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AIWave documents an OpenAI-compatible gateway for calling multiple Chinese AI model families through one integration. You send a Bearer-keyed POST request to https://aiwave.live/v1/chat/completions, set the requested model ID, and provide standard chat messages. That lets an application switch among supported DeepSeek, GLM, Kimi, Qwen, ERNIE, MiniMax, Doubao, StepFun, MiMo and other catalog entries without implementing a separate protocol for every provider.
“50+” is AIWave’s catalog claim, not an independently measured count. Verify the live model IDs, modalities, rates and availability before shipping. The practical advantage is integration simplicity; quality, latency, privacy, limits, reliability and cost still have to be tested for your workload.
What does “one API” mean for Chinese AI models?
A single gateway gives your application one authentication method, request format and base URL while the gateway routes a request to the model named in the payload. AIWave’s organization documentation shows the OpenAI-style chat-completions route and names model families including DeepSeek, GLM, Kimi, Qwen, ERNIE, MiniMax, Doubao, StepFun and MiMo. The aiwave package listing separately claims access to more than 50 models.
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This does not make the models identical. Each model can differ in language ability, context window, vision or other modalities, tool support, rate limits, response time, output style and price. Treat the gateway as a common transport layer, then select and test models individually.
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What the common request contains
- An API key sent as an
Authorization: Bearerheader. - A model identifier in the
modelfield. - A chat history in the
messagesarray, with roles such assystem,userandassistant. - Optional generation settings supported by the selected model and gateway.
AIWave’s README uses deepseek-flash as an example ID. Example IDs can change, so copy the current identifier from the provider’s catalog instead of assuming that example remains callable.
Which Chinese model families can you call?
The documented roster covers several major Chinese model families, but the exact live inventory is volatile. Use the family names below as starting points for investigation, not as a guarantee that every example is enabled on your account.
| Family named in documentation | Examples shown by AIWave | What to verify before use |
|---|---|---|
| DeepSeek | DeepSeek V4 Pro, V3, R1; deepseek-flash appears in the README example |
Current model ID, reasoning behavior, context and supported features |
| GLM | GLM-5.1, GLM-5, GLM-4-Flash | Exact ID, language coverage, tool or streaming support |
| Kimi | Kimi VL, Kimi Thinking | Whether vision input is enabled and the required message format |
| Qwen | Qwen 3, Qwen 3 VL | Text versus vision variant and current context limit |
| ERNIE | ERNIE 4.5, ERNIE Speed | Live availability, rate limits and billing tier |
| MiniMax | Family named, specific examples not established | Current catalog ID and modality |
| Doubao | Family named, specific examples not established | Current catalog ID and regional availability |
| StepFun | Family named, specific examples not established | Current catalog ID and supported API features |
| MiMo | Family named, specific examples not established | Current catalog ID and usage rates |
AIWave’s model-roster article lists IDs, context lengths and suggested uses, but it is not a live catalog. Confirm every value in the current catalog and dated pricing material linked from the official GitHub organization.
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How do you get a key and make the first request?
Create an account with AIWave, generate an API key in its current dashboard, and store the key as a secret. The provider describes the service as pay-as-you-go and says signup credits and no minimum deposit are available; credit amounts, eligibility and billing conditions can change, so check the account and pricing pages that are live when you sign up.
- Create and protect the key. Put it in a secret manager or an environment variable named
AIWAVE_API_KEY. Do not commit it to source control or expose it in browser JavaScript. - Confirm a live model ID. Open AIWave’s catalog and copy the exact spelling, including punctuation and version suffixes. Check whether the model accepts text only, vision input or other modalities.
- Send a minimal chat request. The documented route is
POST https://aiwave.live/v1/chat/completions. - Inspect the response and usage. Record the returned text, finish information and any usage fields. Use request-level usage records when estimating cost.
curl https://aiwave.live/v1/chat/completions
-H "Authorization: Bearer $AIWAVE_API_KEY"
-H "Content-Type: application/json"
-d '{
"model": "deepseek-flash",
"messages": [
{"role": "user", "content": "Explain photosynthesis in two sentences."}
]
}'
If this returns an unknown-model error, do not change the request format first. Recheck the catalog, account permissions and the model’s current ID. A successful HTTP response also does not prove that the model supports every option your application will later send.
How do you use the API from Python?
The package page lists Python 3.8 or newer and shows both an AIWave-specific client and an OpenAI SDK configuration. It lists version 0.1.0, uploaded June 20, 2026, with an Alpha development-status classifier. For a production service, pin the version you tested and monitor package updates.
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Option 1: the AIWave client
python -m pip install aiwave
import os
from aiwave import AIWave
client = AIWave(api_key=os.environ["AIWAVE_API_KEY"])
response = client.chat.completions.create(
model="deepseek-flash",
messages=[
{"role": "user", "content": "Summarize this text in three bullets."}
],
)
print(response.choices[0].message.content)
Use the package’s current documentation to confirm method names and supported parameters before upgrading. Because the package is marked Alpha, keep a direct HTTP or OpenAI-SDK path available as a fallback during evaluation.
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python -m pip install openai
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["AIWAVE_API_KEY"],
base_url="https://aiwave.live/v1",
)
response = client.chat.completions.create(
model="deepseek-flash",
messages=[
{"role": "user", "content": "List three risks in this contract."}
],
)
print(response.choices[0].message.content)
The OpenAI-compatible shape reduces application changes when you switch between model IDs, but compatibility is not proof that every SDK feature is implemented. Test streaming, tool calls, structured output, vision payloads and other features separately if your application depends on them.
How do you call the gateway from Node.js?
Node.js can use any HTTP client that sends JSON. The example below uses the built-in fetch available in current Node.js releases; on older runtimes, install a fetch implementation supported by your project.
const response = await fetch("https://aiwave.live/v1/chat/completions", {
method: "POST",
headers: {
"Authorization": `Bearer ${process.env.AIWAVE_API_KEY}`,
"Content-Type": "application/json"
},
body: JSON.stringify({
model: "deepseek-flash",
messages: [
{ role: "user", content: "Give me a five-step testing checklist." }
]
})
});
if (!response.ok) {
throw new Error(`AIWave request failed: ${response.status}`);
}
const data = await response.json();
console.log(data.choices?.[0]?.message?.content);
Keep the key on your server. If a web client must request text, expose your own authenticated endpoint and enforce quotas, logging rules and input limits there rather than forwarding the provider key to the browser.
How should you choose among the available models?
Start with the task, not the family name. Select a candidate by required modality and context, then verify quality, latency, cost and feature compatibility on representative inputs.
- Define the output contract. Decide whether you need plain text, reliable JSON, citations, code, long-context synthesis, image understanding, tool calls or a reasoning-style response.
- Filter by capability. Remove models that lack the required modality, context length or API feature.
- Check current rates. Use the dated pricing page or pricing JSON linked in AIWave’s README. Rates can differ by model and by input versus output tokens.
- Test realistic prompts. Use anonymized samples that reflect production length, language, concurrency and failure conditions.
- Measure the whole request. Record time to first token, total latency, error rate, output validity, token usage and retry frequency.
- Decide how much variability you can accept. Routing or fallback can improve availability but may change style, reasoning depth or schema adherence between attempts.
| Evaluation axis | Questions to answer |
|---|---|
| Identity and capability | Is the exact live ID documented, and does it perform the required task? |
| Modalities and context | Does it accept the needed text, image or other input, and can it hold your largest prompt? |
| Feature compatibility | Do streaming, tools, JSON constraints and token controls behave as your client expects? |
| Economics | What are the current input and output rates, and how much does a representative request consume? |
| Operations | What latency, rate limits, retry behavior and availability do you observe under your load? |
| Data handling | Where is data processed, how long is it retained and what contractual controls apply? |
AIWave recommends representative workload testing and request-level usage review. The available documentation does not establish an independent winner on quality, speed or cost, so publish or deploy only results from your own controlled evaluation.
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What does AIWave say the gateway does behind the API?
In an AIWave-authored technical article, the service is described as a protocol-normalizing proxy that can translate formats, route requests, balance load and use fallback chains. These are vendor-described platform behaviors, not independent reliability or performance guarantees.
For your application, that distinction matters. A fallback may rescue a failed request, but the replacement model can have different context limits, safety behavior, output format or price. If deterministic behavior is important, log the model actually used, constrain fallback candidates and validate every response against your schema.
How much does access cost?
The service is presented as pay-as-you-go, with no minimum deposit and signup credits in the Python package description. Those are vendor claims; the current credit amount, eligibility rules, expiration and billing terms were not established here. Do not budget from a promotional credit or an old screenshot.
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What should you verify before putting the integration in production?
Compatibility is only the starting point. Run these checks in a staging account with production-like traffic and sanitized data.
Reliability and limits
- Set connect, read and overall request timeouts appropriate to your user experience.
- Implement bounded retries with exponential backoff for transient failures, and avoid retrying authentication or validation errors.
- Discover account and model rate limits, then use concurrency controls and a queue to stay below them.
- Record status codes, provider request IDs when available, selected model, latency and token usage.
- Test fallback behavior deliberately; verify that a fallback response still satisfies your output contract.
Quality and safety
- Create a fixed evaluation set covering normal, edge-case and adversarial prompts.
- Validate JSON or structured responses before they reach downstream systems.
- Check language, citation, code and long-context behavior for the models you intend to offer.
- Define refusal, moderation and human-review handling for your domain.
Privacy and governance
- Obtain current retention, training-use, regional-processing and deletion terms from AIWave’s primary policy documentation.
- Remove secrets and unnecessary personal data from prompts and logs.
- Confirm whether your contractual or regulatory requirements permit sending the relevant data through this gateway.
- Document who can view request and response logs, and set a deletion schedule.
The reviewed documentation does not establish AIWave’s uptime, retention period, regional processing, contractual guarantees or independent benchmark results. Treat each as an item to confirm directly before sending sensitive or regulated data.
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What are the most common integration failures?
“Model not found” or an invalid model error
Usually the ID is stale, misspelled or unavailable to the account. Copy the current ID from the live catalog, check capitalization and version suffixes, and confirm that the model has not been retired or restricted.
401 or 403 responses
Check that the environment variable is populated in the running process, the header is exactly Authorization: Bearer YOUR_KEY, and the key has not been revoked. A newly created key may also require account activation or credit setup.
400 errors after switching models
Different models may accept different modalities or optional parameters. Reduce the request to a plain text message, remove unsupported fields, then add features one at a time while consulting the current model documentation.
Timeouts and intermittent failures
Measure connection and server time separately, raise limits only when justified, and use bounded retries with jitter. If fallback is enabled, log both the requested and actual model so you can identify changes in output or cost.
Unexpectedly high bills
Inspect request-level usage, especially retries, long conversation histories and fallback calls. Cap maximum output where appropriate, trim stale messages and alert on per-request and daily spend.
Is one gateway better than separate provider integrations?
A gateway is attractive when your application needs to experiment with several model families, centralize key management or keep one OpenAI-style client. Separate direct integrations can provide clearer provider-specific features, contracts and operational visibility, but they require more adapters and credentials.
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Choose the gateway when a common interface and rapid model switching outweigh the need for provider-native controls. Keep an abstraction layer in your own code either way: represent model capabilities, pricing metadata, timeout policy and fallback rules explicitly instead of scattering assumptions throughout prompts.
How do you keep model availability current?
Model catalogs change faster than application releases. At deployment time, resolve a configured logical name to a verified live ID, and fail clearly if that mapping is unavailable. Maintain a small inventory containing ID, family, modality, context, price date, tested SDK version and last evaluation date.
Re-run smoke tests whenever the catalog, package or pricing data changes. Pin the aiwave package version in environments that use it, and review release notes before upgrading from the Alpha-listed 0.1.0 package.
Frequently Asked Questions
Can I use an existing OpenAI SDK application without rewriting it?
Often, yes: point the SDK client at https://aiwave.live/v1, use your AIWave key, and change the model ID. Test every feature your application relies on because an OpenAI-compatible endpoint does not guarantee identical support for streaming, tools, vision or structured output.
Does “50+ models” mean every model is available in every country or account?
No. The number is AIWave’s catalog claim, and availability can depend on the current catalog, account permissions, model retirement and regional or billing conditions. Verify the live list and make a test request with the exact ID you plan to use.
Can I send images or other non-text inputs through the gateway?
Only when the selected model and the gateway’s current request format support that modality. Check the model entry for vision or other input support and test the exact payload; a text-only model will reject or mishandle an image message.
How can I prevent a fallback model from changing my application’s output?
Restrict fallback candidates to models that meet the same capability and schema requirements, and validate every response before accepting it. Log the actual model used so quality and cost changes are visible during incidents.
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