Text
Text Generation
Text generation covers chat, reasoning, translation, summarization, coding, extraction, classification, and agent workflows.
Who this is for
Customers choosing a language model for business workflows.
Configuration reference
Values to confirm before setup
Common API
OpenAI-compatible Chat Completions
Typical models
Qwen Max / Plus / Flash families subject to region
Typical controls
streaming, tool calls, structured output, thinking mode where supported
Setup flow
Practical steps
- 01Identify the task type.
- 02Select a flagship, balanced, or fast model.
- 03Choose streaming or non-streaming.
- 04Set context and output limits.
- 05Test prompt quality before scaling traffic.
Choosing a text lane
Use stronger models for reasoning, planning, and high-value customer-facing answers. Use faster models for classification, routing, extraction, support drafts, and high-volume internal work.
Common mistakes
Check these before escalating
- A cheaper model can become expensive if it needs repeated retries.
- Long context increases token cost.
- Structured output should be tested before production.
Related guides
OpenAI-Compatible Chat API
Most OpenAI-compatible integrations need only three changes: API key, base URL, and model name. The hard part is choosing the correct plan and endpoint.
Rate Limits and Quota Errors
Rate limits are calculated by account, model, and aggregate API-key usage. A customer quote should include traffic assumptions and an escalation path.
Billing and Pricing Structure
A trustworthy quote separates official model usage, Token Plan subscription, shared quota, payment costs, taxes, and ModelSmarter service fees.