兔子API
use-case
General text, chat, and reasoning model guide
Direct answerGeneral text models cover Q&A, writing, extraction, and analysis, while reasoning models fit multi-step problems; production selection should balance task accuracy, latency, cost, and control.
Updated · Reviewed
Separate task depth first
Summarization, rewriting, classification, and simple Q&A usually prioritize speed, price, and stable formatting. Mathematics, planning, complex analysis, and constrained decisions depend more on reasoning. Do not send every request to the most expensive model; route by difficulty and keep a fallback.
Selection dimensions
Compare accuracy, instruction following, structured-output success, context, time to first token, and total cost on real inputs. Work involving the web, files, or business systems also requires tool reliability, permission boundaries, and recovery; model knowledge does not replace a current data source.
Minimal call
This site uses a Bearer token. Select the model name from the marketplace, set timeouts, and distinguish authentication, rate limit, context, and upstream failures.
curl "$BASE_URL/v1/chat/completions" \
-H "Authorization: Bearer $API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"MODEL_NAME","messages":[{"role":"user","content":"Analyze this problem"}]}'
Quality evaluation
Build a fixed sample from real work and record factual accuracy, schema pass rate, human editing, refusal quality, latency, and cost. Use traceable sources or human rubrics for open answers and deterministic validation for structured tasks; model self-evaluation is not acceptance evidence.
Common limits
A context window is not reliable memory. Long input increases latency and can dilute important evidence. Temperature, reasoning effort, JSON Schema, tools, and caching are model-specific; run field-level contract tests against the exact model and channel before launch.
Use cases
- Knowledge Q&A and content generation
- Complex analysis and multi-step reasoning
- Structured extraction and classification
API protocols
/v1/responses/v1/chat/completions/v1/messages
FAQ
Should I choose Responses, Chat Completions, or Messages?
Start with the endpoints actually supported by the target model and channel. Existing messages/choices clients can keep Chat, new workflows can evaluate Responses, and native Claude structures commonly use Messages. Tools, stream events, and response fields differ, so migration is not a URL-only change.
Is a reasoning model always better than a general text model?
No. Multi-step math, code, and dense constraints are more likely to benefit from reasoning, while summarization, classification, and simple extraction often prioritize low latency and stable formatting. Reasoning can add wait time and output tokens, so route by task difficulty and set budgets.
Can I compare two text models using a public benchmark alone?
No. Use real business samples and record factual accuracy, schema pass rate, human editing, refusal quality, first-token and total latency, and actual cost. Validate structured tasks programmatically and open answers with sources or a human rubric.
Does a large context window mean the model reliably remembers everything?
No. Long inputs raise cost and latency and can dilute key evidence. Send only what the task needs, redact sensitive data, and review service retention terms. Critical facts still need retrieval, citations, or deterministic checks.
Related guides
Official sources
- OpenAI Models Official
- Claude Models Official
- Gemini Models Official