What can the MiMo-V2-Omni API understand besides text?

MiMo-V2-Omni is built for image, video, audio, and undfied perceptual system rather than separate modality add-ons, which makes it a better fit for multimodal agents than a text-only LLM.

Can MiMo-V2-Omni API process audio and video together?

Yes. the model supports native audio-video joint input for video comprehension, so it can reason over what is happening on screen and in the soundtrack at the same time.

How long of an audio file can MiMo-V2-Omni API handle?

MiMo-V2-Omni supports continuous audio understanding beyond 10 hours. That is a strong signal that it is meant for long-form audio analysis rather than short clip transcription only.

When should I use MiMo-V2-Omni API instead of MiMo-V2-Pro?

Use MiMo-V2-Omni when the job depends on multimodal perception: screens, videos, voice, or audio-visual workflow mostly agentic text work and you want the largest flagship context window, which Xiaomi says reaches 1M tokens.

Does MiMo-V2-Omni API support structured tool?

Yes. MiMo-V2-Omni natively supports structured tool calling, function execution, and UI grounding, which is exactly what you want for agent automation.

Is MiMo-V2-Omni API good for browser automation and real-world agents?

Yes. Xiaomi’s demos show it scanning shopping adviceing on JD.com, and completing a TikTok upload workflow through OpenClaw. That makes it a strong fit for browser agents, workflow automation, and UI-driven tasks.

Rimelig mimo-v2-omni API | text-to-text

MiMo-V2-Omni Overview

MiMo-V2-Omni is Xiaomi MiMo’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

Item	MiMo-V2-Omni
Provider	Xiaomi MiMo
Model family	MiMo-V2
Modality	Image, video, audio, text
Output type	Text
Native audio support	Yes
Native audio-video joint input	Yes
Structured tool calling	Yes
Function execution	Yes
UI grounding	Yes
Long audio handling	Over 10 hours continuous audio understanding
Release date	2026-03-18
Public numeric context length	Not 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

Model	Core strength	Context / scale	Best fit
MiMo-V2-Omni	Multimodal perception + agent action	Public context length not stated on the Omni page	Audio, image, video, UI, and browser agents
MiMo-V2-Pro	Largest flagship agent model	Up to 1M-token context; 1T+ params, 42B active	Heavy agent orchestration and long-horizon work
MiMo-V2-Flash	Fast reasoning and coding	256K context; 309B total, 15B active	Efficient 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.

Priser for mimo-v2-omni

Utforsk konkurransedyktige priser for mimo-v2-omni, designet for å passe ulike budsjetter og bruksbehov. Våre fleksible planer sikrer at du bare betaler for det du bruker, noe som gjør det enkelt å skalere etter hvert som kravene dine vokser. Oppdag hvordan mimo-v2-omni kan forbedre prosjektene dine samtidig som kostnadene holdes håndterbare.

Comet Price (USD / M Tokens)	Official Price (USD / M Tokens)	Discount
Inndata:$0.32/M Utdata:$1.6/M	Inndata:$0.4/M Utdata:$2/M	-20%

Eksempelkode og API for mimo-v2-omni

Få tilgang til omfattende eksempelkode og API-ressurser for mimo-v2-omni for å effektivisere integreringsprosessen din. Vår detaljerte dokumentasjon gir trinn-for-trinn-veiledning som hjelper deg med å utnytte det fulle potensialet til mimo-v2-omni i prosjektene dine.

# 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}`);
  }
}