"We tried a few models and this one felt better" is how most model selection actually happens, and it's not a great methodology. Once you've got a gateway giving you access to multiple models through one endpoint, the actual hard part isn't the integration, it's building a comparison that isn't just vibes.
A methodology that isn't vibes
- Define the task category first. "Chatbot response" and "agent planning step" and "code generation" are different evaluation problems, don't lump them into one test.
- Write 10-20 representative prompts per category. Pull from real usage if you have any, not just hello-world examples.
- Run the identical prompt set against every model you're comparing. Same prompts, same order, logged consistently.
- Score on the dimensions that actually matter for that task, not a generic "quality" score:
| Test type | What to actually measure |
|---|---|
| Basic chat | Relevance, tone, completeness |
| Structured output | JSON/table formatting reliability, not just "looks right" |
| Agent step | Planning quality, tool-output interpretation |
| Code prompt | Correctness (run it), formatting, explanation clarity |
| Long prompt | Instruction retention across the full context, not just the first paragraph |
| Cost | Tokens per completed task, not per call |
| Latency | User-facing response time, including any retry overhead |
| Error handling | Does your app's retry/fallback logic actually trigger correctly |
- Pick per-task, not one model for everything. The model that wins on chat quality isn't necessarily the one you want for structured extraction.
The production checklist that's easy to skip after step 5
Getting a model comparison right and then skipping basic production hygiene is a common failure mode:
- [ ] Keys in environment variables or a secrets manager, never in frontend code
- [ ] Logging model name + latency per request (you'll need this when something's slow and you don't know why)
- [ ] Token usage tracked by prompt category, not just aggregate spend
- [ ] Retries with backoff for transient errors, not naive infinite retry
- [ ] Reviewed the provider's data-handling policy before sending anything sensitive through it
Where a gateway actually helps this process
The value of something like GonkaRouter here isn't "one more model to try", it's that the comparison methodology above becomes cheap to run because you're not standing up N separate SDK integrations just to get the test data. One endpoint, swap the model parameter, run the same prompt set. Current lineup per their docs: MiniMax-M2.7, Kimi-K2.6, GLM-5.2, worth checking gonkarouter.io/models directly since model lists in this category shift often.
from openai import OpenAI
client = OpenAI(api_key="sk-xxxxxx", base_url="https://api.gonkarouter.io/v1")
for model in ["MiniMaxAI/MiniMax-M2.7", "moonshotai/Kimi-K2.6"]:
response = client.chat.completions.create(
model=model,
messages=[{"role": "user", "content": PROMPT}],
)
log_result(model, response)
Being honest about scope: this is a router for a specific, currently small model set, not a universal catalog. If your roadmap needs a much broader model list, that's worth checking against their current docs before committing, not assuming it'll grow to cover you.
What's your actual eval loop look like, do you score outputs manually, or has anyone gotten a decent automated grading step working for this kind of comparison?
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