Colay / Guides
Compare open and closed AI models without mixing up the questions
When choosing between an open-weight model and a proprietary model, compare the actual answers and the deployment you would use. Colay can help you inspect available candidates with the same brief. Keep quality, license permissions and operating requirements separate: none of those can be inferred from the word “open” or a polished answer.
Clarify what open means for the candidate
Record the exact candidate, version and proposed access route. Downloadable weights, a permissive license and a complete open-source AI system are different descriptions. The Open Source Initiative's definition includes requirements concerning data information, code and parameters; weights alone do not establish compliance with that definition.
Read the model's actual license and deployment documentation before making a commercial-use or modification claim. A model accessed through a hosted service is being evaluated under that service's conditions. Its appearance in a catalog does not demonstrate that your intended self-hosted setup has been tested.
Compare responses under visible, consistent conditions
Choose a concrete task that you can grade: transform an approved document into a required structure, answer from supplied material or extract specified fields. Give the available candidates identical inputs through Ask separately. Record their displayed labels, the date and any settings visible to you.
Treat the result as a comparison of the configurations you actually used. Differences in tools, context, system instructions or serving setup can matter. Do not label a result as proof that an entire model family or all open models are superior. If a required candidate is unavailable, leave the comparison incomplete instead of substituting an unlabelled model.
Example: summarizing a policy without inventing an exception
Fictional task: summarize an internal equipment-booking policy. The supplied policy permits bookings up to five working days and says exceptions require approval. It does not define who approves them. A useful answer preserves the limit and marks the approver as unspecified. A friendly answer that names the department manager has added a fact.
Run this case with each candidate, preserve the outputs and check the same fields. Then evaluate deployment separately. A candidate's correct summary says nothing about the resources needed to host it or whether your organization can use its license under the intended terms.
| Track | Evidence to collect | What it does not establish |
|---|---|---|
| Answer quality | Preserves five working days and unknown approver | License or infrastructure suitability |
| License | Actual permissions and restrictions for the intended use | Task accuracy |
| Deployment | Intended hosting route, resources and operational ownership | Quality under every configuration |
| Total operation | Measured usage, maintenance and review effort | A universal cheapest option |
A prompt that makes unsupported detail visible
After checking the initial answers, ask for a comparison of their differences. Supply the original policy and labelled outputs explicitly. A synthesis should explain which wording is supported, not count how many models repeated the same added detail. Keep a correct minority answer visible.
Summarize this supplied policy for employees: [permitted text]. Return allowed actions, limits, exceptions and unresolved details. Attach a supporting excerpt to each substantive statement. Preserve the distinction between working days and calendar days. Do not invent approval roles or procedures. If the policy omits a detail, state that it is unspecified.
Choose a deployment to pilot, not an ideology
Write the conditions under which each candidate makes sense for your product. An organization may value a particular deployment route; another may prioritize a managed service or a required capability. Estimate those tradeoffs from your workload and responsibilities rather than treating access to weights as a zero operating-cost promise.
In Colay, the initial comparison uses your account's credits and limits. That expense is separate from a future hosting or API bill. Take the shortlisted model into a small test of its intended production configuration, including failures and fallback behavior. Keep the original comparison as a record of why it earned that next test.
Questions, answered
Are open weights the same as open source?
Not automatically. Check the actual license and available materials against the definition you are using. OSI's AI definition covers more than downloadable weights.
Does a hosted comparison predict self-hosted performance?
It can identify a candidate worth testing, but it does not validate your serving configuration, hardware, settings or operational behavior.
Can I prove which category is best from a few prompts?
No. You can document which tested candidate met specific requirements under recorded conditions. Broader claims need broader evidence.
Sources and methodology
- Open Source Initiative — The Open Source AI Definition 1.0
Primary definition supporting the distinction between available weights and an open-source AI system. No candidate is classified or legally assessed by this article.
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