AIWave · Developer guide · Updated August 5, 2026

How to Choose the Right AI Model for Coding, Chat, RAG, or Agents

Choosing an AI API in 2026 is an engineering decision, not a slogan. Teams need a predictable interface, clear billing units, and enough model choice to match latency, reasoning, coding, retrieval, and agent workloads. This guide uses the live AIWave model catalog and primary vendor documentation so the decision can be reproduced with your own prompts.

AIWave exposes Chinese models through an OpenAI-compatible endpoint at https://aiwave.live/v1. Existing clients keep their message format while a routing decision changes the model name. Review the Chat Completions API reference, then verify current rates on the live pricing page.

A use-case decision tree

For deterministic extraction, start with a fast model and measure schema validity. For repository coding, test real languages and tests. For RAG, prioritize instruction following, citation discipline, and context handling. For agents, prioritize tool-call correctness, bounded reasoning, and recovery. For chat, compare helpfulness, tone, latency, and multilingual coverage.

The tree narrows the search but does not replace evaluation. Keep two candidates per workload and route by configuration. A unified gateway makes experiments practical without separate SDK integrations.

Reference rates

AIWave figures come from its public pricing endpoint at publication time. Rates can change; re-check live pricing before budgeting.

Build an evaluation set

Create 20–50 prompts per use case with normal cases, edge cases, long inputs, and malformed requests. Run coding patches through tests, verify RAG claims against retrieved context, and reject invalid agent arguments. Store scores and token usage in a versioned file.

OpenAI-compatible test

from openai import OpenAI

client = OpenAI(
    base_url="https://aiwave.live/v1",
    api_key="YOUR_API_KEY_HERE",
)
response = client.chat.completions.create(
    model="deepseek-v4-flash",
    messages=[{"role":"user","content":"Summarize this incident and list three tests."}],
    max_tokens=600,
)
print(response.choices[0].message.content)

Route by risk

Use a fast model for low-risk work and reserve a stronger model for decisions affecting users, money, or security. Set timeouts and fallback rules. Record the selected model with request metadata and remove sensitive prompts from logs.

Production checklist

Start with a representative workload, not a leaderboard. Capture prompt length, completion length, tool calls, latency budget, and failure handling. Run the same fixture through each candidate with identical decoding parameters. Measure first-token time, total duration, JSON validity, task acceptance, and token usage. Report p50 and p95 latency rather than one average.

Keep the provider boundary thin. Store the model identifier in configuration. Log request identifiers, token usage, status, and latency while removing prompts and completions from persistent storage. Set an output budget, abort stalled streams, retry only idempotent failures, validate tool arguments before side effects, and keep a tested fallback model. Review the AIWave trust notes with security stakeholders.

Operational notes

Long context does not mean every request should include an entire repository. Chunk documents, retrieve relevant passages, and summarize older turns. For agents, cap tool output and iteration count. When a model change is proposed, run the same fixture, compare quality and cost, and record the decision. Do not silently switch models when reproducibility matters. Remove sensitive prompts from logs and review retention settings.

Use a private, versioned test set with normal cases, edge cases, long inputs, and malformed requests. For coding, run generated patches through tests. For RAG, verify claims against retrieved context. For agents, replay tool traces and reject invalid arguments. Store scores and token histograms so a model change is reviewable. A benchmark is a decision aid, not a promise about every workload.

Use a private, versioned test set with normal cases, edge cases, long inputs, and malformed requests. For coding, run generated patches through tests. For RAG, verify claims against retrieved context. For agents, replay tool traces and reject invalid arguments. Store scores and token histograms so a model change is reviewable. A benchmark is a decision aid, not a promise about every workload.

Use a private, versioned test set with normal cases, edge cases, long inputs, and malformed requests. For coding, run generated patches through tests. For RAG, verify claims against retrieved context. For agents, replay tool traces and reject invalid arguments. Store scores and token histograms so a model change is reviewable. A benchmark is a decision aid, not a promise about every workload.

Use a private, versioned test set with normal cases, edge cases, long inputs, and malformed requests. For coding, run generated patches through tests. For RAG, verify claims against retrieved context. For agents, replay tool traces and reject invalid arguments. Store scores and token histograms so a model change is reviewable. A benchmark is a decision aid, not a promise about every workload.

Use a private, versioned test set with normal cases, edge cases, long inputs, and malformed requests. For coding, run generated patches through tests. For RAG, verify claims against retrieved context. For agents, replay tool traces and reject invalid arguments. Store scores and token histograms so a model change is reviewable. A benchmark is a decision aid, not a promise about every workload.

Use a private, versioned test set with normal cases, edge cases, long inputs, and malformed requests. For coding, run generated patches through tests. For RAG, verify claims against retrieved context. For agents, replay tool traces and reject invalid arguments. Store scores and token histograms so a model change is reviewable. A benchmark is a decision aid, not a promise about every workload.

Use a private, versioned test set with normal cases, edge cases, long inputs, and malformed requests. For coding, run generated patches through tests. For RAG, verify claims against retrieved context. For agents, replay tool traces and reject invalid arguments. Store scores and token histograms so a model change is reviewable. A benchmark is a decision aid, not a promise about every workload.

Use a private, versioned test set with normal cases, edge cases, long inputs, and malformed requests. For coding, run generated patches through tests. For RAG, verify claims against retrieved context. For agents, replay tool traces and reject invalid arguments. Store scores and token histograms so a model change is reviewable. A benchmark is a decision aid, not a promise about every workload.

Use a private, versioned test set with normal cases, edge cases, long inputs, and malformed requests. For coding, run generated patches through tests. For RAG, verify claims against retrieved context. For agents, replay tool traces and reject invalid arguments. Store scores and token histograms so a model change is reviewable. A benchmark is a decision aid, not a promise about every workload.

Use a private, versioned test set with normal cases, edge cases, long inputs, and malformed requests. For coding, run generated patches through tests. For RAG, verify claims against retrieved context. For agents, replay tool traces and reject invalid arguments. Store scores and token histograms so a model change is reviewable. A benchmark is a decision aid, not a promise about every workload.

Use a private, versioned test set with normal cases, edge cases, long inputs, and malformed requests. For coding, run generated patches through tests. For RAG, verify claims against retrieved context. For agents, replay tool traces and reject invalid arguments. Store scores and token histograms so a model change is reviewable. A benchmark is a decision aid, not a promise about every workload.

Use a private, versioned test set with normal cases, edge cases, long inputs, and malformed requests. For coding, run generated patches through tests. For RAG, verify claims against retrieved context. For agents, replay tool traces and reject invalid arguments. Store scores and token histograms so a model change is reviewable. A benchmark is a decision aid, not a promise about every workload.

Use a private, versioned test set with normal cases, edge cases, long inputs, and malformed requests. For coding, run generated patches through tests. For RAG, verify claims against retrieved context. For agents, replay tool traces and reject invalid arguments. Store scores and token histograms so a model change is reviewable. A benchmark is a decision aid, not a promise about every workload.

Use a private, versioned test set with normal cases, edge cases, long inputs, and malformed requests. For coding, run generated patches through tests. For RAG, verify claims against retrieved context. For agents, replay tool traces and reject invalid arguments. Store scores and token histograms so a model change is reviewable. A benchmark is a decision aid, not a promise about every workload.

Use a private, versioned test set with normal cases, edge cases, long inputs, and malformed requests. For coding, run generated patches through tests. For RAG, verify claims against retrieved context. For agents, replay tool traces and reject invalid arguments. Store scores and token histograms so a model change is reviewable. A benchmark is a decision aid, not a promise about every workload.

Use a private, versioned test set with normal cases, edge cases, long inputs, and malformed requests. For coding, run generated patches through tests. For RAG, verify claims against retrieved context. For agents, replay tool traces and reject invalid arguments. Store scores and token histograms so a model change is reviewable. A benchmark is a decision aid, not a promise about every workload.

Further reading

Check the model catalog, pricing, and API docs. Consult DeepSeek docs and Z.ai docs.