The Tool Desk
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Start with the work, not a model ranking
“Coding” or “writing” is too broad to guide a choice on its own. A small, well-defined edit is different from a difficult change that spans a codebase. A quick lookup differs from research that must synthesize many sources. A routine first draft differs from a polished document with strict factual and stylistic constraints.
Describe the real workload, including its complexity, the context the model needs, how often the task occurs, and what happens if the answer is wrong. This helps distinguish work that can use a fast, efficient setting from work that needs more reasoning, source access, or careful review. OpenAI’s model-selection guide recommends starting with the task and experimenting with the same inputs; treat provider guidance as a starting point, not proof that a model will perform well in your environment.
Set a quality bar you can check
Write down acceptance criteria before comparing models. Otherwise, a fluent answer can seem better than a correct, complete, and usable one. Match the criteria to the consequences of the task:
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- Coding: Does the solution pass relevant tests, fit the existing project and conventions, handle edge cases, and remain maintainable?
- Research: Are claims accurate and supported by suitable evidence? Does the response cover the important parts of the question, and can you verify its sources?
- Writing: Does the output preserve facts, follow the brief and constraints, match the intended tone and structure, and require an acceptable amount of editing?
- Customer support: Is the response grounded in approved information, useful and policy-compliant? Does it express uncertainty appropriately, protect privacy, and escalate cases that need a person?
These are practical evaluation criteria, not results from a comparative test. Adjust them to the work you do and decide in advance which failures are unacceptable.
Compare candidates on representative tasks
- Build a small test set from real work. Use examples resembling the tasks, context, and constraints the model will encounter. Include routine cases and, where relevant, difficult or unusual ones.
- Give each candidate the same inputs. Keep instructions and available context consistent so differences are easier to judge.
- Score outputs against your criteria. Check correctness, completeness, reliability, and the time needed to verify, edit, or escalate. For research, verify factual support; for code, run the relevant tests rather than judging only by appearance.
- Repeat or broaden the comparison. Generative models can produce different results from the same prompt, so one impressive response is not enough to establish a reliable choice. OpenAI’s evaluation guidance discusses this variability and the need for evaluation suited to AI systems.
- Re-test when conditions change. Revisit the decision when the model version, task mix, product access, or applicable terms change.
How to choose for each kind of work
Coding
For a constrained fix or small edit, an efficient model and low-effort setting may be sufficient if it meets your tests. Complex changes, broad codebase context, or work with several coordinated steps may justify a stronger model or reasoning setting. OpenAI’s selection guide and Anthropic’s enterprise consumption guide offer vendor recommendations along these lines; they are not independent comparative proof.
Evaluate candidates on repository tasks from your own projects. Check that the result works, follows project conventions, addresses edge cases, and can be maintained—not simply that it produces plausible-looking code.
Research
Choose based on whether the workflow needs current source access, careful verification, synthesis, and citations. Model memory alone is not evidence that an answer is current, and a benchmark rank does not establish that a system can answer your particular research question reliably.
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Use prompts with answers you can check. Assess whether claims are supported by the cited material and whether important evidence or qualifications are missing, rather than rewarding fluent prose by itself.
Writing
Specify the deliverable: a short edit, a routine first draft, or a polished external document. Use the same brief and constraints for each candidate, then compare factual fidelity, tone, structure, instruction-following, and editing time. A lighter model or setting is the sensible default when it meets your quality bar; move up only if the comparison demonstrates a worthwhile gain.
Customer support
Separate routine, high-volume work—such as ticket summaries or first-draft replies—from emotionally sensitive, unusual, policy-sensitive, or high-impact cases. Anthropic identifies summaries and first-draft emails as examples to consider for a lightweight model, but that is vendor guidance, not independent evidence of support quality.
Test responses against approved support material and policies. Include cases where the correct behavior is to acknowledge uncertainty or escalate, and account for privacy requirements and the human effort needed to review outputs.
Weigh the operational trade-offs
A useful comparison covers more than the answer itself. Assess each candidate against these questions:
Rank #4
| Factor | What to check |
|---|---|
| Task quality | Does it meet your acceptance criteria on realistic examples? |
| Reliability | Does it meet them consistently across different examples and repeated runs? |
| Speed | Is its latency suitable for interactive work or asynchronous jobs? |
| Cost | What are expected model or API usage costs at your volume? |
| Human effort | How much review, correction, escalation, and integration work does it require? |
| Data and terms | Where does your data go, and what terms or safeguards apply? |
| Availability | Can you access the exact model version in the intended app or API and your geography? |
Inference speed and API billing are not the whole cost of deploying a model. OpenAI’s GDPval discussion cautions that its speed and cost figures cover inference time and API billing, not human oversight, iteration, or workplace integration. Its occupational evaluation used expert-reviewed tasks and blind comparisons of model and human deliverables against rubrics; the findings apply to that evaluation set and methodology, not automatically to every coding, research, writing, or support job.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Check access and data handling before deployment
Confirm the model version, where it is available, and the terms that govern your intended use. Availability and terms can differ between a consumer product, an API, and regions, and provider lineups change. Check current vendor documentation rather than assuming a model name guarantees access to the same version everywhere.
Also determine whether prompts or other data are sent to an outside provider. OpenAI’s external-model documentation says external model calls pass data to third parties and may be subject to different terms and weaker safety guarantees. Anthropic describes its policies and practices in its Transparency Hub. Review the terms that apply to your specific product and use case before sending sensitive information.
Best Value
Use benchmarks as bounded evidence
Benchmarks can help explain how a model performed on a defined set of tasks, but they do not establish a universal winner. GDPval is one example of task-relevant evaluation, with occupational tasks and rubric-based comparisons. Its results should be read in the context of the tasks, models, and methodology reported on its project page, not generalized to every workplace workload.
For your own decision, representative evaluation on your tasks is more directly relevant than a broad ranking. Combine it with the operational checks above, then retain a fallback and repeat the comparison when your needs or the available models change.
Quick Recap
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