Decision instruments

Purpose-built tools that turn a workload into a shortlist, then pressure-test it against quality, cost, latency, context, and provider reality.
Choose
LLM Selector
Stress-test
Agentic Fit Finder
Deploy
Provider Finder
Workload routing

Featured instrument · 01

Agentic Fit Finder

Model agent loops as a system: capability, cost per completed task, response time, context, and deployment constraints in one decision surface.

Route an agent workload

Decision path

From question to production

Use one instrument or follow the whole sequence. Each step narrows a different kind of uncertainty.

Choose · test · deploy
01

Which model should I start with?

Use the LLM Selector to turn your workload into a practical shortlist.

02

Will it survive agent loops?

Stress-test cost per task, autonomy, response time, and context in Agentic Fit Finder.

03

Where should I run it?

Compare provider pricing and latency before the architecture hardens.

Quick answers

How do I compare LLM providers?

Use Provider Finder to compare LLM providers by cost per million tokens, tokens per second, and time to first token (TTFT).

What’s the cheapest provider for GPT / Claude / Gemini?

Pick the model on /compare/providers and sort by Cheapest. If you want more context and variants, open the same model in Explore.

Which model should I pick for my use case?

Start with the LLM Selector to get a ranked shortlist, then validate tradeoffs on Compare.

Why do price and speed differ across providers?

Providers can run different hardware, inference stacks, and rate limits. That’s why the same model can have very different pricing, latency, and tokens/sec depending on where you run it.