# Open-model routing and evaluation

> Compare open and hosted AI models on representative tasks, then route work by quality, latency, cost, and safe fallback rules.

- Canonical: https://gptnavi.com/workflows/open-model-routing-and-evaluation
- Category: Coding
- Difficulty: Advanced
- Setup time: 4 hours
- Estimated time saved: 3-8 hours
- Last materially updated: 2026-08-19
- Best for: AI product teams, Developers, Technical founders
- Tools: Hugging Face, Replicate, Together AI, GroqCloud, LangSmith

## Quick answer

Model choice is an operations decision, not a leaderboard decision. Use a fixed task set and define which quality or safety failure must trigger a fallback or human review.

## When to use it

- AI assistant
- Extraction workflow
- Media generation feature
- Cost-sensitive internal tool

## Steps

1. **Choose candidate capabilities** — Select models that meet your license, data, modality, and deployment needs; document why each is eligible. Tool: Hugging Face. Expected output: A candidate model list.
2. **Run a real task set** — Test representative examples through an API and preserve inputs, outputs, latency, cost, and failure cases. Tool: Replicate. Expected output: Comparable model runs.
3. **Test a production route** — Compare quality and operational behavior for the most promising models using the route you would actually ship. Tool: Together AI. Expected output: A routing shortlist.
4. **Check latency-sensitive cases** — Run the interactive or voice-critical test set at realistic traffic assumptions and record the user-visible response time. Tool: GroqCloud. Expected output: A latency baseline.
5. **Approve a route and fallback** — Score each candidate against the rubric and define the default, fallback, alert threshold, and human escalation path. Tool: LangSmith. Expected output: A versioned routing decision.

## Prompt templates

### Model selection rubric

Create a model-selection rubric for this AI feature. Score task quality, safety, privacy, controllability, latency, cost, licensing, operational maturity, fallback behavior, and user correction burden. Feature: [paste]

### Routing failure review

Analyze these failed model runs. Separate prompt, retrieval, model, latency, quota, tool-use, and UX causes. Recommend a default route, fallback rule, human escalation trigger, and regression cases. Runs: [paste]

## Common mistakes

- Choosing from benchmark scores alone
- Ignoring licensing and data-handling requirements
- Failing open when a model or provider is unavailable

## Related workflows

- https://gptnavi.com/workflows/llm-evaluation-before-production
- https://gptnavi.com/workflows/ai-product-release-control-plane
- https://gptnavi.com/workflows/api-to-ai-operations-workflow