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Qwen: Qwen3.5 397B A17B

qwen/qwen3.5-397b-a17b

The Qwen3.5 series 397B-A17B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency.

Modalities
TextImageText
In / out price
$0.6875 / $4.375
per 1M tokens
Context
131K
131,072 tokens
Max output
131K
131,072 tokens
Released
Feb 16, 2026

Pricing

What you pay per 1 million tokens. Cached prompt tokens are billed at the cache-read price when the upstream reports them; otherwise at the input price.

Token typePrice / 1MPer token
Input (prompt)$0.6875$0.0000006875
Output (completion)$4.375$0.000004375
Cache read$0.13875$0.00000013875

Worked example: a request with 10,000 prompt tokens and 1,000 completion tokens costs $0.0113. While it runs, the request holds the maximum it could cost (based on max_tokens), then settles at the actual cost. How pricing works

Capabilities

Only capabilities we have verified end to end through the API are listed.

Features
StreamingTool callingReasoning
Input
TextImage
Output
Text
Context window
131,072 tokens
Max output
131,072 tokens

Performance on AI Models Router

Measured from real requests through our API in the last 7 days, all customers. A figure appears once it rests on at least 50 requests; until then we show how many there are, never an estimate.

Success rate
91.4%
140 requests
Cache-hit rate
0.0%
128 requests
Time to first token
Not enough data
8 of 50 requests
Output speed
Not enough data
8 of 50 requests
How each figure is measured
Success rate
Requests that returned a complete response, out of those that succeeded or failed on the supply side (an upstream error, timeout, capacity limit or a fault on our side). Requests you cancel, invalid requests and your own credit or key limits don't count. A request that fails before any output is never charged.
Cache-hit rate
Cached prompt tokens as a share of all prompt tokens, billed at the cache-read price.
Time to first token
Median time from your request reaching us to the first streamed token, reasoning included.
Output speed
Median output tokens per second after the first token, on streamed replies of at least 50 tokens.

Updated every 10 minutes · last computed 19:55 UTC

Usage · last 30 days

Requests
144
Tokens
49K
19K in · 30K out

Requests per day

Requests by day
DayRequests
2026-10-060
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2026-11-0168
2026-11-0266
2026-11-030
2026-11-040

API

  1. 1Get your API key

    Create a key on the API keys page and export it:

    Shell
    export AMR_API_KEY=sk-amr-v1-...
  2. 2Make your first request

    Any OpenAI-compatible client works. The model slug is qwen/qwen3.5-397b-a17b.

    from openai import OpenAI
    
    client = OpenAI(
        base_url="https://api.aimodelsrouter.ai/v1",
        api_key="sk-amr-v1-...",  # or os.environ["AMR_API_KEY"]
    )
    
    completion = client.chat.completions.create(
        model="qwen/qwen3.5-397b-a17b",
        messages=[{"role": "user", "content": "What is the meaning of life?"}],
    )
    print(completion.choices[0].message.content)
  3. 3Stream the response

    Set stream: true. The final chunk carries usage and cost.

    stream = client.chat.completions.create(
        model="qwen/qwen3.5-397b-a17b",
        messages=[{"role": "user", "content": "Write a haiku about routers."}],
        stream=True,
        stream_options={"include_usage": 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:  # final chunk: one choice with an empty delta, usage present
            print("\ncost (USD):", chunk.usage.cost)