AIWave API Documentation Quickstart for Chinese Model Routing

Keyword source: AIWave Daily Keyword Intelligence for server date 2026-08-19, generated on 2026-08-20 Asia/Shanghai. Pricing pages were rechecked during this blog run before deployment.

Why Documentation Intent Matters

The Aug 19 AIWave keyword report shows that `aiwave api documentation`, `aiwave api`, `aiwave pricing`, and `aiwave quickstart` are visible in Tier 1 search contexts but still need stronger click-through and better internal paths. That is a high-value signal because documentation-intent readers are closer to a test request than broad news readers. A developer in the United States, the United Kingdom, Germany, Japan, or Singapore who searches for API documentation is usually trying to answer a practical question: can I call Chinese model families through a familiar client, can I explain the cost, and can I reverse the change if quality or latency is not acceptable.

This guide turns that search intent into a routeable workflow. It does not ask a team to trust a single model headline. It starts with four pages: Chat Completions, model catalog, pricing, and predictable pricing. The Chat Completions docs establish the OpenAI-compatible request shape. The model catalog tells engineering which exact model IDs exist. The pricing page gives the current AIWave account rows. The predictable-pricing page explains how AIWave's all-day DeepSeek rates differ from the official DeepSeek peak and off-peak schedule.

The live AIWave pricing files checked on Aug 20, 2026 still show the Aug 19 rate card for DeepSeek V4 Flash at $0.638 per 1M input tokens, $1.914 per 1M output tokens, and $0.0203 per 1M cache-hit input tokens. DeepSeek V4 Pro is listed at $1.914 input, $5.742 output, and $0.0638 cache-hit per 1M tokens. The predictable-pricing page also keeps the official DeepSeek rows separate: Flash peak $0.440 input, $1.320 output, $0.0140 cache-hit; Flash off-peak $0.220, $0.660, $0.0070; Pro peak $1.320, $3.960, $0.0440; Pro off-peak $0.660, $1.980, $0.0220. That separation is essential because AIWave is not claiming to be below the official DeepSeek list row. It is offering a unified API and all-day planning row for teams that value predictable routing and multi-model workflow.

The Four-Page Path

A clean documentation path should move a reader from concept to first controlled request without sending them into a maze. The table below is the recommended order for an engineering team evaluating AIWave for Chinese model routing. It also shows what the team should record before traffic moves from a local test to staging.

Step	Page to open	Decision to make	Ledger field to record
1	Chat Completions docs	Confirm SDK compatibility and base URL	client_name, base_url, environment
2	Model catalog	Choose candidate DeepSeek, Qwen, Kimi, or GLM IDs	model_id, provider_family, capability
3	Pricing page	Timestamp the AIWave account row	price_source_date, input_row, output_row, cache_row
4	Predictable pricing	Compare all-day planning with official schedule	schedule_policy, reason_for_route
5	Usage ledger	Measure actual request behavior	input_tokens, cached_input_tokens, output_tokens, status

The important point is that documentation should not end at a successful response. A response proves connectivity. It does not prove that the route is acceptable for a production tenant. The first test should also write a ledger row with model ID, policy version, source date, and task kind. That ledger row becomes the bridge between search-intent documentation and real product operations.

This path also keeps AIWave content aligned with Tier 1 buyer concerns. US and UK startups may care about quick SDK migration. German and Dutch teams often ask for clear cost records and privacy posture. Japanese and Singaporean teams may evaluate latency, regional routing, and support clarity. One documentation path can serve all of those markets if it uses precise facts instead of broad promises.

OpenAI-Compatible Client Example

The first code sample should be small enough to paste into a local smoke test and strict enough to prevent bad habits. Do not place price policy inside the prompt. Do not commit credentials. Do not hard-code one global model forever. Start with a route object and make the selected model visible in logs.

from dataclasses import dataclass, asdict
from openai import OpenAI

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

@dataclass(frozen=True)
class Route:
    model: str
    max_tokens: int
    policy_version: str
    price_source_date: str
    reason: str

def choose_route(task_kind: str) -> Route:
    if task_kind == "architecture_review":
        return Route("deepseek-v4-pro", 2400, "docs-route-2026-08-20", "2026-08-20", "reasoning_review")
    if task_kind == "coding_execution":
        return Route("qwen3-coder-plus", 1800, "docs-route-2026-08-20", "2026-08-20", "code_execution")
    if task_kind == "long_context_review":
        return Route("kimi-k3", 3200, "docs-route-2026-08-20", "2026-08-20", "long_context")
    return Route("deepseek-v4-flash", 1200, "docs-route-2026-08-20", "2026-08-20", "default_compact")

route = choose_route("coding_execution")
response = client.chat.completions.create(
    model=route.model,
    messages=[{"role": "user", "content": "Summarize the test failure and propose the next command."}],
    max_tokens=route.max_tokens,
)

print({"response_id": response.id, **asdict(route)})

The model names in route policy should match live catalog entries before launch. If a catalog name changes or a provider alias points to a newer version, update policy deliberately and keep the older policy version for audit. This is especially important after price changes, because finance and support need to know which row was active when a request ran.

A production implementation can add tenant budgets, environment allowlists, evaluation scores, and fallback rules. Keep the first layer simple. The easiest route policy to review is a small table that maps task kind to allowed models and maximum output. Complexity should come from measured workload needs, not from a desire to build a universal router on day one.

Price Checks Beyond DeepSeek

A documentation quickstart should not pretend that DeepSeek is the only route. Official pages checked on Aug 20, 2026 show that Chinese model families expose different pricing surfaces. QwenCloud documents pay-as-you-go billing, request-length tiers, context caching, Batch API behavior, thinking-token billing, and marketplace pages for model-specific rows. The Qwen3-Coder-Plus model page shows $1 per 1M input tokens, $5 per 1M output tokens, $0.2 per 1M implicit-cache input tokens, $1.25 per 1M explicit cache creation, and $0.1 per 1M explicit cache read for the listed tier. Kimi K3's public pricing page has a 1M-token context and separates cache-miss input, cache-hit input, and output rows. Z.AI lists GLM-5.2 and GLM-5.1 at $1.40 input, $0.26 cached input, and $4.40 output per 1M tokens.

These rows should not be collapsed into one generic `cost` field. A serious ledger records model family and token category. A coding workload using Qwen may have explicit cache creation and read semantics. A long-context Kimi workload may be dominated by repeated context. A GLM workload may use reasoning, vision, tools, or agent features. A DeepSeek workload may require a peak-window assumption if it is planned directly from the official schedule. The documentation path should teach readers to preserve those differences.

This is also where internal links do useful SEO work. A reader who lands on this article from `aiwave api documentation` should be one click from the Chat Completions docs, model catalog, pricing, and predictable-pricing page. That path answers the operational questions behind the query and helps Google understand the relationship between brand, docs, pricing, and model-specific content.

Production Readiness Checklist

Before moving beyond a smoke test, require a checklist that senior engineers can inspect. Confirm the base URL, SDK version, route policy version, model ID, output cap, price source date, expected token range, retry policy, and rollback route. Confirm whether the prompt contains customer data and whether logs store content or only billing metadata. Confirm that VIP account behavior is described correctly: VIP status applies to all your tokens automatically, so client code should not ask developers to select token groups.

Run the same representative prompts through the previous route and the AIWave route. Store latency, status, output tokens, reviewer result, and actual model. For schema tasks, compare exact validity. For summaries, compare human acceptance. For coding-agent tasks, compare tests passed and follow-up edits. The goal is not to prove every model is interchangeable. The goal is to know which task classes can move safely.

The documentation quickstart is complete when a developer can make a request, read the model row, explain the price source, and find the next guide. That is the path from a low-CTR docs query to a serious trial. It is also the path that keeps content honest: every claim points back to a live page, an official source, or a ledger field that a production team can verify.

External sources checked

Related AIWave guides

FAQ

What should a team read first in AIWave API documentation?

Start with Chat Completions, the model catalog, current pricing, and the predictable-pricing page before writing route policy.

Can the same OpenAI SDK client call several Chinese model families?

Yes. Keep the base URL and authentication stable, then choose model IDs from policy code instead of hard-coding one model everywhere.

What price rows were checked for this guide?

The guide references AIWave live pricing checked on Aug 20, 2026, plus DeepSeek, QwenCloud, Kimi, and Z.AI public pricing pages checked the same day.