Structured model comparison
LLM Evaluation Scorecard: 25 Criteria for Model Selection
An interactive worksheet designed to help enterprise AI buyers and platform leads score and compare large language models (LLMs) across 25 essential criteria. This framework supports bake-offs and licensing decisions with transparent, quantifiable metrics.
Selecting a large language model (LLM) for enterprise adoption requires multi-dimensional evaluation beyond headline accuracy. This interactive scorecard breaks down common selection considerations into 25 criteria spanning performance, integration, policy, and cost factors.
Users can input scores and notes for candidate models in each category. The aggregate scoring and final assessment help prioritize models best aligned with specific business needs and technical environments.
Inputs
Name or version of the model under evaluation (e.g., GPT-4, PaLM 2, Claude 3).
Evaluate model performance on benchmark tasks such as MMLU, Big-Bench, or vendor-reported NLP scores.
Score the model's ability to handle multi-step reasoning, math, and logic challenges.
Enter the token limit for context length. Larger windows improve long-form tasks.
Rate the effectiveness of the model's moderation tools and guardrails.
Assess recency of training data. Higher scores for models trained on data less than 12 months old.
Availability and flexibility of customization workflows (e.g., fine-tuning, adapters, prompt tuning).
Score model availability across on-prem, cloud, hybrid, or edge environments.
Input average response times for typical API requests.
Enter the published price for inference usage; used for total cost scoring.
Rate alignment with enterprise privacy policies (HIPAA, GDPR, CCPA).
Evaluate support responsiveness, SLA terms, and escalation pathways.
Availability of client libraries, integrations with orchestration, MLOps tools.
Capabilities for understanding outputs and tracing model decisions.
Effectiveness across supported languages beyond English.
Frequency of model updates to address drift, capabilities.
Score based on computational and memory demands for deployment.
Robustness of vendor’s ethical AI governance and transparency.
Availability of model weights or training code under permissive licenses.
Active user/developer community, third-party tooling support.
Position on aggregate third-party benchmarks.
Permissiveness and constraints of commercial licensing agreements.
Native integration or suitability for external knowledge integration.
Availability of methods to probe inner workings (attention visualization, etc.).
Result
(performance-accuracy + reasoning-capacity + safety-controls + data-freshness + customization-options + deployment-flexibility + privacy-compliance + vendor-support + ecosystem-integration + explainability-features + multilingual-support + update-frequency + ethical-oversight + open-source-availability + ecosystem-maturity + benchmark-ranking + usage-flexibility + data-augmentation + interpretability-tools) / 19weighted-score / (pricing / 10)Model suitability score
Tip
Adjust weights or scoring criteria according to your organization’s priorities by modifying the input values or recalculating with custom formulas to tailor this scorecard.
Subsequent sections unlock after submit