Choose an LLM by testing it on the work you actually need done—not by assuming one model is best at everything. Set a quality bar, run the same representative tasks across candidates, compare quality with speed and total cost, and keep the least costly option that reliably passes. Repeat the comparison when models, settings, availability, or prices change.
Start with the job, not a model ranking
“Coding,” “research,” and “writing” each cover tasks with very different demands. A small code edit is not the same as debugging a complex system; drafting from a clear brief is not the same as synthesizing a large set of sources. Before comparing models, define the work and the conditions under which you will use one.
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- Task type: Is it a routine one-step request, or a multi-step task involving ambiguity, planning, or difficult judgment?
- Inputs: Will the model need a large context, images or other modalities, or access to tools?
- Use route: Do you need the model in a particular product, or through an API for an application or automated workflow?
- Success criteria: What must be correct, useful, or acceptable for you to use the output?
Model capabilities, tools, settings, and availability vary by model and product. Check the current documentation for each candidate rather than assuming a feature or control is available throughout a provider’s catalog. OpenAI’s model-selection guide and Anthropic’s model-selection guide offer provider-authored starting points, not independent rankings.
Build a small, representative evaluation
Before trying candidates, write down what a passing answer looks like. Use prompts and data drawn from your real workflow, and include at least a few difficult or unusual cases. Anthropic’s Claude Platform documentation recommends creating tests specific to the use case and calls a good evaluation set the most important step in the process.
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- Select representative tasks. Choose examples you expect to encounter, not only polished demonstrations or easy prompts.
- Define pass criteria. Decide what counts as correct and useful, what errors are unacceptable, and how much editing or review is reasonable.
- Run identical inputs. Give each candidate the same prompt and relevant data. Record the model and settings used so the comparison stays interpretable.
- Score the outputs. Compare task accuracy, response quality, edge-case handling, latency, cost, and the amount of human correction required.
- Repeat where results are close. A single prompt may not reveal whether a model performs consistently across the work you care about.
Adapt the test to your workflow
For coding
Use tasks from the actual project: implementation, debugging, refactoring, or tool-assisted work if that is part of your workflow. Check correctness and edge cases, then run the project’s normal tests and other checks before accepting generated code. A plausible-looking answer is not evidence that the change works.
For research
Test with the kinds of questions and source material you actually use. Judge whether the answer addresses the question, represents evidence accurately, and provides useful analysis. For source-sensitive work, inspect the cited evidence directly; fluent prose does not establish factual accuracy.
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For writing
Use a real brief and audience. Check whether the output preserves required facts, fits the format and voice, and needs an acceptable amount of editing. There is no source-supported universal writing-quality ranking that can replace this test.
Compare trade-offs, not just answer quality
A candidate that produces the strongest answer may not be the best fit if it is too slow or costly for repeated use. Compare the factors that matter to your workflow together.
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| Factor | What to check |
|---|---|
| Capability and quality | Does it meet your pass criteria on routine tasks and difficult cases? |
| Speed | Is the response time acceptable for interactive work or batch processing? |
| Total cost | Estimate cost for your expected workload and frequency, not just a headline rate. Repeated automation can make small per-task costs add up; check current provider pricing separately. |
| Reasoning controls | Which effort settings are supported, what is the default, and do alternative settings measurably help your tasks? |
| Features and availability | Confirm required tools, context, modalities, and access through the specific product or API you plan to use. |
| Operational fit | For repeated tasks, measure consistency and the human review or correction needed to reach an acceptable result. |
Tune settings before paying for more capability
Where a model supports reasoning or effort controls, test an appropriate setting before switching models. Higher effort can trade additional latency and cost for stronger reasoning, but it should not be assumed to improve every task. OpenAI’s reasoning-model guide associates medium effort with work involving planning, complex reasoning, and judgment, and advises evaluating medium and high effort for complex workflows when appropriate. Supported values depend on the specific model, so verify its documentation and compare settings on the same evaluation tasks.
OpenAI recommends comparing models on identical inputs and retaining the lightest setting that meets the quality bar. Anthropic’s guidance describes both efficiency-first and capability-first starting points, followed by evaluation and optimization. Those recommendations can help structure a trial, but they are vendor guidance rather than proof that a particular named model is best for your work.
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Make the decision—and revisit it
Choose the least costly model and setting that passes your evaluation with acceptable speed and review effort. If no candidate passes, clarify the task, adjust the workflow or settings, or test a more capable option. For repeated work, you can also test a two-tier approach: use a lower-cost model for routine cases and reserve a stronger model for selected difficult decisions. Measure the added routing and review complexity alongside any savings.
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Quick Recap
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