> ## 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 35B A3B API

> Qwen 3.5 35B A3B API on Venice: 250K context, $0.31 input and $1.25 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-3-5-35b-a3b","name":"Qwen 3.5 35B A3B","modality":"text","task":"chat","provider":"alibaba","description":"Qwen 3.5 35B A3B is a highly efficient MoE model with 35B total parameters and only 3B active parameters. It surpasses the larger Qwen3-235B-A22B while being 6.7x smaller, excelling at reasoning, coding, and general knowledge tasks.","primary":"qwen3-5-35b-a3b","variants":["qwen3-5-35b-a3b"],"created":1771977600,"updated":1771977600,"privacy":["private"],"openWeights":true},"models":[{"id":"qwen3-5-35b-a3b","name":"Qwen 3.5 35B A3B","type":"text","modality":"text","task":"chat","variant":"standard","provider":"alibaba","created":1771977600,"description":"Qwen 3.5 35B A3B is a highly efficient MoE model with 35B total parameters and only 3B active parameters. It surpasses the larger Qwen3-235B-A22B while being 6.7x smaller, excelling at reasoning, coding, and general knowledge tasks.","source":"https://huggingface.co/Qwen/Qwen3.5-35B-A3B","privacy":"private","openWeights":true,"text":{"context":256000,"maxOutput":16384,"reasoning":{"supported":true,"effort":["none","low","medium","high"],"defaultEffort":"low"},"caps":{"tools":true,"structured":true,"vision":true,"maxImages":10,"videoInput":true,"webSearch":true,"logprobs":true,"code":true},"sampling":{"temperature":1,"top_p":0.95,"repetition_penalty":1}},"pricing":{"input":0.3125,"output":1.25,"cacheRead":0.15625,"blended":0.546875},"headline":{"value":0.546875,"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-3-5-35b-a3b"}],"related":{"similar":[{"slug":"mercury-2","name":"Mercury 2","provider":"inception","modality":"text","privacy":["anonymized"],"created":1771545600,"headline":{"value":0.46875,"unit":"per 1M tokens","basis":"blended"},"variants":1},{"slug":"minimax-m3-preview","name":"MiniMax M3 Preview","provider":"minimax","modality":"text","privacy":["private"],"created":1781222400,"headline":{"value":0.525,"unit":"per 1M tokens","basis":"blended"},"variants":1},{"slug":"minimax-m2-7","name":"MiniMax M2.7","provider":"minimax","modality":"text","privacy":["private"],"created":1773792000,"headline":{"value":0.65625,"unit":"per 1M tokens","basis":"blended"},"variants":1},{"slug":"gpt-5-6-luna","name":"GPT-5.6 Luna","provider":"openai","modality":"text","privacy":["anonymized"],"created":1783555200,"headline":{"value":0.5625,"unit":"per 1M tokens","basis":"blended"},"variants":1}],"versions":[{"slug":"qwen-3-6-35b-a3b","name":"Qwen 3.6 35B A3B","provider":"alibaba","modality":"text","privacy":["private","e2ee"],"created":1779235200,"headline":{"value":0.325,"unit":"per 1M tokens","basis":"blended"},"variants":2}]},"providers":{"alibaba":{"slug":"alibaba","name":"Alibaba Qwen","logo":"/images/icons/models/qwen.svg"},"inception":{"slug":"inception","name":"Inception","logo":"/images/icons/models/inception.svg"},"minimax":{"slug":"minimax","name":"MiniMax","logo":"/images/icons/models/minimax.svg"},"openai":{"slug":"openai","name":"OpenAI","logo":"/images/icons/models/openai.svg"}},"faq":[{"q":"How much does the Qwen 3.5 35B A3B API cost?","a":"$0.31 per 1M input tokens and $1.25 per 1M output tokens, with cached input at $0.16 per 1M. Prices are in USD and can be paid in DIEM at parity."},{"q":"What is the Qwen 3.5 35B A3B model ID?","a":"Use `qwen3-5-35b-a3b` as the `model` parameter."},{"q":"Is the Qwen 3.5 35B A3B API private?","a":"Qwen 3.5 35B A3B 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 35B A3B?","a":"250K tokens of context, with up to 16K output tokens per response."},{"q":"What does Qwen 3.5 35B A3B support?","a":"Qwen 3.5 35B A3B supports function calling, structured outputs, reasoning, image input, web search and prompt caching. Reasoning effort is adjustable with `reasoning_effort`: none, low, medium and high (default low)."},{"q":"Which endpoint does the Qwen 3.5 35B A3B API use?","a":"Call `POST /chat/completions`. `/responses` (Alpha) is also supported."}]}} />

<div className="vx-static">
  <Accordion title="Plain-text specification">
    # Qwen 3.5 35B A3B API

    Qwen 3.5 35B A3B is a large language model by Alibaba Qwen, available on the Venice API as `qwen3-5-35b-a3b`. It runs privately, with zero data retention.

    Qwen 3.5 35B A3B is a highly efficient MoE model with 35B total parameters and only 3B active parameters. It surpasses the larger Qwen3-235B-A22B while being 6.7x smaller, excelling at reasoning, coding, and general knowledge tasks.

    ## Qwen 3.5 35B A3B API pricing

    | Model ID | Variant | Privacy | Price |
    | - | - | - | - |
    | `qwen3-5-35b-a3b` | Standard | Private | $0.31 input / $1.25 output per 1M tokens |

    ## Qwen 3.5 35B A3B specifications

    | Spec | Value |
    | - | - |
    | Provider | Alibaba Qwen |
    | Released | Feb 25, 2026 |
    | Privacy | Private |
    | Open weights | Yes |
    | Context window | 250K tokens |
    | Max output | 16K tokens |
    | Reasoning effort | none, low, medium, high |

    ## How to use the Qwen 3.5 35B A3B API

    Send requests to `POST https://api.venice.ai/api/v1/chat/completions` with `"model": "qwen3-5-35b-a3b"` 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": "qwen3-5-35b-a3b",
        "messages": [{ "role": "user", "content": "Explain TEE attestation in two sentences." }],
        "reasoning_effort": "low"
      }'
    ```

    ## Qwen 3.5 35B A3B API FAQ

    ### How much does the Qwen 3.5 35B A3B API cost?

    $0.31 per 1M input tokens and $1.25 per 1M output tokens, with cached input at \$0.16 per 1M. Prices are in USD and can be paid in DIEM at parity.

    ### What is the Qwen 3.5 35B A3B model ID?

    Use `qwen3-5-35b-a3b` as the `model` parameter.

    ### Is the Qwen 3.5 35B A3B API private?

    Qwen 3.5 35B A3B 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 35B A3B?

    250K tokens of context, with up to 16K output tokens per response.

    ### What does Qwen 3.5 35B A3B support?

    Qwen 3.5 35B A3B supports function calling, structured outputs, reasoning, image input, web search and prompt caching. Reasoning effort is adjustable with `reasoning_effort`: none, low, medium and high (default low).

    ### Which endpoint does the Qwen 3.5 35B A3B API use?

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

    ## Related models

    * [Qwen 3.6 35B A3B API](/models/qwen-3-6-35b-a3b): $0.10 input / $1.00 output per 1M tokens
    * [Mercury 2 API](/models/mercury-2): $0.31 input / $0.94 output per 1M tokens
    * [MiniMax M3 Preview API](/models/minimax-m3-preview): $0.30 input / $1.20 output per 1M tokens
    * [MiniMax M2.7 API](/models/minimax-m2-7): $0.38 input / $1.50 output per 1M tokens
    * [GPT-5.6 Luna API](/models/gpt-5-6-luna): $0.25 input / $1.50 output per 1M tokens
  </Accordion>
</div>


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