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Data freshness: As of 2026-07, all model recommendations and benchmark numbers come from the Model Info overview (APIYI official). That document is continuously updated as vendors ship new models; for the latest model list and real-time prices, refer to the APIYI console pricing page.
How to read this page:
  • We focus on 8 task scenarios (coding, writing, fast response, long context, reasoning, agents, web search, cost control) and give the recommended combos from model-info

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📌 Recommended Combos for 8 Scenarios (Copy-Paste Ready)

Taken directly from Model Info overview → Usage Recommendations

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Scenario 1: Coding

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Scenario 2: Writing

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Scenario 3: Fast Response

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Scenario 4: Long Context

Long-context caveat: The numbers above are the context windows stated in model-info. The real “effective window” (the longest length at which retrieval stays accurate) is usually below the nominal value; for high-accuracy domains like finance and healthcare, combine with RAG chunking.

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Scenario 5: Complex Reasoning

How to enable reasoning tiers:
  • GPT-5.5 / GPT-5.5 Pro default to medium; to use xhigh, pass reasoning_effort: xhigh in the request
  • GPT-5.5 Pro only works on /v1/responses, not /v1/chat/completions
  • Do not use the GPT Pro series for everyday tasks — a single call can cost several dollars

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Scenario 6: Agents

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Scenario 8: Cost Control

Prices for other models: model-info does not state explicit input/output prices for every model. All prices are governed by the APIYI console pricing page. With “source-forwarded routes + fixed 1:7 exchange rate + recharge bonuses” combined, your effective price is usually below going direct to the vendors — see the console for exact numbers.

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🧮 Coding Benchmark Reference Table

Only models with explicit numbers in model-info are listed. For entries with question marks or no numbers, see the full descriptions in model-info.

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📚 Context Window Reference Table

Reminder: context window numbers = vendor-claimed maximums. Real retrieval accuracy (“needle in a haystack”) drops significantly at extreme lengths; long context ≠ a replacement for RAG.

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💡 4 Cost-Optimization Tips from model-info (verbatim)

  1. Tier your usage: cheap models for simple tasks, premium models for complex ones
  2. Test then scale: prototype with a small model, switch to a large one once requirements are clear
  3. Batch processing: choose Nano or Mini variants for large volumes of similar tasks
  4. Cache and reuse: cache results for repeated queries

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❓ 3 GPT-5 Series Caveats from model-info (verbatim)

GPT-5 series usage notes:
  1. The temperature parameter must be set to 1 (only 1 is supported)
  2. Use max_completion_tokens instead of max_tokens
  3. Do not pass the top_p parameter

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🔗 Related Resources

Still unsure which to pick? Contact APIYI support and tell us:
  • Your use case (chat / RAG / agents / writing / …)
  • Daily call volume
  • Performance requirements (first-token latency, output quality)
  • Budget range
With these 4 data points we will give you a tailored model combo.
Disclaimer: All data on this page comes from docs/api-capabilities/model-info.mdx (read as of 2026-07). model-info is continuously updated as vendors ship models; for the latest recommendations, consult model-info directly or the APIYI console pricing page.