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mimo-v2-omni

ฤแบงu vร o:$0.32/M
ฤแบงu ra:$1.6/M
Ngร y phรกt hร nh:Mar 24, 2026

MiMo-V2-Omni is a frontier omni-modal model that natively processes image, video, and audio inputs within a unified architecture. It combines strong multimodal perception with agentic capability - visual grounding, multi-step planning, tool use, and code execution - making it well-suited for complex real-world tasks that span modalities. 256K context window.

Mแป›i
Sแปญ dแปฅng thฦฐฦกng mแบกi

Playground cho mimo-v2-omni

Khรกm phรก Playground cแปงa mimo-v2-omni โ€” mรดi trฦฐแปng tฦฐฦกng tรกc ฤ‘แปƒ kiแปƒm tra mรด hรฌnh vร  chแบกy truy vแบฅn theo thแปi gian thแปฑc. Thแปญ prompts, ฤ‘iแปu chแป‰nh tham sแป‘ vร  lแบทp lแบกi ngay lแบญp tแปฉc ฤ‘แปƒ tฤƒng tแป‘c phรกt triแปƒn vร  xรกc thแปฑc cรกc trฦฐแปng hแปฃp sแปญ dแปฅng.

MiMo-V2-Omni Overview

MiMo-V2-Omni is Xiaomi MiMoโ€™s omni foundation model for the API platform, built to see, hear, read, and act in the same workflow. Xiaomi positions it as a multimodal agent model that combines image, video, audio, and text understanding with structured tool calling, function execution, and UI grounding.

Technical specifications

ItemMiMo-V2-Omni
ProviderXiaomi MiMo
Model familyMiMo-V2
ModalityImage, video, audio, text
Output typeText
Native audio supportYes
Native audio-video joint inputYes
Structured tool callingYes
Function executionYes
UI groundingYes
Long audio handlingOver 10 hours continuous audio understanding
Release date2026-03-18
Public numeric context lengthNot stated on the official Omni page

What is MiMo-V2-Omni?

MiMo-V2-Omni is designed for agentic systems that need perception and action in one model. Xiaomi says the model fuses dedicated image, video, and audio encoders into one shared backbone, then trains it to anticipate what should happen next rather than only describe what is already visible.

Main features of MiMo-V2-Omni

  • Unified multimodal perception: image, video, audio, and text are handled as one perceptual stream rather than separate add-ons.
  • Agent-ready outputs: the model natively supports structured tool calling, function execution, and UI grounding for real agent frameworks.
  • Long-form audio understanding: Xiaomi claims it can handle continuous audio longer than 10 hours, which is unusually strong for a general omni model.
  • Native audio-video reasoning: the official page highlights joint audio-video input for video comprehension instead of a text-only transcript pipeline.
  • Browser and workflow execution: Xiaomi demonstrates end-to-end browser shopping and TikTok upload flows using MiMo-V2-Omni plus OpenClaw.
  • Perception-to-action framing: the model is trained to connect what it sees with what it should do next, which is the core difference between a demo model and an agentic model.

Benchmark performance

mimo-v2-omni

It clearly states that Omni exceeds Gemini 3 Pro on audio understanding, exceeds Claude Opus 4.6 on image understanding, and performs on par with the strongest reasoning models on agentic productivity benchmarks.

MiMo-V2-Omni vs MiMo-V2-Pro vs MiMo-V2-Flash

ModelCore strengthContext / scaleBest fit
MiMo-V2-OmniMultimodal perception + agent actionPublic context length not stated on the Omni pageAudio, image, video, UI, and browser agents
MiMo-V2-ProLargest flagship agent modelUp to 1M-token context; 1T+ params, 42B activeHeavy agent orchestration and long-horizon work
MiMo-V2-FlashFast reasoning and coding256K context; 309B total, 15B activeEfficient reasoning, coding, and high-throughput agent tasks

Best use cases

MiMo-V2-Omni is the right pick when your workflow depends on non-text inputs or outputs: screen understanding, voice and audio analysis, video review, browser automation, multimodal assistants, and robotics-style agent loops. If your workload is mostly text-only and you care more about raw speed or maximum context, the sibling Pro and Flash models are the more obvious alternatives.

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Giรก cแบฃ cho mimo-v2-omni

Khรกm phรก mแปฉc giรก cแบกnh tranh cho mimo-v2-omni, ฤ‘ฦฐแปฃc thiแบฟt kแบฟ ฤ‘แปƒ phรน hแปฃp vแป›i nhiแปu ngรขn sรกch vร  nhu cแบงu sแปญ dแปฅng khรกc nhau. Cรกc gรณi linh hoแบกt cแปงa chรบng tรดi ฤ‘แบฃm bแบฃo bแบกn chแป‰ trแบฃ tiแปn cho nhแปฏng gรฌ bแบกn sแปญ dแปฅng, giรบp dแป… dร ng mแปŸ rแป™ng quy mรด khi yรชu cแบงu cแปงa bแบกn tฤƒng lรชn. Khรกm phรก cรกch mimo-v2-omni cรณ thแปƒ nรขng cao cรกc dแปฑ รกn cแปงa bแบกn trong khi vแบซn kiแปƒm soรกt ฤ‘ฦฐแปฃc chi phรญ.

Comet Price (USD / M Tokens)Official Price (USD / M Tokens)Discount
ฤแบงu vร o:$0.32/M
ฤแบงu ra:$1.6/M
ฤแบงu vร o:$0.4/M
ฤแบงu ra:$2/M
-20%

Mรฃ mแบซu vร  API cho mimo-v2-omni

Truy cแบญp mรฃ mแบซu toร n diแป‡n vร  tร i nguyรชn API cho mimo-v2-omni ฤ‘แปƒ tแป‘i ฦฐu hรณa quy trรฌnh tรญch hแปฃp cแปงa bแบกn. Tร i liแป‡u chi tiแบฟt cแปงa chรบng tรดi cung cแบฅp hฦฐแป›ng dแบซn tแปซng bฦฐแป›c, giรบp bแบกn khai thรกc toร n bแป™ tiแปm nฤƒng cแปงa mimo-v2-omni trong cรกc dแปฑ รกn cแปงa mรฌnh.

# Get your CometAPI key from https://api.cometapi.com/console/token
# Export it as: export COMETAPI_KEY="your-key-here"

curl https://api.cometapi.com/v1/chat/completions \
  -H "Authorization: Bearer $COMETAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "mimo-v2-omni",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "Who is Lei Jun?"}
    ],
    "tools": [{"type": "web_search", "force_search": true, "max_keyword": 3, "limit": 1}],
    "thinking": {"type": "disabled"}
  }'

cURL Code Example

# Get your CometAPI key from https://api.cometapi.com/console/token
# Export it as: export COMETAPI_KEY="your-key-here"

curl https://api.cometapi.com/v1/chat/completions \
  -H "Authorization: Bearer $COMETAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "mimo-v2-omni",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "Who is Lei Jun?"}
    ],
    "tools": [{"type": "web_search", "force_search": true, "max_keyword": 3, "limit": 1}],
    "thinking": {"type": "disabled"}
  }'

Python Code Example

from openai import OpenAI
import os

# Get your CometAPI key from https://api.cometapi.com/console/token, and paste it here
COMETAPI_KEY = os.environ.get("COMETAPI_KEY") or "<YOUR_COMETAPI_KEY>"

client = OpenAI(api_key=COMETAPI_KEY, base_url="https://api.cometapi.com/v1")

# mimo-v2-omni: built-in web_search tool (pass as top-level tools param)
completion = client.chat.completions.create(
    model="mimo-v2-omni",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Who is Lei Jun?"},
    ],
    tools=[{"type": "web_search", "force_search": True, "max_keyword": 3, "limit": 1}],
    tool_choice="auto",
    extra_body={"thinking": {"type": "disabled"}},
)

msg = completion.choices[0].message
if msg.content:
    print(msg.content)

# annotations are populated when web_search runs (content may be null on search-only responses)
raw = completion.model_dump()
annotations = raw["choices"][0]["message"].get("annotations") or []
if annotations:
    print("\n--- Sources ---")
    for ann in annotations:
        c = ann.get("url_citation") or {}
        print(f"[{c.get('title')}] {c.get('url')}")

JavaScript Code Example

// Get your CometAPI key from https://api.cometapi.com/console/token, and paste it here
const api_key = process.env.COMETAPI_KEY || "<YOUR_COMETAPI_KEY>";

// mimo-v2-omni: use fetch for web_search (non-standard tool type unsupported by openai SDK)
const resp = await fetch("https://api.cometapi.com/v1/chat/completions", {
  method: "POST",
  headers: { Authorization: `Bearer ${api_key}`, "Content-Type": "application/json" },
  body: JSON.stringify({
    model: "mimo-v2-omni",
    messages: [
      { role: "system", content: "You are a helpful assistant." },
      { role: "user", content: "Who is Lei Jun?" },
    ],
    tools: [{ type: "web_search", force_search: true, max_keyword: 3, limit: 1 }],
    tool_choice: "auto",
    thinking: { type: "disabled" },
  }),
});

const data = await resp.json();
const msg = data.choices[0].message;
if (msg.content) console.log(msg.content);

const annotations = msg.annotations ?? [];
if (annotations.length) {
  console.log("\n--- Sources ---");
  for (const ann of annotations) {
    const c = ann.url_citation ?? {};
    console.log(`[${c.title}] ${c.url}`);
  }
}