Source page: https://www.o10.io/ai-models

Plain-text reference for reading, copying, and citation. Examples are illustrative; use your own credentials for API requests.

Model data: https://www.o10.io/api/models.json
Content index: https://www.o10.io/llms.txt

# AI model selection: capabilities, constraints and evidence

AI model selection is the process of choosing a model and endpoint that meet a task's quality, feature, latency, and operating requirements. Compare actual application behavior before using price as the deciding factor.

## Key takeaways

### What is ai models?

AI model selection is the process of choosing a model and endpoint that meet a task's quality, feature, latency, and operating requirements. Compare actual application behavior before using price as the deciding factor.

## Filter by required capabilities
Identify the input and output types, tool interface, structured-output requirements, context size, and operating constraints of the task. A model that cannot support a required capability is not a cheaper substitute.
The same model name can appear behind different endpoints with different feature support, quotas, retention terms, and pricing. Keep the provider, endpoint, and version in the comparison record.

## Evaluate on your task distribution
Use examples representative of real inputs and material errors. Test the complete prompt and surrounding application, including retrieval and tools where applicable. Separate development examples from held-out release checks.
Benchmark rankings help identify candidates; they are not a guarantee for a different language, domain, toolset, or error tolerance. Inspect individual failures and avoid hiding rare but expensive mistakes in an average score.

## Compare complete operating cost
Measure cost per accepted result, latency, retries, and manual correction. For self-hosted models, account for utilization, spare capacity, and operations. For an API, verify the endpoint's billing dimensions and eligibility for discounts.
The o10 catalog is a dated reference, not proof of model availability or quality. A missing value means the snapshot lacks that information; it must not be read as a zero price or an unlimited context window.

## Methodology

Cost examples are illustrative. Measure quality and total costs on your own tasks, and check current endpoint terms before implementation.

## Related links

- [AI inference: from model output to production system](https://www.o10.io/ai-inference)
- [AI agents: useful actions, measurable outcomes](https://www.o10.io/ai-agents)
- [LLM routing: choose a model per task with explicit constraints](https://www.o10.io/routing)
- [Inference spend: calculate cost per accepted outcome](https://www.o10.io/inference-spend)

## Source URL

https://www.o10.io/ai-models