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LLM Provider Evaluation Scorecard

Scorecard template10 minNewNeura Dynamics

Compare up to three model providers on task quality, cost, latency, features, data handling, reliability and support, with adjustable weights and a weighted total.

What it helps you do

Provider comparisons are often based on public benchmarks and list prices that do not reflect your task. This scorecard provides a structured starting point for comparing providers on criteria that commonly affect production decisions, using evidence from your own tests.

How to use it

Run the same evaluation set against each candidate first. Then score each criterion from 1 to 5 based on those results and the providers' documentation and terms.

Scorecard

Weights total: 100
CriterionWeight
Task qualityResults on your own evaluation set, not public leaderboards.
Cost per taskToken cost multiplied by typical usage, including retries.
LatencyTime to first token and total time at p50 and p95 for your prompts.
FeaturesContext length, tool calling, structured outputs, multimodal input as your use case needs.
Data handling and privacyRetention, training use, regional hosting, compliance documentation.
Reliability and limitsUptime history, rate limits, quota increases.
Deployment optionsAPI, cloud marketplace, private or self-hosted deployment.
Support and termsSupport channels, SLAs, model deprecation policy.
Provider A3.00
Acceptable with trade-offs
Provider B3.00
Acceptable with trade-offs
Provider C3.00
Acceptable with trade-offs
Scores are weighted averages out of 5. Default weights are a starting point; change them to match your priorities before scoring.
01

Test on your own data

Use 30 or more examples from your real task and score outputs with the same rubric for every provider. Keep prompts identical, apart from any provider-specific formatting.

02

Check the terms early

Data retention, training use and hosting region can rule a provider out regardless of quality. Review these before running detailed tests.

03

Plan for change

Models are updated and retired regularly. Design the integration so switching providers is possible, and re-run the scorecard when a major version changes.

Want to build this?

If you want help setting up a fair comparison on your own data, our Generative AI team can run it with you.

Generative AI Development
Sources & further reading
  1. Define success criteria and build evaluationsAnthropic, Claude Docs · Documentationplatform.claude.com
  2. A practical guide to building agentsOpenAI · 2025 · Guide (PDF)cdn.openai.com
  3. AI Risk Management FrameworkNIST · Frameworknist.gov
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