AIWave API Documentation for Production Chinese Model Migration

Why This Documentation Page Should Be a Migration Checklist

The 2026-08-21 AIWave keyword report shows Tier 1 demand around `aiwave api`, `aiwave api documentation`, and `aiwave quickstart`. That search intent is practical: developers do not want another landing page. They want to know whether an OpenAI-compatible Chinese model gateway can be added to an existing codebase without breaking authentication, telemetry, budget controls, or incident response.

Treat the documentation as a migration checklist. A quick test request proves connectivity, but production migration needs a stronger sequence: inventory the current OpenAI calls, select comparable Chinese model routes, verify the request and response fields, add dated price metadata, replay representative prompts, and define rollback. This article is written for Tier 1 and Tier 2 teams that already run API workloads and need a structured path from evaluation to controlled traffic.

Source-Date Discipline

Prices and model catalogs move. During this run on Aug 22, 2026, AIWave pricing and predictable-pricing pages still published DeepSeek V4 all-day rows with a rate date of 2026-08-19. V4 Flash is listed at $0.638 per 1M input tokens, $1.914 per 1M output tokens, and $0.0203 per 1M cache-hit input tokens. V4 Pro is listed at $1.914 input, $5.742 output, and $0.0638 cache-hit per 1M tokens.

DeepSeek official docs checked the same day list a separate time-window structure: V4 Flash peak rows of $0.44 input, $1.32 output, and $0.014 cache-hit per 1M tokens, with off-peak rows at half those rates; V4 Pro peak rows of $1.32 input, $3.96 output, and $0.044 cache-hit, again with off-peak rows at half. A migration checklist should store both the provider source and the checked date because the best route is workload dependent.

Migration Surface Map

The migration surface is wider than one `base_url` value. Authentication, model IDs, context limits, streaming behavior, JSON output, tool calls, error shape, retry policy, and observability all need review. The table below is the minimum map a platform team should complete before moving customer traffic.

OpenAI-Compatible Setup

A documentation-led migration starts with a narrow adapter instead of touching every call site. Keep the SDK initialization in one module, then pass task-specific model routes from configuration. That keeps the rest of the application focused on messages, tools, schemas, and business logic.

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY_HERE",
    base_url="https://aiwave.live/v1",
)

MODEL_ROUTES = {
    "support_summary": "deepseek-v4-flash",
    "architecture_review": "deepseek-v4-pro",
    "coding_patch": "qwen3-coder-plus",
}

def complete(task: str, user_prompt: str):
    model = MODEL_ROUTES[task]
    return client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": user_prompt}],
        temperature=0.2,
        max_tokens=1200,
    )

Prompt Replay Before Live Traffic

Do not judge migration quality from a single happy-path prompt. Build a replay set from real support tickets, coding tasks, extraction jobs, and failure cases. Run each prompt through the current model and the candidate AIWave route. Score structure validity, factual consistency, latency, token use, user-visible quality, and review effort.

For a SaaS team, replay results should be grouped by tenant class and task kind. A Japanese customer-support summary, a German compliance note, and a US coding-agent patch may all behave differently. The decision should be based on representative prompts rather than a generic benchmark table.

Budget Controls in the Documentation Layer

The documentation cluster should point readers from setup to pricing and predictable-pricing because a working API call is only one part of production readiness. Every route needs a cost ceiling before dispatch, an output cap, and a ledger row after completion. If the migration makes it easier to call more models, the budget guard becomes more important, not less.

Use dated rows in config, then add alerts for unusual output expansion, rising retry count, or a route mix that drifts toward higher-reasoning models. This is especially important when teams compare AIWave all-day pricing with official time-window pricing. The gateway value is operational simplicity and routing breadth; the budget model still has to be explicit.

Internal Links That Help Searchers Act

The keyword report identifies brand and documentation queries with Tier 1 impressions but weak click-through. The article should move a searcher into action with visible internal links: Chat Completions docs, Models docs, pricing, and predictable-pricing. Those links form the path from search to test request.

Add model-specific links only when they match the route in the article. For DeepSeek traffic, link to DeepSeek V4 Flash and DeepSeek V4 Pro. For coding routes, link to model docs first, then use external official pricing pages for source verification.

Launch Checklist

Before launch, verify the API key scope, model IDs, request fields, response parsing, stream handling, JSON schema checks, retry caps, cost ledger, alert thresholds, and rollback flag. Record the source date for every public price row used in estimates. Keep the first launch small enough that a rollback is a configuration change rather than a migration project.

After launch, review route mix daily for the first week. Look for unexpected Pro usage, long prompts with poor cache behavior, tenant-specific spikes, and schema repair loops. That operational review is what turns API documentation into a maintainable migration system.