How to Choose the Right AI Model?

Choose AI models by task, budget, and measured results, with current scenario recommendations, integration checks, and small-scale testing.

Short Answer

First confirm that a model can handle your task, then compare quality, response time, and actual cost. The newest or most expensive model is not always the right choice, and unit price alone is not enough.

See Model Pricing for model IDs and current prices, and Model Information Overview for capabilities and integration guidance.

Which Models Should You Evaluate?

These candidates are based on the APIYI model catalog as of October 14, 2026 (UTC+8). They are starting points for evaluation, not a fixed ranking or the only options for each task.

Your task	Models to evaluate first	What to compare
Batch classification, information extraction, short summaries	gpt-6-luna, claude-haiku-5-5	Accuracy, output format consistency, actual cost per task
Everyday conversations, copywriting, content organization	gemini-3.8-flash, gpt-6.1-sol	Writing style, instruction following, response time
Coding, code review, tool calling	gpt-6.1-sol, claude-sonnet-5-5, deepseek-v4.1-flash	Completion rate on real coding tasks, tool calling, client compatibility
Complex analysis, multi-step agent tasks	claude-opus-5-5, gpt-6-astra	Multi-step execution quality and whether the additional latency and cost are worthwhile
Image generation, reference image editing	gemini-nano-banana-2.1, gpt-image-2.5-flare	Reference fidelity, text rendering, resolution, generation success rate
Transparent backgrounds, masked inpainting, precise editing	Official API routes for gpt-image-2.5-sunburst, gpt-image-2.5-flare	Parameter control, editing precision, output format, generation time

Four Checks Before Integration

• Input and output capabilities: Image input support does not imply image generation. A long context window does not imply an equally long output limit.
• Endpoints and parameters: Confirm support for your client’s API format, tool calling format, and parameters. Switching models may require more than changing model.
• Token permissions: Confirm that your token includes the required groups and is not restricted by its model allowlist or quota. See Tokens and Groups.
• Actual cost: Text tasks may incur input, output, reasoning token, and long-context charges. Image tasks also depend on resolution, quality, reference images, and retries. Refer to Model Pricing and actual call logs.

How Do You Choose Between Candidates?

Select a small set of real business tasks, such as 20-50 examples, including both routine requests and cases that are likely to fail.

1. Compare two or three candidates using the same inputs and clear output requirements.
2. Record correctness, format compliance, response time, failures, retries, and actual cost.
3. Among candidates that meet your quality requirements, choose the better balance of speed and total cost. More demanding tasks can be routed to a more capable model separately.

A stable production workflow does not need to switch immediately whenever a new model appears. Validate on a small scale, migrate gradually, and keep a rollback option.

Example: Generating Product Titles from Images

This task requires understanding an image and producing text. Choose a text model that supports image input, not an image generation model.

Start by evaluating gpt-6-luna or claude-haiku-5-5 on ordinary product images. Check whether product categories, colors, and materials are described accurately. If more complex images need better recognition or descriptions, compare gemini-3.8-flash or gpt-6.1-sol before deciding to upgrade.

For existing product text, such as extracting attributes, organizing descriptions, or generating titles in batches, you can also include deepseek-v4.1-flash in the comparison. Text-only test results are not evidence of image understanding capability.

Frequently Asked Questions

Is the newest model always a better fit?

No. A new model may change endpoints, parameters, response structure, or default reasoning behavior. Validate it with your own business examples.

Does a lower unit price always mean lower cost?

No. Output length, reasoning tokens, retries, and manual corrections all affect total cost. Compare how much it actually costs to complete one acceptable result.

Can I use just one model?

Yes. For a focused use case, start with one model that meets your requirements. If task difficulty varies widely, consider routing by task rather than sending every request to the most expensive model.

Scenario-Specific Consultation

If you have specific application scenarios and are unsure which model to choose, feel free to contact us for professional advice:

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Please provide the following information when consulting:

• 🎯 Application Scenario: Specific use case (e.g., batch image title generation, customer service dialogue)
• 📊 Usage Scale: Expected daily call volume
• ⚡ Performance Requirements: Response speed, quality expectations
• 💰 Budget Range: Cost control targets

This information helps us recommend the most suitable model combination for you.

Model Update Notes

Stay Informed

Model availability, prices, and API capabilities may change. We recommend:

• Regularly check the Model Information page and Model Pricing
• Follow the Changelog for new models and API changes
• Validate new models on a small set of real tasks, then migrate gradually after checking results, cost, and client compatibility