The first step in model selection is not to compare the rankings, but to confirm what inputs and outputs are required for the task, and whether erroneous results can be automatically discovered. The request format, latency, and cost of different types of models cannot be compared within the same set of criteria.
Check before you start
Answer five questions first:
- Is the input text, image, audio, or file?
- Does the output want text, structured data, images or videos?
- How long can users wait?
- Can the results be verified by rules or manually?
- What is the acceptable cost for a single task?
Configuration steps
text model
Suitable for dialogue, summarization, translation, code generation, classification and extraction. When choosing, pay attention to:
- Context window.
- Tool calling capabilities.
- Structured output stability.
- Output speed versus tokens cost.
Prioritize testing of low-cost models for classification and extraction, and then upgrade capabilities for complex coding and long-term agent tasks.
image model
Suitable for literary drawings, picture editing, product pictures and promotional materials. Fixed when testing:
- Prompt word.
- Size and aspect ratio.
- Reference picture.
- Output quality level.
You can't just compare the "best looking picture", you should compare the consistency of multiple generations and the number of reworks.
For the complete access and acceptance process, seeImage generation API integration guide。
video model
Video costs and wait times are typically higher. First use low duration and low resolution to verify the composition and movement, and then generate the official version. Focus on recording failure rates, queue times, character consistency, and whether audio is included.
Asynchronous tasks, polling and result saving seeVideo generation API access tutorial。
Retrieve model
Retrieval models or network search capabilities are suitable for time-sensitive information and source tracking. Need to evaluate simultaneously:
- Returns whether the source is accessible.
- Whether the citation supports the conclusion.
- Is the search time range correct?
- Is it stable to query the same issue repeatedly?
FAQ
Should the most expensive model be used for all tasks?
It shouldn't be. Stratify tasks by complexity and failure cost: use low-cost models for tasks that are simple and verifiable, use more capable models for tasks that are complex and costly to fail.
The model names are similar, can they be replaced directly?
Full compatibility cannot be assumed. Even within the same family, context, output caps, tool calls, and parameter support may differ. Use fixed samples for regression testing.
How to assess true costs
Don’t just look at unit price per million tokens. Also record the number of retries, output length, manual correction time, and failed task costs.
Next step
Establish a set of fixed samples covering real failure cases, record completion rate, delay, tokens, manual correction time and total cost of a single task respectively, and then decide the production route.