182 models ranked for ML engineering. Scored with bonuses for reasoning (architecture decisions), large context (reading full codebases), large output (complete implementations), JSON mode, and function calling.
| # | Model | Score |
|---|---|---|
| 1 | Claude Opus 4.7Anthropic | 95 |
| 2 | GPT-5.5OpenAI | 93 |
| 3 | Gemini 3.1 Pro Preview Custom ToolsGoogle | 92 |
| 4 | Gemini 3.1 Pro PreviewGoogle | 92 |
| 5 | GPT-5.4 ProOpenAI | 92 |
| 6 | GPT-5.4OpenAI | 92 |
| 7 | GPT-5.5 ProOpenAI | 91 |
| 8 | GPT-5.2 ProOpenAI | 91 |
| 9 | Claude Opus 4.6 (Fast)Anthropic | 90 |
| 10 | Claude Opus 4.6Anthropic | 90 |
| 11 | GPT-5.2-CodexOpenAI | 90 |
| 12 | GPT-5.2OpenAI | 90 |
| 13 | GPT-5.3-CodexOpenAI | 89 |
| 14 | GPT-5 ProOpenAI | 89 |
| 15 | Gemini 3 Flash PreviewGoogle | 88 |
| 16 | GPT-5.1-Codex-MaxOpenAI | 88 |
| 17 | GPT-5 CodexOpenAI | 88 |
| 18 | GPT-5OpenAI | 88 |
| 19 | GPT-5.1OpenAI | 87 |
| 20 | GPT-5.1-CodexOpenAI | 87 |
| 21 | GPT-5.1-Codex-MiniOpenAI | 87 |
| 22 | DeepSeek V4 ProDeepSeek | 87 |
| 23 | o3 Deep ResearchOpenAI | 87 |
| 24 | o3 ProOpenAI | 87 |
| 25 | o3OpenAI | 87 |
| 26 | Claude Sonnet 4.6Anthropic | 85 |
| 27 | Claude Opus 4.5Anthropic | 85 |
| 28 | Grok 4.20xAI | 89 |
| 29 | Gemini 2.5 ProGoogle | 84 |
| 30 | Gemini 2.5 Pro Preview 06-05Google | 84 |
Design neural network architectures, select hyperparameters, and choose training strategies. Reasoning models analyze trade-offs between model complexity and performance.
Generate PyTorch, TensorFlow, and scikit-learn code for training pipelines, data loaders, custom layers, and evaluation scripts. Large output produces complete implementations.
Analyze experiment results, suggest next steps, and document findings. JSON mode structures experiment metadata for tools like MLflow, W&B, and Neptune.
Create model serving configs, write Docker/Kubernetes manifests for inference, and build monitoring dashboards. Function calling integrates with deployment APIs.
Yes, models generate PyTorch, TensorFlow, and scikit-learn code. Reasoning helps with hyperparameter selection, architecture design, and debugging convergence issues. They analyze training curves, suggest data augmentation strategies, and write evaluation metrics.
AI models complement MLOps tools. They write the code that runs on platforms like MLflow, Kubeflow, and SageMaker. Use AI for experiment design, model selection, and code generation, then deploy through your MLOps infrastructure.
Reasoning models identify useful features from raw data descriptions, suggest transformations, and generate preprocessing code. They understand statistical concepts (normalization, encoding, imputation) and suggest appropriate techniques for different data types and ML tasks.
Models with large context windows can process entire research papers and generate implementation code. Reasoning helps understand novel architectures and loss functions. Web search accesses the latest papers on arXiv. Models scoring highest here consistently reproduce research results.