Do these 3 things before closing this tab:
1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsThere is no universally best model setting for accuracy, speed, and cost. Define what a good answer means for your task, shortlist models that support the capabilities you need, then compare them on representative prompts. For OpenAI API models, reasoning effort, sampling controls, and output-token limits affect different parts of a request; none guarantees factual accuracy on its own.
Start by defining success for your task
Before changing parameters, decide what the application must get right. Specify the expected answer, required format, unacceptable errors, and how you will score borderline cases. A customer-support answer, a structured extraction, and a complex analysis have different quality bars, so the same settings may not suit all three.
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Build a small evaluation set from realistic inputs, including difficult cases and common failure modes. Apply the same rubric to each candidate: correctness, completeness, instruction-following, and format compliance. This makes “better” measurable instead of relying on a few appealing examples.
Choose a model that fits the workload
Filter models by required capabilities, such as the input types and task features your application uses. OpenAI’s model catalog offers vendor guidance about starting points for different workloads and cost sensitivities. Treat those descriptions as selection guidance, not independent benchmark results or proof that a model will be best for your prompts.
#1 Best Overall
Check the selected model’s current capabilities and limits before tuning request parameters. Names, availability, supported settings, and limits can change; confirm them in the documentation for the model and endpoint you actually use.
Set reasoning effort to the lowest level that passes
For OpenAI reasoning-capable models that expose a reasoning-effort parameter, start with the lowest supported effort that meets your evaluation rubric. OpenAI says lower reasoning effort can make responses faster and use fewer reasoning tokens; available values and defaults depend on the model. See the reasoning guide for current model-specific details.
Rank #2
Raise effort only if representative tests show a meaningful quality improvement that justifies the additional time and token use. Do not assume that a higher setting improves every task: measure whether it helps with your cases, including whether it reduces errors that matter to your users.
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In the OpenAI API reference, temperature controls randomness: “A higher temperature increases randomness in the outputs.” The same reference describes top_p as an alternative sampling control. These descriptions explain how sampling behaves; they do not establish that lowering temperature makes answers more factually correct. Consult the current Responses API reference for the parameters supported by your endpoint and model.
Rank #3
If repeatability matters, test the behavior you get with the chosen sampling control on the same evaluation inputs. Avoid adjusting temperature and top_p together without a specific reason and an evaluation that can show what changed. A more repeatable answer is not necessarily a more correct one.
Set an output-token limit that permits a complete answer
An output-token limit caps how much the model can generate. Set it high enough for a complete answer in the required format, but do not use a generous limit as a substitute for clear instructions about length. A limit that is too low can truncate useful output; one that is unnecessarily high can allow avoidable generation.
Request fields and limit behavior vary by endpoint and model. Check the relevant model and endpoint documentation rather than assuming one limit or parameter works everywhere. OpenAI’s Responses API reference documents request options for that endpoint.
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Estimate cost using both input and output
API cost depends on the model and the tokens used, including input and generated output. Reasoning-token use may also matter for reasoning-capable models. Compare candidates using representative requests, not just the visible answer length, and check the current OpenAI API pricing for the applicable model and billing unit before estimating spend. Rates and model availability can change, so a price figure without a model, unit, and date can quickly become misleading.
For a useful estimate, record input-token volume, output-token volume, and any model-specific token categories that affect billing across realistic tasks. Then compare that estimate with actual usage from your application; real traffic may differ from a small test set.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare candidates on the same workload
Run each candidate against the same representative prompts and score them using the same rubric. Record quality, latency, input and output token use, and resulting cost. Include supported capabilities and relevant context or output limits as constraints, and test consistency if predictable outputs matter.
- Define the quality bar: write down required content, format, and errors the application cannot accept.
- Shortlist compatible models: use the model catalog to identify candidates, then verify their current capabilities and supported settings.
- Choose a starting configuration: begin with the lowest reasoning effort that appears adequate, a sampling configuration appropriate to the task, and an output limit large enough for a complete response.
- Run a controlled evaluation: use the same prompts and scoring rules for every candidate; change one setting at a time when testing its effect.
- Measure the trade-offs: compare quality, latency, and token use and cost on the intended workload.
- Select against your priorities: keep the configuration that meets the quality bar with an acceptable speed and cost for your application.
This process is a practical evaluation method, not a claim that any particular model or setting wins universally. OpenAI’s public catalog and API documentation describe models and parameters, but they do not establish a workload-independent optimal configuration or a controlled cross-provider comparison.
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