> ## Documentation Index
> Fetch the complete documentation index at: https://veniceai-feat-models-redesign.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Qwen 3.5 9B API

> Qwen 3.5 9B API on Venice: 250K context, $0.10 input and $0.15 output per 1M tokens. Private, with zero data retention. Pricing, specs and code examples.

export const HubMount = ({view, children, ...props}) => {
  const [hub, setHub] = useState(null);
  const [failed, setFailed] = useState(false);
  useEffect(() => {
    let alive = true;
    const w = window;
    if (!w.__veniceModelHub) {
      const urls = w.location.hostname === 'localhost' ? ['http://localhost:3333/data/model-hub.bundle.json', '/data/model-hub.bundle.json'] : ['/data/model-hub.bundle.json'];
      const load = i => fetch(urls[i], {
        cache: 'no-cache'
      }).then(res => {
        if (!res.ok) throw new Error(`bundle ${res.status}`);
        return res.json();
      }).catch(err => i + 1 < urls.length ? load(i + 1) : Promise.reject(err));
      const Frag = <></>.type;
      const h = (type, props, ...kids) => {
        const T = type;
        const {key, ...rest} = props || ({});
        if (!kids.length) return <T key={key} {...rest} />;
        if (kids.length === 1) return <T key={key} {...rest}>{kids[0]}</T>;
        return <T key={key} {...rest}>{kids.map((kid, i) => <Frag key={i}>{kid}</Frag>)}</T>;
      };
      w.__veniceModelHub = load(0).then(bundle => new Function(`return (${bundle.code})`)()({
        h,
        Fragment: Frag,
        useState,
        useEffect,
        useRef,
        useMemo,
        useCallback
      }));
    }
    w.__veniceModelHub.then(instance => {
      if (alive) setHub(instance);
    }).catch(() => {
      w.__veniceModelHub = null;
      if (alive) setFailed(true);
    });
    return () => {
      alive = false;
    };
  }, []);
  const View = hub ? hub[view] : null;
  if (View) return <View {...props}>{children}</View>;
  if (failed) {
    return <div className="vx-mount is-failed">
        <p className="vx-mount-note">The interactive model catalog could not load. The full data is below.</p>
        {children}
      </div>;
  }
  return <div className="vx-mount" aria-busy="true">
      <div className="vx-mount-skeleton" aria-hidden="true"><span /><span /><span /></div>
      <div className="vx-mount-source">{children}</div>
    </div>;
};

<HubMount view="ModelPage" data={{"family":{"slug":"qwen-3-5-9b","name":"Qwen 3.5 9B","modality":"text","task":"chat","provider":"alibaba","description":"A 9B dense model with 262K native context window (extendable to 1M). Features Gated DeltaNet hybrid attention architecture for efficient long-context processing. Supports 201 languages, thinking/reasoning mode, and function calling.","primary":"qwen3-5-9b","variants":["qwen3-5-9b"],"created":1772668800,"updated":1772668800,"privacy":["private"],"openWeights":true},"models":[{"id":"qwen3-5-9b","name":"Qwen 3.5 9B","type":"text","modality":"text","task":"chat","variant":"standard","provider":"alibaba","created":1772668800,"description":"A 9B dense model with 262K native context window (extendable to 1M). Features Gated DeltaNet hybrid attention architecture for efficient long-context processing. Supports 201 languages, thinking/reasoning mode, and function calling.","source":"https://huggingface.co/Qwen/Qwen3.5-9B","privacy":"private","openWeights":true,"text":{"context":256000,"maxOutput":32768,"quantization":"fp8","reasoning":{"supported":true,"effort":["none","low","medium","high"],"defaultEffort":"low"},"caps":{"tools":true,"structured":true,"vision":true,"maxImages":10,"webSearch":true,"logprobs":true}},"pricing":{"input":0.1,"output":0.15,"blended":0.1125},"headline":{"value":0.1125,"unit":"per 1M tokens","basis":"blended"},"endpoints":[{"id":"chat","method":"POST","path":"/chat/completions","name":"Chat Completions","status":"stable","recommended":true},{"id":"responses","method":"POST","path":"/responses","name":"Responses","status":"alpha"}],"family":"qwen-3-5-9b"}],"related":{"similar":[{"slug":"nvidia-nemotron-3-nano-30b","name":"NVIDIA Nemotron 3 Nano 30B","provider":"nvidia","modality":"text","privacy":["private"],"created":1769472000,"headline":{"value":0.13125,"unit":"per 1M tokens","basis":"blended"},"variants":1},{"slug":"mistral-small-3-2-24b-instruct","name":"Mistral Small 3.2 24B Instruct","provider":"mistral","modality":"text","privacy":["private"],"created":1768435200,"headline":{"value":0.132813,"unit":"per 1M tokens","basis":"blended"},"variants":1},{"slug":"openai-gpt-oss-120b","name":"OpenAI GPT OSS 120B","provider":"openai","modality":"text","privacy":["private","e2ee"],"created":1762387200,"headline":{"value":0.1275,"unit":"per 1M tokens","basis":"blended"},"variants":2},{"slug":"glm-4-7-flash","name":"GLM 4.7 Flash","provider":"zai","modality":"text","privacy":["private"],"created":1769644800,"headline":{"value":0.145,"unit":"per 1M tokens","basis":"blended"},"variants":1}],"versions":[{"slug":"qwen-3-8-27b","name":"Qwen 3.8 27B","provider":"alibaba","modality":"text","privacy":["private","e2ee"],"created":1786924800,"headline":{"value":1.1375,"unit":"per 1M tokens","basis":"blended"},"variants":2},{"slug":"qwen-3-6-27b","name":"Qwen 3.6 27B","provider":"alibaba","modality":"text","privacy":["private"],"created":1776988800,"headline":{"value":1.05625,"unit":"per 1M tokens","basis":"blended"},"variants":1},{"slug":"qwen-2-5-7b","name":"Qwen 2.5 7B","provider":"alibaba","modality":"text","privacy":["e2ee"],"created":1773792000,"headline":{"value":0.07,"unit":"per 1M tokens","basis":"blended"},"variants":1},{"slug":"qwen-3-5-397b","name":"Qwen 3.5 397B","provider":"alibaba","modality":"text","privacy":["anonymized"],"created":1771200000,"headline":{"value":1.6875,"unit":"per 1M tokens","basis":"blended"},"variants":1}]},"providers":{"alibaba":{"slug":"alibaba","name":"Alibaba Qwen","logo":"/images/icons/models/qwen.svg"},"nvidia":{"slug":"nvidia","name":"NVIDIA","logo":"/images/icons/models/nvidia.svg"},"mistral":{"slug":"mistral","name":"Mistral AI","logo":"/images/icons/models/mistral.svg"},"openai":{"slug":"openai","name":"OpenAI","logo":"/images/icons/models/openai.svg"},"zai":{"slug":"zai","name":"Z.ai","logo":"/images/icons/models/Zhipu.svg"}},"faq":[{"q":"How much does the Qwen 3.5 9B API cost?","a":"$0.10 per 1M input tokens and $0.15 per 1M output tokens. Prices are in USD and can be paid in DIEM at parity."},{"q":"What is the Qwen 3.5 9B model ID?","a":"Use `qwen3-5-9b` as the `model` parameter."},{"q":"Is the Qwen 3.5 9B API private?","a":"Qwen 3.5 9B is private: requests run on infrastructure Venice controls with zero data retention, and prompts and outputs are never stored or used for training."},{"q":"What is the context window of Qwen 3.5 9B?","a":"250K tokens of context, with up to 32K output tokens per response."},{"q":"What does Qwen 3.5 9B support?","a":"Qwen 3.5 9B supports function calling, structured outputs, reasoning, image input and web search. Reasoning effort is adjustable with `reasoning_effort`: none, low, medium and high (default low)."},{"q":"Which endpoint does the Qwen 3.5 9B API use?","a":"Call `POST /chat/completions`. `/responses` (Alpha) is also supported."}]}} />

<div className="vx-static">
  <Accordion title="Plain-text specification">
    # Qwen 3.5 9B API

    Qwen 3.5 9B is a large language model by Alibaba Qwen, available on the Venice API as `qwen3-5-9b`. It runs privately, with zero data retention.

    A 9B dense model with 262K native context window (extendable to 1M). Features Gated DeltaNet hybrid attention architecture for efficient long-context processing. Supports 201 languages, thinking/reasoning mode, and function calling.

    ## Qwen 3.5 9B API pricing

    | Model ID | Variant | Privacy | Price |
    | - | - | - | - |
    | `qwen3-5-9b` | Standard | Private | $0.10 input / $0.15 output per 1M tokens |

    ## Qwen 3.5 9B specifications

    | Spec | Value |
    | - | - |
    | Provider | Alibaba Qwen |
    | Released | Mar 5, 2026 |
    | Privacy | Private |
    | Open weights | Yes |
    | Context window | 250K tokens |
    | Max output | 32K tokens |
    | Reasoning effort | none, low, medium, high |
    | Served precision | FP8 |

    ## How to use the Qwen 3.5 9B API

    Send requests to `POST https://api.venice.ai/api/v1/chat/completions` with `"model": "qwen3-5-9b"` and your API key.

    ```bash theme={null}
    curl https://api.venice.ai/api/v1/chat/completions \
      -H "Authorization: Bearer $VENICE_API_KEY" \
      -H "Content-Type: application/json" \
      -d '{
        "model": "qwen3-5-9b",
        "messages": [{ "role": "user", "content": "Explain TEE attestation in two sentences." }],
        "reasoning_effort": "low"
      }'
    ```

    ## Qwen 3.5 9B API FAQ

    ### How much does the Qwen 3.5 9B API cost?

    $0.10 per 1M input tokens and $0.15 per 1M output tokens. Prices are in USD and can be paid in DIEM at parity.

    ### What is the Qwen 3.5 9B model ID?

    Use `qwen3-5-9b` as the `model` parameter.

    ### Is the Qwen 3.5 9B API private?

    Qwen 3.5 9B is private: requests run on infrastructure Venice controls with zero data retention, and prompts and outputs are never stored or used for training.

    ### What is the context window of Qwen 3.5 9B?

    250K tokens of context, with up to 32K output tokens per response.

    ### What does Qwen 3.5 9B support?

    Qwen 3.5 9B supports function calling, structured outputs, reasoning, image input and web search. Reasoning effort is adjustable with `reasoning_effort`: none, low, medium and high (default low).

    ### Which endpoint does the Qwen 3.5 9B API use?

    Call `POST /chat/completions`. `/responses` (Alpha) is also supported.

    ## Related models

    * [Qwen 3.8 27B API](/models/qwen-3-8-27b): $0.45 input / $3.20 output per 1M tokens
    * [Qwen 3.6 27B API](/models/qwen-3-6-27b): $0.33 input / $3.25 output per 1M tokens
    * [Qwen 2.5 7B API](/models/qwen-2-5-7b): $0.05 input / $0.13 output per 1M tokens
    * [Qwen 3.5 397B API](/models/qwen-3-5-397b): $0.75 input / $4.50 output per 1M tokens
    * [NVIDIA Nemotron 3 Nano 30B API](/models/nvidia-nemotron-3-nano-30b): $0.07 input / $0.30 output per 1M tokens
    * [Mistral Small 3.2 24B Instruct API](/models/mistral-small-3-2-24b-instruct): $0.09 input / $0.25 output per 1M tokens
    * [OpenAI GPT OSS 120B API](/models/openai-gpt-oss-120b): $0.07 input / $0.30 output per 1M tokens
    * [GLM 4.7 Flash API](/models/glm-4-7-flash): $0.06 input / $0.40 output per 1M tokens
  </Accordion>
</div>


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