact Pareto Frontier): Non-dominated set across Quality, Latency, Cost, Cliff Retention, and Lock-in Freedom (orange nodes).
- Lens 4 (Jagged-Frontier Cliff Index): Perturbed neighbor retention ratio.
- Lens 5 (Team Marginal Value / WAR): Paired holdout lift net of coordination token tax.
| Component & Publisher | Primary Facet | Quadrant & Frontier | Lens Scores | Cliff & WAR | License |
|---|---|---|---|---|---|
| Meta SAM 2.1 Meta ( model_family_version) |
multimodal_vision_audio_3d |
EMPIRICAL_LEADER PARETO | Exec: 95, Fit: 93, Wave: 93.84 | Cliff: 89%, WAR: +0.27 | Apache-2.0 |
| faster-whisper (CTranslate2 INT8) SYSTRAN ( inference_runtime) |
multimodal_vision_audio_3d |
EMPIRICAL_LEADER PARETO | Exec: 96, Fit: 94, Wave: 93.8 | Cliff: 91%, WAR: +0.26 | MIT |
| Whisper Large v3 Turbo OpenAI ( model_family_version) |
multimodal_vision_audio_3d |
EMPIRICAL_LEADER PARETO | Exec: 96, Fit: 93, Wave: 93.4 | Cliff: 87%, WAR: +0.26 | MIT |
| IBM Docling IBM ( rag_component) |
multimodal_vision_audio_3d |
EMPIRICAL_LEADER | Exec: 95, Fit: 94, Wave: 93.24 | Cliff: 91%, WAR: +0.25 | MIT |
| Kokoro-82M TTS hexgrad ( model_family_version) |
multimodal_vision_audio_3d |
EMPIRICAL_LEADER PARETO | Exec: 97, Fit: 94, Wave: 93.08 | Cliff: 91%, WAR: +0.25 | Apache-2.0 |
| ColPali Visual Document Retriever Illuin / Hugging Face ( embedding_model) |
multimodal_vision_audio_3d |
EMPIRICAL_LEADER | Exec: 92, Fit: 92, Wave: 92.8 | Cliff: 89%, WAR: +0.27 | MIT |
| Qwen2.5-VL-7B-Instruct Alibaba Qwen ( model_family_version) |
multimodal_vision_audio_3d |
EMPIRICAL_LEADER | Exec: 92, Fit: 91, Wave: 91.94 | Cliff: 86%, WAR: +0.26 | Apache-2.0 |
| FLUX.1-schnell Black Forest Labs ( model_family_version) |
multimodal_vision_audio_3d |
EMPIRICAL_LEADER | Exec: 94, Fit: 92, Wave: 91.06 | Cliff: 86%, WAR: +0.24 | Apache-2.0 |
| Microsoft TRELLIS 3D Microsoft ( model_family_version) |
multimodal_vision_audio_3d |
EMPIRICAL_LEADER | Exec: 88, Fit: 90, Wave: 88.95 | Cliff: 82%, WAR: +0.22 | MIT |
| FLUX.1-dev Black Forest Labs ( model_family_version) |
multimodal_vision_audio_3d |
EMPIRICAL_LEADER | Exec: 88, Fit: 82, Wave: 87.39 | Cliff: 84%, WAR: +0.22 | FLUX-NonCommercial |
| Tencent Hunyuan3D-2 Tencent ( model_family_version) |
multimodal_vision_audio_3d |
OPERATIONAL_WORKHORSE | Exec: 86, Fit: 81, Wave: 85.41 | Cliff: 80%, WAR: +0.19 | Hunyuan-Community |
| Stable Diffusion 3.5 Large Stability AI ( model_family_version) |
multimodal_vision_audio_3d |
OPERATIONAL_WORKHORSE | Exec: 87, Fit: 81, Wave: 85.33 | Cliff: 82%, WAR: +0.17 | Stability-Community |