> ## 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.

# MiniMax M2.7 API

> MiniMax M2.7 API on Venice: 198K context, $0.38 input and $1.50 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":"minimax-m2-7","name":"MiniMax M2.7","modality":"text","task":"chat","provider":"minimax","description":"MiniMax-M2.7 is a next-generation large language model designed for autonomous, real-world productivity with advanced agentic capabilities through multi-agent collaboration.","primary":"minimax-m27","variants":["minimax-m27"],"created":1773792000,"updated":1773792000,"privacy":["private"]},"models":[{"id":"minimax-m27","name":"MiniMax M2.7","type":"text","modality":"text","task":"chat","variant":"standard","provider":"minimax","created":1773792000,"description":"MiniMax-M2.7 is a next-generation large language model designed for autonomous, real-world productivity with advanced agentic capabilities through multi-agent collaboration.","privacy":"private","text":{"context":198000,"maxOutput":32768,"reasoning":{"supported":true,"effort":["none","low","medium","high"],"defaultEffort":"low"},"caps":{"tools":true,"webSearch":true,"code":true}},"pricing":{"input":0.375,"output":1.5,"cacheRead":0.06875,"blended":0.65625},"headline":{"value":0.65625,"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":"minimax-m2-7"}],"related":{"similar":[{"slug":"qwen-3-coder-480b-turbo","name":"Qwen 3 Coder 480B Turbo","provider":"alibaba","modality":"text","privacy":["private"],"created":1769472000,"headline":{"value":0.6375,"unit":"per 1M tokens","basis":"blended"},"variants":1},{"slug":"qwen3-vl-235b","name":"Qwen3 VL 235B","provider":"alibaba","modality":"text","privacy":["private"],"created":1768521600,"headline":{"value":0.6325,"unit":"per 1M tokens","basis":"blended"},"variants":1},{"slug":"qwen-3-5-35b-a3b","name":"Qwen 3.5 35B A3B","provider":"alibaba","modality":"text","privacy":["private"],"created":1771977600,"headline":{"value":0.546875,"unit":"per 1M tokens","basis":"blended"},"variants":1},{"slug":"deepseek-v4-1-flash","name":"DeepSeek V4.1 Flash","provider":"deepseek","modality":"text","privacy":["private"],"created":1788998400,"headline":{"value":0.65625,"unit":"per 1M tokens","basis":"blended"},"variants":1}],"versions":[{"slug":"minimax-m2-5","name":"MiniMax M2.5","provider":"minimax","modality":"text","privacy":["private"],"created":1770854400,"headline":{"value":0.44,"unit":"per 1M tokens","basis":"blended"},"variants":1}]},"providers":{"minimax":{"slug":"minimax","name":"MiniMax","logo":"/images/icons/models/minimax.svg"},"alibaba":{"slug":"alibaba","name":"Alibaba Qwen","logo":"/images/icons/models/qwen.svg"},"deepseek":{"slug":"deepseek","name":"DeepSeek","logo":"/images/icons/models/deepseek.svg"}},"faq":[{"q":"How much does the MiniMax M2.7 API cost?","a":"$0.38 per 1M input tokens and $1.50 per 1M output tokens, with cached input at $0.069 per 1M. Prices are in USD and can be paid in DIEM at parity."},{"q":"What is the MiniMax M2.7 model ID?","a":"Use `minimax-m27` as the `model` parameter."},{"q":"Is the MiniMax M2.7 API private?","a":"MiniMax M2.7 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 MiniMax M2.7?","a":"198K tokens of context, with up to 32K output tokens per response."},{"q":"What does MiniMax M2.7 support?","a":"MiniMax M2.7 supports function calling, reasoning, web search and prompt caching. Reasoning effort is adjustable with `reasoning_effort`: none, low, medium and high (default low)."},{"q":"Which endpoint does the MiniMax M2.7 API use?","a":"Call `POST /chat/completions`. `/responses` (Alpha) is also supported."}]}} />

<div className="vx-static">
  <Accordion title="Plain-text specification">
    # MiniMax M2.7 API

    MiniMax M2.7 is a large language model by MiniMax, available on the Venice API as `minimax-m27`. It runs privately, with zero data retention.

    MiniMax-M2.7 is a next-generation large language model designed for autonomous, real-world productivity with advanced agentic capabilities through multi-agent collaboration.

    ## MiniMax M2.7 API pricing

    | Model ID | Variant | Privacy | Price |
    | - | - | - | - |
    | `minimax-m27` | Standard | Private | $0.38 input / $1.50 output per 1M tokens |

    ## MiniMax M2.7 specifications

    | Spec | Value |
    | - | - |
    | Provider | MiniMax |
    | Released | Mar 18, 2026 |
    | Privacy | Private |
    | Context window | 198K tokens |
    | Max output | 32K tokens |
    | Reasoning effort | none, low, medium, high |

    ## How to use the MiniMax M2.7 API

    Send requests to `POST https://api.venice.ai/api/v1/chat/completions` with `"model": "minimax-m27"` 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": "minimax-m27",
        "messages": [{ "role": "user", "content": "Explain TEE attestation in two sentences." }],
        "reasoning_effort": "low"
      }'
    ```

    ## MiniMax M2.7 API FAQ

    ### How much does the MiniMax M2.7 API cost?

    $0.38 per 1M input tokens and $1.50 per 1M output tokens, with cached input at \$0.069 per 1M. Prices are in USD and can be paid in DIEM at parity.

    ### What is the MiniMax M2.7 model ID?

    Use `minimax-m27` as the `model` parameter.

    ### Is the MiniMax M2.7 API private?

    MiniMax M2.7 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 MiniMax M2.7?

    198K tokens of context, with up to 32K output tokens per response.

    ### What does MiniMax M2.7 support?

    MiniMax M2.7 supports function calling, reasoning, web search and prompt caching. Reasoning effort is adjustable with `reasoning_effort`: none, low, medium and high (default low).

    ### Which endpoint does the MiniMax M2.7 API use?

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

    ## Related models

    * [MiniMax M2.5 API](/models/minimax-m2-5): $0.27 input / $0.95 output per 1M tokens
    * [Qwen 3 Coder 480B Turbo API](/models/qwen-3-coder-480b-turbo): $0.35 input / $1.50 output per 1M tokens
    * [Qwen3 VL 235B API](/models/qwen3-vl-235b): $0.21 input / $1.90 output per 1M tokens
    * [Qwen 3.5 35B A3B API](/models/qwen-3-5-35b-a3b): $0.31 input / $1.25 output per 1M tokens
    * [DeepSeek V4.1 Flash API](/models/deepseek-v4-1-flash): $0.38 input / $1.50 output per 1M tokens
  </Accordion>
</div>


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