OpenAI Provider

A useful process is to narrow the catalog to two or three realistic candidates and evaluate them on the same workload.

Start with task difficulty

For difficult reasoning, coding, and end-to-end professional work, start with the stronger frontier models. In the current OpenAI catalog, GPT-6 Astra is positioned for the most demanding work, while GPT-6.1 Sol targets complex tasks at a lower cost.

If your workload is well-scoped and repeated at high volume, a smaller model can be more economical. GPT-6 Luna is positioned for efficient, focused workloads, while earlier Mini and Nano models provide additional lower-cost options.

The key question is not whether a smaller model is "worse." It is whether it is sufficiently reliable for the exact task you need to run thousands or millions of times.

Consider coding and agentic behavior separately from ordinary chat

A model that performs well on a single coding prompt is not automatically the best model for an agent.

Agentic software tasks can require the model to inspect files, maintain state, choose tools, recover from errors, revise a plan, and continue toward a larger goal. That workload should be tested end to end.

Models such as GPT-5.3 Codex are explicitly oriented toward agentic coding, while the newer GPT-6 family is positioned for complex reasoning and coding workflows. The practical choice should be based on repository-level or workflow-level evaluations, not only short code-generation prompts.

Match context size to the real workload

Large context windows are useful when a request needs extensive documentation, long conversations, repository context, retrieved knowledge, logs, or multiple source documents.

But a larger context window is not automatically better. Large prompts can increase cost, add irrelevant information, and make evaluation harder. A good production design should measure how much context the application actually sends and how effectively the model uses it.

EidoStack exposes context consumption alongside token usage so that you can compare models using the same input rather than treating the advertised context window as the only decision criterion.

Compare total request cost, not just the input price

OpenAI API pricing is typically based on token usage, but the effective cost of a workload depends on input tokens, output tokens, repeated or cached context, reasoning behavior, request frequency, context length, processing tier, tool calls, and surrounding application logic.

A model with a low input price can still become expensive if it consistently generates long outputs or requires more retries. A more capable model can sometimes reduce total workflow cost if it completes difficult tasks in fewer attempts.

That is why request-level measurement is more useful than comparing one price column in isolation.

Account for model lifecycle and migration

OpenAI regularly introduces new models and retires older ones. Some applications still depend on older model IDs for compatibility, reproducibility, or controlled migrations.

When evaluating a model for a new production system, consider not only its current behavior but also its lifecycle status and the cost of replacing it later. For long-lived systems, explicit model IDs, regression tests, and repeatable evaluation sets can make migrations much safer.