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RESEARCH REPORT • 2025
From Classical ML to LLMsModel Selection Guide
A comprehensive decision matrix for choosing the right AI model across 18 use cases — from tabular data to multimodal understanding. Navigate XGBoost vs LLMs, when to fine-tune, and production-ready recommendations for 2025.
18 use cases covered
Classical ML vs Deep Learning vs LLMs
Production recommendations
Latest 2025 models included
Model SelectionLLMsDecision Matrix2025 Guide
Complete Model Selection Matrix
| Use Case | Classical ML | Deep Learning | Foundation Models (2024-25) | LLM/Multimodal | Best Choice |
|---|---|---|---|---|---|
| Tabular Classification | XGBoostLightGBMCatBoost | TabNetFT-Transformer | TabPFN-2.5TabR | TabLLMGPT-5.2Claude Opus 4.5Gemini 3 Pro | TabPFN-2.3 for ≤15K samples/30 features. XGBoost/CatBoost for larger datasets |
| Tabular Regression | XGBoostLightGBMCatBoost | Neural Networks | TabPFN-2.5TabR | GPT-5.2Claude Opus 4.5Gemini 3 Pro | XGBoost/LightGBM still dominant. TabPFN-2.5 for small data |
| Time Series Forecasting | ARIMAProphetXGBoost | N-BEATSTemporal CNNs | TisPFN-TS (2025)ChronosTimesFM | GPT-5.2Claude Opus 4.5Gemini 3 Pro | TabPFN-TS for zero-shot. Classical ML + DL ensembles for production |
| Text Classification | Naive BayesSVM + TF-IDF | BERTRoBERTaDeBERTa-v3 | SetFitFlan-T5 | GPT-5.2Claude Opus 4.5Gemini 3 Pro | SetFit for few-shot. DeBERTa-v3 with labeled data. GPT-5 for zero-shot |
| Named Entity Recognition (NER) | CRFBERT-NER | BILSTM-CRFBERT-NER | GLINER (zero-shot entities) | GPT-5.2Claude Opus 4.5Gemini 3 Pro | GLINER for zero-shot. Fine-tuned LLM for complex entities |
| Image Classification | Random Forest on features | ResNetEfficientNetViT | DINOv2EVA-CLIPConvNeXt-v2 | GPT-5.2 (vision)Claude Opus 4.5Gemini 3 Pro | DINOv2 for features. Claude Opus 4.5 for zero-shot + reasoning |
| Object Detection | HOG + SVM | Faster R-CNNYOLOv8 | YOLOv12YOLOv11RT-DETRGrounding DINO | GPT-5.2 (vision)Claude Opus 4.5Gemini 3 Pro | YOLOv12 for speed/accuracy. Grounding DINO for open-vocabulary |
| Semantic Segmentation | N/A | U-NetDeepLab | SAM-2SegFormerMask2Former | GPT-5.2Claude Opus 4.5Gemini 3 Pro | SAM-2.1 revolutionary for zero-shot segmentation |
| Speech Recognition | HMM-GMM | Wav2Vec 2.0 | Whisper Large V3Whisper TurboCanary Qwen 2.5B | GPT-5.2 (audio)Claude Opus 4.5Gemini 3 Pro | Canary Qwen (5.6% WER SOTA). Whisper V3 for multilingual |
| Text Embedding | Word2VecSentence-BERT | E5-mistral-7bBGE-M3Nomic-Embed v1.5Jina v3 | gemini-embedding-002VoyageCohere Embed v3 | OpenAI text-embedding-3Qwen2-Embedding | Open-source: BGE-M3, Qwen2-Embedding. Commercial: Gemini Embedding, Voyage 3 |
| Code Generation | Template-basedCodeBERT | CodeT5 | StarCoder 2DeepSeek-CoderQwen2.5-CoderKimi-Dev-72B | GPT-5.2-CodesClaude Opus 4.5Gemini 3 ProGLM-4.6 | Claude Opus 4.5 (80.9% SWE-bench). Open-source: Qwen3-Coder, DeepSeek-V3.2 |
| Machine Translation | Statistical MT | TransformersmLLM-200 | MADLAD-400MQM-100 | GPT-5.2Claude Opus 4.5Gemini 3 Pro | NLLB-200/MADLAD for coverage. GPT-5.2 for quality |
| Multimodal Understanding | Feature concatenation | CLIPALIGN | LLaVA 1.6CogVLMQwen-VL | Claude Opus 4.5GPT-5.2Gemini 3 Pro | Claude Opus 4.5 best overall. LLaVA 1.6 best open-source |
| Video Understanding | 3D features | C3DI3D | VideoLLaMA2InternVideo2 | Gemini 3 Pro (native video)GPT-5.2Claude Opus 4.5 | Gemini 3 Pro with native long context. InternVideo2 open-source |
| Document Understanding | OCR + rules | LayoutLM-v3Donut | NougatKosmos-2.5 | Claude Opus 4.5GPT-5.2Gemini 3 | Claude Opus 4.5/GPT-5 for complex layouts |
| Recommendation Systems | NCFMatrix FactorizationSVD | RecFormerSASRecBERT4Rec | Deep & Cross Networks | GPT-5.2Claude Opus 4.5Gemini 3 Pro | Classical/DL hybrids in production. GPT-5.2/Claude for explainable recs |
| Graph Tasks | PageRankLabel Propagation | GCNGAT | GraphGPSGPS++GraphFormer-v2 | GPT-5.2Claude Opus 4.5Gemini 3 Pro | Graph Transformers (GraphGPS) SOTA. GNNs for efficiency |
| Anomaly Detection | Isolation ForestLOF | AutoencoderVAE | Deep SVDDNeuTraL AD | GPT-5.2Claude Opus 4.5Gemini 3 Pro | Deep learning for high-dimensional. Classical for interpretability |
Understanding the Matrix
Classical ML
Traditional algorithms like XGBoost, Random Forest. Best for structured data with clear feature engineering.
Deep Learning
Neural networks trained on large datasets. Good for complex patterns but requires significant data.
Foundation Models
Pre-trained models from 2024-25. Often provide best balance of performance and ease of use.
LLM/Multimodal
Large language models with multimodal capabilities. Best for zero-shot and reasoning tasks.
Best Choice
Our production-tested recommendation based on accuracy, cost, and deployment complexity.
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