Qwen3.8-Max API

Pricing

Billed per token: input (every message you send) and output (the reply, including reasoning) at separate rates. Credits are reserved when the request starts and settled to the actual usage when it ends.

At a glance

Quick start

Create a key in the dashboard, then call the OpenAI-compatible endpoint directly or through the official OpenAI SDK with a custom base_url. The usage block of every response carries the credits charged.

curl -s -X POST https://api.genmux.tech/v1/chat/completions \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"qwen3.8-max","messages":[{"role":"system","content":"You are a concise assistant."},{"role":"user","content":"Explain idempotency keys in two sentences."}],"max_tokens":200}'

{
  "id": "0d8b6a3e-2f1c-4a5b-9c1d-7e2f3a4b5c6d",
  "object": "chat.completion",
  "created": 1756411279,
  "model": "qwen3.8-max",
  "choices": [
    {"index": 0, "message": {"role": "assistant", "content": "…"}, "finish_reason": "stop"}
  ],
  "usage": {"prompt_tokens": 31, "completion_tokens": 58, "total_tokens": 89, "cost": 0.028, "cost_source": "vendor"}
}

curl -sN -X POST https://api.genmux.tech/v1/chat/completions \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"qwen3.8-max","messages":[{"role":"system","content":"You are a concise assistant."},{"role":"user","content":"Explain idempotency keys in two sentences."}],"max_tokens":200,"stream":true}'

data: {"id":"0d8b6a3e-…","object":"chat.completion.chunk","model":"qwen3.8-max","choices":[{"index":0,"delta":{"role":"assistant","content":"An"},"finish_reason":null}]}

data: {"id":"0d8b6a3e-…","object":"chat.completion.chunk","model":"qwen3.8-max","choices":[{"index":0,"delta":{"content":" idempotency"},"finish_reason":null}]}

…

data: {"id":"0d8b6a3e-…","object":"chat.completion.chunk","model":"qwen3.8-max","choices":[{"index":0,"delta":{},"finish_reason":"stop"}]}

data: {"id":"0d8b6a3e-…","object":"chat.completion.chunk","model":"qwen3.8-max","choices":[],"usage":{"prompt_tokens":31,"completion_tokens":58,"total_tokens":89,"cost":0.028,"cost_source":"estimated"}}

data: [DONE]

import os
from openai import OpenAI

client = OpenAI(base_url="https://api.genmux.tech/v1", api_key=os.environ["API_KEY"])

completion = client.chat.completions.create(
    model="qwen3.8-max",
    messages=[
        {"role": "system", "content": "You are a concise assistant."},
        {"role": "user", "content": "Explain idempotency keys in two sentences."},
    ],
    max_tokens=200,
)

print(completion.choices[0].message.content)
print(completion.usage.total_tokens, "tokens,", completion.usage.model_extra["cost"], "credits")

import OpenAI from "openai";

const client = new OpenAI({ baseURL: "https://api.genmux.tech/v1", apiKey: process.env.API_KEY });

const completion = await client.chat.completions.create({
  model: "qwen3.8-max",
  messages: [
    { role: "system", content: "You are a concise assistant." },
    { role: "user", content: "Explain idempotency keys in two sentences." },
  ],
  max_tokens: 200,
});

console.log(completion.choices[0].message.content);
console.log(completion.usage.total_tokens, "tokens,", completion.usage.cost, "credits");

// Streaming: the final chunk carries usage
const stream = await client.chat.completions.create({
  model: "qwen3.8-max",
  messages: [{ role: "user", content: "Explain idempotency keys in two sentences." }],
  stream: true,
});
for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content ?? "");
  if (chunk.usage) console.log(`\n${chunk.usage.total_tokens} tokens, ${chunk.usage.cost} credits`);
}

import os
from openai import OpenAI

client = OpenAI(base_url="https://api.genmux.tech/v1", api_key=os.environ["API_KEY"])

stream = client.chat.completions.create(
    model="qwen3.8-max",
    messages=[{"role": "user", "content": "Explain idempotency keys in two sentences."}],
    max_tokens=200,
    stream=True,
)

for chunk in stream:
    if chunk.choices and chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)
    if chunk.usage:  # the last chunk: token counts and the credits charged
        print(f"\n{chunk.usage.total_tokens} tokens, {chunk.usage.model_extra['cost']} credits")

Frequently asked questions

What is the Qwen3.8-Max API?

Alibaba's newest flagship Qwen model, with a very large context window and solid tool use — a sensible default for most agent and reasoning work. Qwen3.8-Max belongs to Alibaba's Qwen3.8 family. It is a proprietary model available only through an API — there are no weights to download. On genmux API it sits behind an OpenAI-compatible chat completions endpoint with a 1M-token context window, billed per token in prepaid credits.

How much does Qwen3.8-Max cost per 1M tokens?

Input tokens cost 138.718 credits per 1M (about $1.734 at the Scale pack rate) and output tokens 416.161 credits per 1M (about $5.202). Every message in the request — system, user and earlier assistant turns — is input; the reply, including any reasoning, is output. A month of 750,000 tokens in and 250,000 tokens out comes to 208.079 credits, about $2.601. Credits are prepaid: no subscription and no per-seat fee.

Which other models is Qwen3.8-Max comparable to?

A newer, larger Qwen3 flagship than Qwen3.7-Max, with a bigger context window for long agent sessions. Every model in the catalogue is reachable through the same endpoint and key, so a comparison is a matter of changing the model field and reading the usage block of each response; GET /v1/models lists context windows, limits and prices side by side.

When is Qwen3.8-Max worth its price over a cheaper model?

When the quality of the answer is the product: general chat and assistants, step-by-step reasoning, multi-step agent loops and long documents. Route the routine traffic — short classifications, drafts, extraction — to a cheaper model and keep Qwen3.8-Max for the calls that need it. With one key and one request shape the split is a routing rule in your code, not a second integration.

What are the context window and max_tokens limits of Qwen3.8-Max?

1M tokens of context, shared between the prompt and the completion. max_tokens caps the reply: the model accepts up to 16,384 and uses 2,048 when you omit the field. A prompt that leaves no room for a reply is rejected with 400 context_length_exceeded before anything is charged, and a reply cut off by the cap ends with finish_reason "length".

Does Qwen3.8-Max support streaming?

Yes. Send "stream": true and the response is text/event-stream: one chat.completion.chunk per token on data: lines, then a final chunk with an empty choices array and a usage object — prompt_tokens, completion_tokens, total_tokens and cost, the credits charged — followed by data: [DONE]. Read usage from that last chunk; the OpenAI SDKs expose it as chunk.usage. If the upstream fails mid-stream you get a data: {"error": {...}} frame and then [DONE].

Can I use the OpenAI SDK with Qwen3.8-Max?

Yes. Point base_url at https://api.genmux.tech/v1, use your genmux API key as api_key and set model to "qwen3.8-max". Chat completions with system, user and assistant messages, streaming, max_tokens, temperature, top_p and stop work as in the SDK. Tools and function calling work as in the SDK too, and response_format json_object / json_schema is validated. n greater than 1 and logprobs are rejected with 400 unsupported_parameter rather than being silently ignored.

Does Qwen3.8-Max support tool calling for coding agents?

Yes. Send tools and tool_choice as on the OpenAI API; the reply carries tool_calls (streamed as delta.tool_calls) and you return results as role: tool messages. Function calling is emulated by the api — the tools are rendered into the prompt and parsed back — so it works with OpenCode, Cline, Roo Code, Continue and any OpenAI SDK loop; tool definitions count as prompt tokens (about 40 per tool plus the schema). Configs and measured pass rates are on the coding agents page.

What am I charged when a request fails or I abort a stream?

Nothing when no tokens were produced: a rejected request, a 503 model_unavailable or a 502 upstream_error refunds the reservation in full. Credits are reserved for the estimated prompt plus min(max_tokens, 2,048) output tokens and settled to the actual usage when the call ends, so an unused reservation is released. If you close a stream early you pay only for the tokens generated up to that point.

What are the rate limits, and how do API keys work?

Each key has a requests-per-second limit (10 by default) and each account a cap on concurrent requests (20 by default); beyond either you get 429 rate_limited with a Retry-After header. Keys are created in the dashboard, shown once, and can carry an IP allowlist and an expiry date. One key works for every model in the catalogue, text and image alike.

Do you store my prompts and completions?

No. The generation record keeps a preview of the last user message (up to 200 characters), the number of messages, a SHA-256 hash of the messages, the token counts and the cost — never the messages or the reply themselves. GET /v1/generations/{id} returns that record so you can reconcile usage; the content exists only in your own process and at the model provider for the duration of the call.

Can I use Qwen3.8-Max's output commercially?

Yes. Under our terms of service you own the rights we can pass on to the text the model writes for you and may use it commercially, provided the prompts and the use comply with the terms and the model provider's usage policy. Read the terms before shipping anything customer-facing.

How do I switch from Qwen3.8-Max to another model?

Change the model field — nothing else in the request changes, and the same key and balance apply. GET /v1/models lists every id with its context window, limits and prices, and marks which ones accept enable_thinking, web_search and images.

Keep reading

• Qwen3 API pricing per 1M tokens: 7-Max, Max, Flash

  Live Qwen3 lineup on one API key: 7-Max, Max, 3.6-Flash prices per 1M tokens, context, and which model fits which job. Plus/Coder return once fixed upstream.

  7 September 2026

• Cline and Roo Code with an OpenAI-compatible provider

  Cline and Roo Code in VS Code with genmux as the OpenAI-compatible provider: the exact fields, which DeepSeek, Qwen3 and Doubao models to pick, vision, limits.

  7 September 2026

• Aider with Qwen3-Max and DeepSeek: OPENAI_API_BASE and done

  Run aider on Qwen3-Max or DeepSeek V4 through genmux: two environment variables, the openai/ model prefix, edit formats, and what a 200K-token session costs.

  7 September 2026

Other models

Same key, same request shape — switch by changing the model field.

• Doubao Seed 2.0 Pro API

  doubao-seed-2-pro

  ByteDance's flagship Doubao model with vision and web search — for complex reasoning and multimodal tasks.

  $1.063 in · $5.10 out per 1M tokens

• Doubao Seed 2.0 Code API

  doubao-seed-2-code

  Doubao's coding-tuned model with vision — for agentic coding, refactors and UI-from-screenshot tasks.

  $1.063 in · $5.10 out per 1M tokens

• Doubao Seed 2.0 Lite API

  doubao-seed-2-lite

  Mid-tier Doubao model with vision — general assistants and image-aware chat at a lower cost than Pro.

  $0.213 in · $1.063 out per 1M tokens

• Doubao Seed 2.0 Mini API

  doubao-seed-2-mini

  The cheapest Doubao model with vision — high-volume, latency-sensitive chat and simple image tasks.

  $0.027 in · $0.85 out per 1M tokens

• Kimi K3 API

  kimi-k3

  Moonshot's flagship Kimi model with a huge context window — for very long documents and multi-step agents.

  $2.463 in · $12.323 out per 1M tokens

• Claude Opus 5.5 API

  claude-opus-5-5

  Anthropic's top Claude Opus model — strong reasoning and agentic coding with a large context window for complex, multi-step work.

  $1.836 in · $9.18 out per 1M tokens

• Image models

  The same key and balance also buy image generation: Nano Banana, Seedream, Qwen-Image, FLUX and more, billed per image.

  • Nano Banana
  • Nano Banana Pro
  • Qwen-Image 2.0
  • Qwen-Image Max
  • Seedream 5.0
  • Wan 2.7 Image