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Qwen-Omni

Use Qwen-Omni over HTTP to understand text, images, audio, and video. Choose Qwen3.8-Omni-Flash for text analysis and Qwen3.5-Omni for speech output.

Getting started

Choose an example by output type: Qwen3.8-Omni-Flash for text analysis, or Qwen3.5-Omni for speech generation.
  • Text output (Qwen3.8-Omni-Flash)
  • Audio output (Qwen3.5-Omni)
Prerequisites
Install the dependencies before you run the code: pip install openai (Python) or npm install openai (Node.js).
Set the DASHSCOPE_BASE_URL environment variable to the base_url for your workspace from the Chat Completions endpoints. Set AUDIO_URL to an accessible WAV audio URL. Use an API key from the selected region.In your terminal, install the dependency and set the following environment variables. Replace the endpoint and audio URL with your values; configure DASHSCOPE_API_KEY using the instructions linked above.
python3 -m pip install openai
# The China (Beijing) region looks like the following. Replace {WorkspaceId} with your Bailian workspace ID. URLs vary by region.
export DASHSCOPE_BASE_URL="https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1"
export AUDIO_URL="<accessible-wav-url>"
import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["DASHSCOPE_API_KEY"],
    base_url=os.environ["DASHSCOPE_BASE_URL"],
)
completion = client.chat.completions.create(
    model="qwen3.8-omni-flash",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "Summarize the main points of this recording."},
            {"type": "input_audio", "input_audio": {
                "data": os.environ["AUDIO_URL"],
                "format": "wav",
            }},
        ],
    }],
    stream=True,
    stream_options={"include_usage": True},
)
output_section = None
for chunk in completion:
    if not chunk.choices:
        if chunk.usage:
            print("\nUsage:", chunk.usage)
        continue
    delta = chunk.choices[0].delta
    if getattr(delta, "reasoning_content", None):
        if output_section != "thinking":
            print("\nThinking:")
            output_section = "thinking"
        print(delta.reasoning_content, end="", flush=True)
    if delta.content:
        if output_section != "answer":
            print("\nAnswer:")
            output_section = "answer"
        print(delta.content, end="", flush=True)
After you run the Python or Node.js code, the console prints the thinking process, then the model reply, and finally the usage. Qwen3.8-Omni-Flash has thinking enabled by default; the response contains a reasoning_content field, and the thinking part is billed as part of usage.completion_tokens_details.reasoning_tokens.
Thinking:
The user wants a summary of the main points from the provided audio recording.
(thinking content truncated; excerpt from an actual response)
Answer:
The recording features a speaker describing a picture of a beach scene. The main points are:

*   **Subject:** A woman is playing with a dog on the beach.
*   **Setting:** The background shows waves crashing against the sand.
*   **Time of Day:** The sky is whitish, suggesting it is either sunrise or sunset.
*   **Closing:** The speaker invites the listener to ask any further questions they might have.
Usage: CompletionUsage(completion_tokens=554, prompt_tokens=176, total_tokens=730, completion_tokens_details=CompletionTokensDetails(audio_tokens=None, reasoning_tokens=458, text_tokens=96), prompt_tokens_details=PromptTokensDetails(audio_tokens=114, cached_tokens=0, text_tokens=62))
When you run the HTTP code, the thinking process and reply are returned as streamed chunks, followed by a final usage chunk (the usage field contains reasoning_tokens).
data: {"id":"chatcmpl-a82cb662-a901-9b0d-ae12-b5b2cced8781","object":"chat.completion.chunk","created":1791876878,"model":"qwen3.8-omni-flash","choices":[{"index":0,"delta":{"reasoning_content":"The","content":""},"logprobs":null,"finish_reason":null}],"usage":null}
data: {"id":"chatcmpl-a82cb662-a901-9b0d-ae12-b5b2cced8781","object":"chat.completion.chunk","created":1791876882,"model":"qwen3.8-omni-flash","choices":[{"index":0,"delta":{"content":"The recording features","reasoning_content":""},"logprobs":null,"finish_reason":null}],"usage":null}
(intermediate chunks omitted)
data: {"id":"chatcmpl-a82cb662-a901-9b0d-ae12-b5b2cced8781","object":"chat.completion.chunk","created":1791876883,"model":"qwen3.8-omni-flash","choices":[{"index":0,"delta":{"content":"","reasoning_content":""},"finish_reason":"stop","logprobs":null}],"usage":null}
data: {"id":"chatcmpl-a82cb662-a901-9b0d-ae12-b5b2cced8781","object":"chat.completion.chunk","created":1791876883,"model":"qwen3.8-omni-flash","choices":[],"usage":{"prompt_tokens":176,"total_tokens":730,"completion_tokens":554,"prompt_tokens_details":{"cached_tokens":0,"audio_tokens":114,"text_tokens":62},"completion_tokens_details":{"reasoning_tokens":458,"text_tokens":96}}}

Model selection

For audio/video understanding, meeting minutes, subtitle generation, and text Q&A, prefer Qwen3.8-Omni-Flash, which supports deep thinking, tool calling, and web search: see Getting started for call examples, Enable/disable thinking mode for thinking, Web search for search, and Function calling for tools. When you need spoken replies, you can use Qwen3.5-Omni below.

Qwen3.8-Omni-Flash

Supported regions: China (Beijing), Singapore, China (Hong Kong), Japan (Tokyo), Germany (Frankfurt), and US (Virginia). Use an API key from the selected region. qwen3.8-omni-flash is designed for audio and video understanding, meeting summaries, and content analysis.
ModelInputOutputAPIs
qwen3.8-omni-flashText, images, audio, videoTextChat Completions, Responses
  • The context length is 1M tokens.
  • Audio input supports 113 languages and dialects, the same as Qwen3.5-Omni. See the Qwen3.5-Omni input audio languages in Model selection for the full list.
  • Supports Function Calling and web search. The only supported built-in Responses tool is currently web_search.

Qwen3.5-Omni: audio output and multimodal interaction

Supported regions: China (Beijing) and Singapore. Use an API key from the selected region.
  • Qwen3.5-Omni series: Supports audio/video understanding, speech responses, and audio control.
    • Input limits: Up to 3 hours of audio or 1 hour of video
    • Audio control: Supports adjusting volume, speaking rate, and emotion through instructions
    • Visual capability: Matches the level of Qwen3.5. Understands images, speech, sound effects, and other multimodal input
    • Combined multimodal input: Supports any combination of text with images, audio, and video in a single request
    • Voice cloning: Supports custom voices (only qwen3.5-omni-plus and qwen3.5-omni-flash; snapshot versions are not supported). For details, see Voice cloning

Other models and specifications

  • Qwen3-Omni-Flash series:
    • Supported regions: China (Beijing) and Singapore. Use the API Key for the corresponding region.
    • Input limits: 20 minutes of audio or 150 seconds of video
    • Thinking mode: Use enable_thinking to enable or disable thinking
    • Input modality: Supports only a combination of text with a single other modality (image, audio, or video).
SeriesAudio-video descriptionDeep thinkingWeb searchInput audio languagesOutput audio languagesSupported voices
Qwen3.5-OmniOmni-modal model with audio outputStrongNot supportedSupported113
Languages: Chinese, English, German, French, Italian, Czech, Indonesian, Thai, Korean, Polish, Japanese, Vietnamese, Finnish, Portuguese, Spanish, Dutch, Russian, Malay, Catalan, Swedish, Turkish, Ukrainian, Romanian, Slovak, Danish, Icelandic, Norwegian (Bokmål), Macedonian, Greek, Hungarian, Galician, Filipino, Croatian, Bosnian, Slovenian, Bulgarian, Kazakh, Belarusian, Latvian, Estonian, Azerbaijani, Uyghur, Swahili, Hindi, Esperanto, Kyrgyz, Tajik, Cebuano, Afrikaans, Arabic, Lithuanian, Javanese, Bengali, Persian, Hebrew, Punjabi, Gujarati, Mongolian, Asturian, Kannada, Marathi, Interlingua, Malayalam, Maltese, Norwegian Nynorsk, Telugu, Urdu, Georgian, Basque, Tamil, Odia, Serbian, MaoriDialects:
Northeastern Mandarin, Guizhou dialect, Cantonese, Henan dialect, Hong Kong Cantonese, Shanghainese, Shaanxi dialect, Tianjin dialect, Taiwanese Hokkien, Yunnan dialect, Anhui dialect, Fujian dialect, Gansu dialect, Guangdong dialect, Hubei dialect, Hunan dialect, Jiangxi dialect, Shandong dialect, Shanxi dialect, Sichuan dialect, Guangxi dialect, Hainan dialect, Chongqing dialect, Changsha dialect, Hangzhou dialect, Hefei dialect, Yinchuan dialect, Zhengzhou dialect, Shenyang dialect, Wenzhou dialect, Wuhan dialect, Kunming dialect, Taiyuan dialect, Nanchang dialect, Jinan dialect, Lanzhou dialect, Nanjing dialect, Hakka, Southern Min

36
Languages: Chinese, English, German, Italian, Portuguese, Spanish, Japanese, Korean, French, Russian, Thai, Indonesian, Arabic, Vietnamese, Turkish, Finnish, Polish, Hindi, Dutch, Czech, Urdu, Tagalog, Swedish, Danish, Hebrew, Icelandic, Malay, Norwegian, Persian
Dialects: Sichuan dialect, Beijing dialect, Tianjin dialect, Nanjing dialect, Shaanxi dialect, Cantonese, Southern Min
55
Qwen3-Omni-FlashHybrid thinking modelWeakerSupportedNot supported19
Language:Chinese, English, German, French, Italian, Thai, Korean, Japanese, Russian, Spanish, PortugueseDialects:Sichuan dialect, Shanghainese, Cantonese, Southern Min, Shaanxi dialect, Nanjing dialect, Tianjin dialect, Beijing dialect
19
Language:Chinese, English, German, French, Italian, Thai, Korean, Japanese, Russian, Spanish, PortugueseDialects:Sichuan dialect, Shanghainese, Cantonese, Southern Min, Shaanxi dialect, Nanjing dialect, Tianjin dialect, Beijing dialect
17 to 49
Varies by version
For model names, context windows, pricing, and snapshot versions, check in the Model Studio console. For rate limits, see Rate limiting.

Model performance

Audio and video content analysis

Generate a comprehensive timestamped description of this video.
00:00.000 – 00:02.500A rain-soaked city street fills the widescreen frame. Long-exposure photography creates streaks of red and blue car lights on wet pavement. A solitary man in a dark knee-length trench coat and light shirt with a tie walks toward the camera along the right sidewalk. Raindrops cling to his shoulders and hair. Each step makes a muffled plop on damp concrete. A low, ominous electronic hum underscores ambient sounds, while steady rain crackles all around. Colorful graffiti covers the brick wall beside him, and neon signs glow in the distance—one clearly displays orange-red cursive script reading "Nice," while another vertical sign shows the letters "AT" at its bottom.......00:12.300 – 00:14.533The camera tilts down. Inside a flashlight beam, a yellowed, aged single sheet lies in a shallow puddle, its edges charred and corners curled. Inked paragraphs cover most of its surface, but moisture and scorch marks make the text unreadable. The man kneels, extending gloved fingers toward the document. Ripples spread outward as his hand nears. Sirens peak then begin to fade slightly, while the ambient hum remains steady. The scene freezes just before the trembling paper is touched, ending the clip.
Only partial timestamps are shown.

Multidialect understanding

Describe the vocal characteristics, emotional state, and full lyrics for singers in different Chinese dialects.
This audio features rap performances by singers from various regions of China, showcasing rich dialect diversity. The following are detailed descriptions of each singer's vocal traits, emotional state, and corresponding lyrics:Hangzhou dialect (Wu)Vocal traits:The male singer's voice is soft and gentle, with the distinctive smoothness and warmth of the Wu dialect. His intonation rises and falls gently, and his rhythm is light and quick, creating a relaxed storytelling effect.Emotional state:Relaxed, comfortable, and full of everyday life. He describes Hangzhou's slow-paced lifestyle and urban changes, expressing affection for his hometown and reflections on modern development. Overall, he feels cozy and familiar.Lyrics:> Hangzhou dialect, listen up! Our culture includes local erhua pronunciation.> Like flowers blooming by West Lake, no cultural survey needed—we understand it best.> None of you understand. 'San bu da men', 'ge'r', 'lao'r', 'fen'r', 'ya'r'... how is an outsider supposed to tell any of these apart?> Let's go! Transport is fast—subways everywhere. We'll check it out.> Welcoming Southeast Asia and Thailand, watching "Thirteen Ta" shows. Proud of our city, direct in personality.> So you only came for the "bridge-crossing" experience, and because the flavor is different, you're not returning?
Only partial results are shown.

Lyric caption generation

Transcribe the song lyrics and provide timestamps for each line in this format:[00:00:15,020 --> 00:00:28,085] : When you walk through a storm, hold your head up high.[00:00:28,085 --> 00:00:40,200] And don't be afraid of the dark. ......
[00:00:12,680 --> 00:00:16,960] Cat thread sways past moonlight on trees.[00:00:18,400 --> 00:00:22,800] Radiators hum 1998 chart hits.[00:00:24.160 → 00:00:28.080] Time parts the mist-like heat waves.[00:00:28,920 --> 00:00:33,000] Neon from the screen shines on my nose bridge.......[00:03:16,720 --> 00:03:21,680] We nestle in the softest ring of the tree trunk.[00:03:22,400 --> 00:03:27,000] Breathing turns residual warmth into honey-sugar.[00:03:28,160 --> 00:03:33,200] The sofa sinks into cloud-fluff shape.[00:03:34,000 --> 00:03:38,800] Every pore soaks in sunshine.[00:04:09,000 --> 00:04:10,020] (End)
Only partial results are shown.

Audio-video programming

Usage

Responses: audio and video input

The following examples use Qwen3.8-Omni-Flash with the Responses API to process audio and video input and generate text responses. Reuse the API key, DASHSCOPE_BASE_URL, and AUDIO_URL from the quick-start example. Responses uses input, with the audio URL in audio_url, rather than the Chat Completions input_audio.data structure. Audio and video input is allowed only in user messages.
import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["DASHSCOPE_API_KEY"],
    base_url=os.environ["DASHSCOPE_BASE_URL"],
)
response = client.responses.create(
    model="qwen3.8-omni-flash",
    input=[{
        "role": "user",
        "content": [
            {"type": "input_text", "text": "Summarize the main points of this recording."},
            {"type": "input_audio", "audio_url": os.environ["AUDIO_URL"], "format": "wav"},
        ],
    }],
    stream=False,
)
print(response.output_text)
To analyze a video, set VIDEO_URL to an accessible video URL and replace input[0].content in the quick-start example with:
[
    {"type": "input_text", "text": "Describe the visuals and sounds in the video."},
    {"type": "input_video", "video_url": os.environ["VIDEO_URL"]},
]
For two-channel or four-channel spatial audio, add "use_multichannel": True (Python) to the audio content object above, alongside audio_url and format. The default is False, which processes audio as single-channel audio. For Base64 audio input, field definitions, and tool scope, see Create a response.

Model configuration

The following configuration recommendations apply to Qwen3.5-Omni. Adjust parameters, prompts, and media lengths for your use case. For Qwen3.8-Omni-Flash, see Recommended prompts.
  • Audio-video understanding
  • Audio understanding
Use caseRecommended video lengthRecommended promptRecommended max_pixels
Fast review, low cost≤60 minutesSimple prompt within 50 words230,400
Content extraction (long video segmentation)≤60 minutes921,600~2,073,600
Standard analysis (short video tagging)≤4 minutesUse the structured prompt below
921,600~2,073,600
Fine-grained analysis (multiple speakers/complex scenes)≤2 minutes2,073,600
You can segment long videos first to obtain fine-grained descriptions.
These prompts apply to qwen3.8-omni-flash. Choose a prompt for your task and place it in the text field of a type="text" item in the user message's content array. Include the audio or video to analyze in the same message. Output format instructions in the prompts guide the generated content.
Combine visual and audio information in a chronological account with time ranges for locating events in the video.
You are a rigorous audio-visual description expert. Your task is to watch and analyze the entire video and produce a highly detailed, coherent, evidence-grounded description that reconstructs, as accurately as possible, what is actually visible, audible, and readable in the video.

Produce a complete, directly usable video description — not an analysis report, not a checklist, not a rough summary. The description must cover the main content of the video from beginning to end, organized in chronological order into naturally coherent, information-dense paragraphs. Level of detail must always yield to factual accuracy: details that cannot be confirmed may be omitted, but you must never guess, fill in, or fabricate anything in order to make the description richer.

## Core Principles

1. Cover the main content of the video from beginning to end, including the opening, the main process, scene changes, important actions, speech, on-screen text, sound, and the ending. Removing redundancy applies only to attributes that have not changed — clothing, room layout, aspect ratio, a continuing music bed — which are described once and then revisited only when they change. It does not license coarser event granularity: you must not skip or compress new actions, text, sounds, or state changes.

2. Every statement must be grounded in what is actually visible, audible, or readable in the video. Do not add background knowledge, common sense, prior assumptions, or speculation from outside the video. Do not treat "seems plausible" as "the video has proven it."

3. Prioritize preserving clearly discernible concrete facts, including people, animals, objects, appearance, clothing, colors, quantities, positions, actions, interactions, on-screen text, subtitles, speech, ambient sound, music, sound effects, camera changes, scene cuts, and state changes.

4. Track people, animals, and objects continuously. Once an entity has been clearly identified, keep its identity consistent in later appearances; if multiple similar entities cannot be reliably distinguished, use positional descriptions such as "the device on the left" or "the person in the center of the frame" rather than forcing a name onto them. Avoid ambiguous references such as "he," "she," "it," "this person," or "that thing."

5. Do not invent a person's identity, age, occupation, emotion, intention, relationship, or the reason behind an action. You may describe visible facial expressions and body movements, but do not interpret an expression directly as a mental state. For example, write "the corners of his mouth turn up and he smiles" rather than "he feels happy."

6. When the identity of an entity, an action, a quantity, text, speech, a temporal relationship, a sound source, or a causal relationship cannot be confirmed, and that uncertainty affects understanding, use brief, specific, conservative wording, for example "the on-screen text is small and cannot be fully made out" or "voices can be heard, but the exact lines are unclear." For unimportant unclear details, simply omit them; do not repeatedly pile up "possibly," "seemingly," and "appears to be."

7. When speech, subtitles, or on-screen text are clearly discernible, preserve the original wording as far as possible, especially for personal names, place names, numbers, brands, labels, proper nouns, formulas, code, and key statements. Do not translate, rewrite, or correct the original text on your own initiative.

8. Distinguish facts directly observed in the footage from opinions expressed by people in the video, by narration, or by subtitles. If a host or narrator offers an evaluation, explanation, recommendation, or causal judgment, write it explicitly as "the host states," "the narration explains," or "the subtitle says"; do not rewrite an opinion voiced in the video as the describer's own objective conclusion.

9. When level of detail and reliability conflict, reliability comes first. It is better to write fewer unconfirmable details than to hallucinate in order to fill up information.

## Description Granularity

The unit of segmentation is a change in information, not a fixed number of seconds. Begin a new event wherever the subject, object, or goal changes; an action enters a new stage; contact is made or released; direction, speed, or trajectory changes noticeably; an observable state change occurs; the speaker turn changes; the sound or music structure changes; the scene, shot, or narrative layer changes; or the interface focus, control, parameter, or result state changes.

For each significant action, write as much of the following as the video actually supports: the subject, its state before the action, the action itself, the object or point of contact, which hand, body part, or tool is used, the direction and trajectory, the speed or manner, any intermediate stage, the resulting state, and the observable consequence. A named action on its own is a label, not a description. "She opens the bottle" is insufficient; "she steadies the bottle with her left hand, then twists the metal cap counter-clockwise with her right, and once the cap is free she sets it down to the right of the bottle" carries the actual granularity. The same applies to a state change: give both ends of it, since "the waveform is narrower after he applies the setting" carries the change while "he adjusts the audio" does not.

Granularity does not degrade with video length. A long or repetitive video receives the same local event granularity as a short one. Length may add chapter-level organization, but it must never replace a sequence of distinct actions with a single coarser generalizing verb.

Before using left, right, in front, or behind, make the frame of reference unambiguous, distinguishing the viewer's left and right within the frame, a person's own left and right, and the left and right inside a software window or interface. When a visual event and a sound belong to the same occurrence, write them together and make the relation explicit rather than listing picture and sound separately for the reader to pair up. Whenever you give a number, make the basis of the count explicit: what is being counted, over which time window, and whether you are counting instantaneous on-screen quantity, distinct entities across the whole video, completed actions, attempts, or sound occurrences.

## Scope of Content Coverage

Describe the following whenever the video actually contains them:

* the video type, subject, narrative line, and overall visual form;
* the main people, animals, and objects, and their identities or roles;
* appearance, clothing, colors, materials, quantities, and spatial positions;
* actions, interactions, operating steps, and their order;
* the scene environment, foreground, middle ground, background, lighting, left-right relationships, and spatial changes;
* camera viewpoint, shot size, focus, push/pull/pan/tilt, following shots, transitions, and frame layout;
* titles, subtitles, labels, interface text, numbers, formulas, code, tables, and charts;
* speech, narration, language, speakers, discernible original spoken content, and obvious tonal characteristics;
* background music, ambient sound, sound effects, and the onset, end, and obvious changes of sounds;
* the entrance, exit, movement, contact, operation, and state changes of the subjects;
* professional procedures, tools, terminology, and conclusions explicitly demonstrated or explicitly stated in the video;
* temporal continuity, location changes, and time jumps between different scenes.

Do not infer a music track's specific BPM, genre, instrumentation, key, mixing, compression, or reverb from listening impression alone. When the sound source cannot be confirmed, use neutral wording such as "an impact sound is heard" or "a short electronic sound effect occurs." Attribute a sound to a specific object only when the sound clearly corresponds to an action visible in the frame.

For OCR, numbers, and charts, transcribe verbatim only when the content is clear enough. Do not guess content from blurry text, and do not estimate values from the heights of bars or lines in a chart. If it cannot be read reliably, omit the specific value or state that part of the text cannot be made out.

## Timestamp Rules

Describe the video in chronological order, and start a new paragraph at shot cuts, scene changes, changes of main activity, the appearance of a new subject, speaker changes, the appearance of key text, or obvious state changes.

Begin each time segment with the following format:

[hh:mm:ss:xxx-hh:mm:ss:xxx]

For example:

[00:00:04:000-00:00:12:000] The shot cuts to ...

Timestamps must be safe time intervals backed by evidence. Timing may be determined from clear shot cuts, subtitle appearances, speech onset and offset points, ASR timestamps, or stably locatable visual events. Do not generate timings from paragraph length, average shot duration, or guesswork, and do not fabricate millisecond-level precision merely to satisfy the millisecond format.

Match precision to the evidence available. Chapters and long scenes take ranges of seconds to minutes; shot boundaries and ordinary actions are locatable to roughly half a second to a second; dialogue turns to a few tenths of a second; clicks, contacts, impacts, and cue tones to around a tenth of a second. If an event can only be confirmed as falling somewhere near a given second, write an approximate range rather than a fabricated millisecond timestamp.

Write an additional precise time point inside a paragraph only when that event can genuinely be located reliably. Otherwise, describe only the order in which events occur and the time segment they fall in. Adjacent time segments must not overlap, and do not manufacture unreliable time boundaries in the pursuit of precision.

Distinguish a new event from a repeated action, a slow-motion pass, a replay, or a flashback. A replayed sequence keeps the same clothing, action order, and target, and should be identified as a replay rather than described as further new events.

## Output Format

Write one concise overview paragraph followed by multiple chronologically ordered description paragraphs.

Open with a short overview introducing the video type, core subjects, main scenes, visual style, and overall auditory environment. Then describe the concrete content in chronological order, integrating visuals, speech, on-screen text, and audio within the same time segment; do not mechanically split by modality into "visual," "audio," "OCR," and so on.

Add a brief closing paragraph only when the video genuinely has a clear overall outcome or concluding development. The closing must not introduce new information absent from the preceding text, and must not offer your own evaluation of the video.

Do not use tables, bullet points, numbered lists, XML, JSON, analytical subheadings, or mechanical headings such as "Scene 1" or "Shot 2.3." Do not output your analysis process, observation process, evidence lists, tool-call records, quality assessments, or any explanation unrelated to the video.

The final output should be natural, fluent, specific, coherent, and information-dense without excessive repetition, and must at all times obey the principles of "evidence first, timing grounded, details reliable."

Please describe this video in detail.
Return separate sections for events, on-screen text, and speech transcripts. Preserve the original wording, speakers, and time ranges for search and verification.
Provide a detailed description of the video.

Make sure your description covers every one of the following dimensions:

Visual
- Subjects and characters: appearance, clothing, gender/age cues, identity, distinctive features
- Actions and events in chronological order, and how the scene evolves over time
- Setting and background: location, environment, time of day
- Spatial layout and relations between subjects/objects; counts and quantities
- On-screen text: captions, titles, subtitles, logos, UI — exact content and appearance
- Visual style: colors, lighting, camera shots, angles, and camera movement

Audio
- Speech: the exact spoken content, transcribed verbatim
- Speakers: who is speaking (mapped to the on-screen person or voice-over), with accent, tone, gender/age cues
- Speaking state: prosody, emotion, volume, and speaking style
- Music: presence, genre/mood, and lyrics if any
- Sound effects and ambient/background sounds
- Non-speech vocalizations: laughter, crying, applause, etc.

Audio-visual correspondence
- Which speech or sound aligns with which on-screen person or visual event
- The timing of each event, expressed with timestamps

It should explicitly include three sections:

1. A structured chronological storyline of **every noticeable audio and visual details**
2. A structured list of all visible text. For each text element, include start timestamp, end timestamp, the exact text content, the appearance characteristics. If no text appears, explicitly state so.
3. A structured speech-to-text transcription, include speaker(Corresponding to the character or voice‑over in Section 1, including their accent and tone), exact spoken content, start timestamp, end timestamp, and speaking state (prosody, emotion, and style). If no speech appears, explicitly state so.

Aside from these three required sections, you are free to organize any additional content in any way you find helpful. This additional content can include global information about the entire video or localized information about specific moments. You may choose the topic of this extra content freely.

Rules:

- Add as much descriptive detail as possible.
- Do not use Markdown bold formatting.
- Carefully look at frames and listen to the audio, making sure no detail is overlooked.

Output Format:

```
## Storyline

<xx:xx.xxx> - <xx:xx.xxx>
<an unstructured long paragraph in natural language describing what happened during this period, blending both audio and video details.>

<xx:xx.xxx> - <xx:xx.xxx>
<an unstructured long paragraph in natural language describing what happened during this period, blending both audio and video details.>

<xx:xx.xxx> - <xx:xx.xxx>
<an unstructured long paragraph in natural language describing what happened during this period, blending both audio and video details.>

...

## Visible Text

<xx:xx.xxx> - <xx:xx.xxx>
“<element>”: <appearance>
“<element>”: <appearance>

<xx:xx.xxx> - <xx:xx.xxx>
“<element>”: <appearance>
“<element>”: <appearance>
“<element>”: <appearance>

<xx:xx.xxx> - <xx:xx.xxx>
“<element>”: <appearance>

...

## Speakers and Transcript

Speaker profiles:
<speaker> - <profile>
<speaker> - <profile>
<speaker> - <profile>
...

<xx:xx.xxx> - <xx:xx.xxx>
Speaker: <speaker>
State: <description>
Content: “<content>”

<xx:xx.xxx> - <xx:xx.xxx>
Speaker: <speaker>
State: <description>
Content: “<content>”

<xx:xx.xxx> - <xx:xx.xxx>
Speaker: <speaker>
State: <description>
Content: “<content>”

...

## <another section>

<paragraphs>

## <another section>

<paragraphs>

...
```
Return JSON organized into scenes and events. Include both the task instructions and the complete JSON Schema in the prompt to define fields, types, required properties, and constraints.
Describe the audio and visual content in detail in English, organized into scenes and events, following the JSON Schema below.
All timestamps must be relative to the beginning of the video. End times must not precede start times or exceed the video duration. Each event must fall within the time range of its parent scene.
Include only information directly supported by the audio or video. Do not guess or invent details. Do not infer causality merely because a sound and an action occur at the same time.
Return only valid JSON, without Markdown fences or commentary.

JSON Schema:
{
  "$defs": {
    "Event": {
      "additionalProperties": false,
      "properties": {
        "time_range": {
          "$ref": "#/$defs/TimeRange",
          "description": "Time range of the event"
        },
        "participants": {
          "description": "People, animals, or objects involved, named by observable features; use consistent names for the same participant",
          "items": {
            "type": "string"
          },
          "title": "Participants",
          "type": "array"
        },
        "action": {
          "description": "Specific actions, interactions, and observable outcomes",
          "title": "Action",
          "type": "string"
        },
        "sounds": {
          "description": "Sounds heard during the event; use an empty list if none are discernible",
          "items": {
            "type": "string"
          },
          "title": "Sounds",
          "type": "array"
        }
      },
      "required": [
        "time_range",
        "participants",
        "action",
        "sounds"
      ],
      "title": "Event"
      ,
      "type": "object"
    },
    "Scene": {
      "additionalProperties": false,
      "properties": {
        "time_range": {
          "$ref": "#/$defs/TimeRange",
          "description": "Time range of the scene"
        },
        "setting": {
          "description": "Environment, spatial layout, and main visual features",
          "title": "Setting",
          "type": "string"
        },
        "events": {
          "description": "Events in chronological order; use an empty list if there are none",
          "items": {
            "$ref": "#/$defs/Event"
          },
          "title": "Events",
          "type": "array"
        }
      },
      "required": [
        "time_range",
        "setting",
        "events"
      ],
      "title": "Scene",
      "type": "object"
    },
    "TimeRange": {
      "additionalProperties": false,
      "properties": {
        "start_seconds": {
          "description": "Start time in seconds relative to the beginning of the video",
          "minimum": 0,
          "title": "Start Seconds",
          "type": "number"
        },
        "end_seconds": {
          "description": "End time in seconds; must not precede the start time",
          "minimum": 0,
          "title": "End Seconds",
          "type": "number"
        }
      },
      "required": [
        "start_seconds",
        "end_seconds"
      ],
      "title": "TimeRange",
      "type": "object"
    }
  },
  "additionalProperties": false,
  "properties": {
    "summary": {
      "description": "An overview of the main content of the video",
      "title": "Summary",
      "type": "string"
    },
    "scenes": {
      "description": "Scenes in chronological order; group continuous footage with a consistent setting into one scene",
      "items": {
        "$ref": "#/$defs/Scene"
      },
      "title": "Scenes",
      "type": "array"
    }
  },
  "required": [
    "summary",
    "scenes"
  ],
  "title": "CaptionResult",
  "type": "object"
}
Describe rapidly changing visuals, actions, and events. Adjust the video sampling frame rate to the dynamics of the content. The model maintains stable results with input sampled at up to 15 fps, capturing finer actions and temporal changes. Higher frame rates increase processing costs, so choose a rate that suits the task.
You are a rigorous audio-visual description expert. Your task is to watch and analyze the entire video and produce a highly detailed, coherent, evidence-grounded description that reconstructs, as accurately as possible, what is actually visible, audible, and readable in the video.
Produce a complete, directly usable video description — not an analysis report, not a checklist, not a rough summary. The description must cover the main content of the video from beginning to end, organized in chronological order into naturally coherent, information-dense paragraphs. Level of detail must always yield to factual accuracy: details that cannot be confirmed may be omitted, but you must never guess, fill in, or fabricate anything in order to make the description richer.
## Core Principles
1. Cover the main content of the video from beginning to end, including the opening, the main process, scene changes, important actions, speech, on-screen text, sound, and the ending. Removing redundancy applies only to attributes that have not changed — clothing, room layout, aspect ratio, a continuing music bed — which are described once and then revisited only when they change. It does not license coarser event granularity: you must not skip or compress new actions, text, sounds, or state changes.
2. Every statement must be grounded in what is actually visible, audible, or readable in the video. Do not add background knowledge, common sense, prior assumptions, or speculation from outside the video. Do not treat "seems plausible" as "the video has proven it."
3. Prioritize preserving clearly discernible concrete facts, including people, animals, objects, appearance, clothing, colors, quantities, positions, actions, interactions, on-screen text, subtitles, speech, ambient sound, music, sound effects, camera changes, scene cuts, and state changes.
4. Track people, animals, and objects continuously. Once an entity has been clearly identified, keep its identity consistent in later appearances; if multiple similar entities cannot be reliably distinguished, use positional descriptions such as "the device on the left" or "the person in the center of the frame" rather than forcing a name onto them. Avoid ambiguous references such as "he," "she," "it," "this person," or "that thing."
5. Do not invent a person's identity, age, occupation, emotion, intention, relationship, or the reason behind an action. You may describe visible facial expressions and body movements, but do not interpret an expression directly as a mental state. For example, write "the corners of his mouth turn up and he smiles" rather than "he feels happy."
6. When the identity of an entity, an action, a quantity, text, speech, a temporal relationship, a sound source, or a causal relationship cannot be confirmed, and that uncertainty affects understanding, use brief, specific, conservative wording, for example "the on-screen text is small and cannot be fully made out" or "voices can be heard, but the exact lines are unclear." For unimportant unclear details, simply omit them; do not repeatedly pile up "possibly," "seemingly," and "appears to be."
7. When speech, subtitles, or on-screen text are clearly discernible, preserve the original wording as far as possible, especially for personal names, place names, numbers, brands, labels, proper nouns, formulas, code, and key statements. Do not translate, rewrite, or correct the original text on your own initiative.
8. Distinguish facts directly observed in the footage from opinions expressed by people in the video, by narration, or by subtitles. If a host or narrator offers an evaluation, explanation, recommendation, or causal judgment, write it explicitly as "the host states," "the narration explains," or "the subtitle says"; do not rewrite an opinion voiced in the video as the describer's own objective conclusion.
9. When level of detail and reliability conflict, reliability comes first. It is better to write fewer unconfirmable details than to hallucinate in order to fill up information.

## Description Granularity
The unit of segmentation is a change in information, not a fixed number of seconds. Begin a new event wherever the subject, object, or goal changes; an action enters a new stage; contact is made or released; direction, speed, or trajectory changes noticeably; an observable state change occurs; the speaker turn changes; the sound or music structure changes; the scene, shot, or narrative layer changes; or the interface focus, control, parameter, or result state changes.
For each significant action, write as much of the following as the video actually supports: the subject, its state before the action, the action itself, the object or point of contact, which hand, body part, or tool is used, the direction and trajectory, the speed or manner, any intermediate stage, the resulting state, and the observable consequence. A named action on its own is a label, not a description. "She opens the bottle" is insufficient; "she steadies the bottle with her left hand, then twists the metal cap counter-clockwise with her right, and once the cap is free she sets it down to the right of the bottle" carries the actual granularity. The same applies to a state change: give both ends of it, since "the waveform is narrower after he applies the setting" carries the change while "he adjusts the audio" does not.
Granularity does not degrade with video length. A long or repetitive video receives the same local event granularity as a short one. Length may add chapter-level organization, but it must never replace a sequence of distinct actions with a single coarser generalizing verb.
Before using left, right, in front, or behind, make the frame of reference unambiguous, distinguishing the viewer's left and right within the frame, a person's own left and right, and the left and right inside a software window or interface. When a visual event and a sound belong to the same occurrence, write them together and make the relation explicit rather than listing picture and sound separately for the reader to pair up. Whenever you give a number, make the basis of the count explicit: what is being counted, over which time window, and whether you are counting instantaneous on-screen quantity, distinct entities across the whole video, completed actions, attempts, or sound occurrences.

## Scope of Content Coverage
Describe the following whenever the video actually contains them:

* the video type, subject, narrative line, and overall visual form;
* the main people, animals, and objects, and their identities or roles;
* appearance, clothing, colors, materials, quantities, and spatial positions;
* actions, interactions, operating steps, and their order;
* the scene environment, foreground, middle ground, background, lighting, left-right relationships, and spatial changes;
* camera viewpoint, shot size, focus, push/pull/pan/tilt, following shots, transitions, and frame layout;
* titles, subtitles, labels, interface text, numbers, formulas, code, tables, and charts;
* speech, narration, language, speakers, discernible original spoken content, and obvious tonal characteristics;
* background music, ambient sound, sound effects, and the onset, end, and obvious changes of sounds;
* the entrance, exit, movement, contact, operation, and state changes of the subjects;
* professional procedures, tools, terminology, and conclusions explicitly demonstrated or explicitly stated in the video;
* temporal continuity, location changes, and time jumps between different scenes.

Do not infer a music track's specific BPM, genre, instrumentation, key, mixing, compression, or reverb from listening impression alone. When the sound source cannot be confirmed, use neutral wording such as "an impact sound is heard" or "a short electronic sound effect occurs." Attribute a sound to a specific object only when the sound clearly corresponds to an action visible in the frame.
For OCR, numbers, and charts, transcribe verbatim only when the content is clear enough. Do not guess content from blurry text, and do not estimate values from the heights of bars or lines in a chart. If it cannot be read reliably, omit the specific value or state that part of the text cannot be made out.

## Timestamp Rules

Describe the video in chronological order, and start a new paragraph at shot cuts, scene changes, changes of main activity, the appearance of a new subject, speaker changes, the appearance of key text, or obvious state changes.
Begin each time segment with the following format:

`[hh:mm:ss:xxx-hh:mm:ss:xxx]`

For example:

`[00:00:04:000-00:00:12:000] The shot cuts to ...`

Timestamps must be safe time intervals backed by evidence. Timing may be determined from clear shot cuts, subtitle appearances, speech onset and offset points, ASR timestamps, or stably locatable visual events. Do not generate timings from paragraph length, average shot duration, or guesswork, and do not fabricate millisecond-level precision merely to satisfy the millisecond format.
Match precision to the evidence available. Chapters and long scenes take ranges of seconds to minutes; shot boundaries and ordinary actions are locatable to roughly half a second to a second; dialogue turns to a few tenths of a second; clicks, contacts, impacts, and cue tones to around a tenth of a second. If an event can only be confirmed as falling somewhere near a given second, write an approximate range rather than a fabricated millisecond timestamp.
Write an additional precise time point inside a paragraph only when that event can genuinely be located reliably. Otherwise, describe only the order in which events occur and the time segment they fall in. Adjacent time segments must not overlap, and do not manufacture unreliable time boundaries in the pursuit of precision.
Distinguish a new event from a repeated action, a slow-motion pass, a replay, or a flashback. A replayed sequence keeps the same clothing, action order, and target, and should be identified as a replay rather than described as further new events.

## Output Format

- Write one concise overview paragraph followed by multiple chronologically ordered description paragraphs.
- Open with a short overview introducing the video type, core subjects, main scenes, visual style, and overall auditory environment. Then describe the concrete content in chronological order, integrating visuals, speech, on-screen text, and audio within the same time segment; do not mechanically split by modality into "visual," "audio," "OCR," and so on.
- Add a brief closing paragraph only when the video genuinely has a clear overall outcome or concluding development. The closing must not introduce new information absent from the preceding text, and must not offer your own evaluation of the video.
- Do not use tables, bullet points, numbered lists, XML, JSON, analytical subheadings, or mechanical headings such as "Scene 1" or "Shot 2.3." Do not output your analysis process, observation process, evidence lists, tool-call records, quality assessments, or any explanation unrelated to the video.
- The final output should be natural, fluent, specific, coherent, and information-dense without excessive repetition, and must at all times obey the principles of "evidence first, timing grounded, details reliable."

Please describe this video in a super detail manner.
Use audio and video to identify speakers and produce a timestamped transcript. When video is provided, the model can use visual information to analyze the correspondence between speakers and speech.
Transcribe the dialogue with speaker identification and timestamps. Output format: <soc><sos><start_time>text<end_time><speakerX><eos>...<eoc>.
Locate a specified sound event. Replace [audio event label] in the prompt with the target event, such as dog_barking. The result contains the event type and start and end times in seconds.
Detect the timestamps of the following sound event in the audio: [audio event label]. Output the result strictly as a JSON array. Each element must contain exactly these keys: "type" (the event label, copied verbatim from the request), "start_time" and "end_time" (both MUST be decimal numbers in seconds, e.g. 4.5 or 12.0, NOT strings, and NOT in mm:ss or hh:mm:ss format). If the same event occurs multiple times, output one element per occurrence, all sharing the same "type", inside the SAME JSON array. Do not include any text outside the JSON array. Example: [{"type": "dog_barking", "start_time": 1.23, "end_time": 4.56}]

Combined multimodal input

Qwen3.8-Omni-Flash and Qwen3.5-Omni support combining text, images, audio, and video in the same request. The examples below use Qwen3.8-Omni-Flash to analyze an image and audio and generate a text response. To generate speech, use Qwen3.5-Omni. See the audio output example.
import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # DASHSCOPE_BASE_URL is your workspace endpoint (see Getting started). China (Beijing) looks like https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1. URLs vary by region.
    base_url=os.environ["DASHSCOPE_BASE_URL"],
)

completion = client.chat.completions.create(
    model="qwen3.8-omni-flash",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241022/emyrja/dog_and_girl.jpeg"
                    },
                },
                {
                    "type": "input_audio",
                    "input_audio": {
                        "data": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20250211/tixcef/cherry.wav",
                        "format": "wav"
                    },
                },
                {"type": "text", "text": "Describe the image content and tell me what the audio is about."},
            ],
        },
    ],
    modalities=["text"],

    stream=True,
    stream_options={"include_usage": True},
)

for chunk in completion:
    if chunk.choices:
        print(chunk.choices[0].delta)
    else:
        print(chunk.usage)

Single modality input

Each request contains text and one other modality (video, audio, or image). The examples use Qwen3.8-Omni-Flash to return text through Chat Completions.
  • Video and text input
  • Audio and text input
  • Image and text input
Provide the video as a video file (with audio support) or an image list. See the tabs below.
  • Video file (supports audio in the video)
  • Image list format
import os
from openai import OpenAI

client = OpenAI(
    # If no environment variable is set, replace the next line with your Model Studio API key: api_key="sk-xxx"
    # API keys for the Singapore and Beijing regions are different. Get an API key: https://help.aliyun.com/en/model-studio/get-api-key
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # DASHSCOPE_BASE_URL is your workspace endpoint (see Getting started). China (Beijing) looks like https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1. URLs vary by region.
    base_url=os.environ["DASHSCOPE_BASE_URL"],
)

completion = client.chat.completions.create(
    model="qwen3.8-omni-flash",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "video_url",
                    "video_url": {
                        "url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241115/cqqkru/1.mp4"
                    },
                },
                {"type": "text", "text": "What is the video about?"},
            ],
        },
    ],

    modalities=["text"],


    stream=True,
    stream_options={"include_usage": True},
)

for chunk in completion:
    if chunk.choices:
        print(chunk.choices[0].delta)
    else:
        print(chunk.usage)
  • Number of files:
    • Qwen3.8-Omni-Flash: Up to 64 files.
    • Qwen3.5-Omni series: Up to 512 files using public URLs and up to 250 files using Base64 encoding.
    • Qwen3-Omni-Flash series: Only one file is allowed.
  • File size:
    • Using public URLs:
      • Qwen3.8-Omni-Flash and Qwen3.5-Omni series: Up to 2 GB
      • Qwen3-Omni-Flash: Up to 256 MB
    • Using Base64 encoding: The encoded Base64 string must be smaller than 10 MB
  • Duration limits:
    • Qwen3.8-Omni-Flash: 2 hours
    • Qwen3.5-Omni series: 1 hour
    • Qwen3-Omni-Flash: 150 seconds
  • File formats: MP4, AVI, MKV, MOV, FLV, WMV, and others.
  • Visual and audio information in the video file are billed separately.

Multichannel audio

qwen3.8-omni-flash supports two-channel stereo and four-channel FOA spatial audio (WYZX channel order). These inputs retain spatial information for audio understanding. use_multichannel defaults to false, which parses all audio as mono. When it is true and the input has two channels (left and right) or four channels (FOA, in WYZX order), the model parses spatial audio information. In the Chat Completions quick-start example, set AUDIO_URL to the corresponding multichannel WAV audio and add the following argument to client.chat.completions.create(). In an HTTP request, use_multichannel is a top-level request body field. For where to pass it in a Responses call, see Responses: audio and video input.
extra_body={"use_multichannel": True},
The following complete example uses a two-channel stereo WAV to call qwen3.8-omni-flash for spatial audio understanding. For four-channel FOA audio, the call is identical—just set AUDIO_URL to a four-channel WAV in WYZX channel order.
import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["DASHSCOPE_API_KEY"],
    base_url=os.environ["DASHSCOPE_BASE_URL"],
)
completion = client.chat.completions.create(
    model="qwen3.8-omni-flash",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "Describe the spatial audio information in this recording."},
            {"type": "input_audio", "input_audio": {
                "data": os.environ["AUDIO_URL"],
                "format": "wav",
            }},
        ],
    }],
    extra_body={"use_multichannel": True},
    stream=True,
    stream_options={"include_usage": True},
)
for chunk in completion:
    if not chunk.choices:
        if chunk.usage:
            print("\nUsage:", chunk.usage)
        continue
    delta = chunk.choices[0].delta
    if getattr(delta, "reasoning_content", None):
        print(delta.reasoning_content, end="", flush=True)
    if delta.content:
        print(delta.content, end="", flush=True)
The multichannel audio must have either 2 channels (two-channel stereo, left and right) or 4 channels (four-channel FOA, WYZX channel order). You can check the channel count with soundfile:
python3 -c "import soundfile as sf; print(sf.info('audio.wav').channels)"
Qwen3.8-Omni-Flash supports web search to retrieve current information and generate text answers. Qwen3.8-Omni-Flash uses the web_search tool through Responses. Qwen3.8-Omni-Flash and Qwen3.5-Omni use the agent search strategy through Chat Completions. For fees, see Web search billing. The following Chat Completions examples use Qwen3.8-Omni-Flash with enable_search=True and search_options={"search_strategy": "agent"} to enable web search and reasoning_effort="none" to disable thinking, then read streamed text responses.
# Prerequisites:
# pip install openai

import os
from openai import OpenAI

# Initialize the client
client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # DASHSCOPE_BASE_URL is your workspace endpoint (see Getting started). China (Beijing) looks like https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1. URLs vary by region.
    base_url=os.environ["DASHSCOPE_BASE_URL"],
)

# Send request (with web search enabled)
try:
    completion = client.chat.completions.create(
        model="qwen3.8-omni-flash",
        reasoning_effort="none",
        messages=[{
            "role": "user",
            "content": "What is today's date and day of the week, and what important holidays are there today?"
        }],
        stream=True,
        stream_options={"include_usage": True},
        # Enable web search
        extra_body={
            "enable_search": True,
            "search_options": {"search_strategy": "agent"}
        }
    )

    print("Model response (with real-time information):")
    for chunk in completion:
        if chunk.choices and chunk.choices[0].delta.content:
            print(chunk.choices[0].delta.content, end="")
    print()

except Exception as e:
    print(f"Request failed:{e}")

Enable/disable thinking mode

Qwen3.8-Omni-Flash

Thinking is enabled by default for qwen3.8-omni-flash, with reasoning_effort defaulting to xhigh. The top-level Chat Completions request field reasoning_effort accepts none, minimal, low, medium, high, xhigh, and max. Do not set reasoning_effort and thinking_budget together: the request returns an error. Choose low, medium, or xhigh for the thinking effort. For compatibility, minimal maps to low, high and max map to xhigh, and none disables thinking. Add the following argument to client.chat.completions.create() in the Chat Completions quick-start example. Pass reasoning_effort directly as an SDK argument.
reasoning_effort="low",

Qwen3-Omni-Flash

This section describes hybrid thinking for Qwen3-Omni-Flash. You can use the enable_thinking parameter to enable or disable the thinking mode:
  • true: enables thinking mode
  • false (default): disables thinking mode
In thinking mode, audio output is not supported.
Qwen3.5-Omni and Qwen3-Omni-Flash support streaming output only. The examples in this section require stream=True; otherwise the request fails.
import os
from openai import OpenAI

client = OpenAI(
    # If no environment variable is set, replace the next line with your Model Studio API key: api_key="sk-xxx"
    # API keys for the Singapore and Beijing regions are different. Get an API key: https://help.aliyun.com/en/model-studio/get-api-key
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # DASHSCOPE_BASE_URL is your workspace endpoint (see Getting started). China (Beijing) looks like https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1. URLs vary by region.
    base_url=os.environ["DASHSCOPE_BASE_URL"],
)

completion = client.chat.completions.create(
    model="qwen3-omni-flash",
    messages=[{"role": "user", "content": "Who are you?"}],

    extra_body={'enable_thinking': True},

    # Set the output modality. Two options are supported in non-thinking mode: ["text","audio"] and ["text"]. Only ["text"] is supported in thinking mode.
    modalities=["text"],

    # Set the voice. The audio parameter is not supported in thinking mode.
    # audio={"voice": "Tina", "format": "wav"},
    # stream must be set to True, otherwise an error occurs.
    stream=True,
    stream_options={"include_usage": True},
)

for chunk in completion:
    if chunk.choices:
        print(chunk.choices[0].delta)
    else:
        print(chunk.usage)
data: {"choices":[{"delta":{"content":null,"role":"assistant","reasoning_content":""},"index":0,"logprobs":null,"finish_reason":null}],"object":"chat.completion.chunk","usage":null,"created":1757937336,"system_fingerprint":null,"model":"qwen3-omni-flash","id":"chatcmpl-ce3d6fe5-e717-4b7e-8b40-3aef12288d4c"}
data: {"choices":[{"finish_reason":null,"logprobs":null,"delta":{"content":null,"reasoning_content":"Hmm"},"index":0}],"object":"chat.completion.chunk","usage":null,"reated":1757937336,"system_fingerprint":null,"model":"qwen3-omni-flash","id":"chatcmpl-ce3d6fe5-e717-4b7e-8b40-3aef12288d4c"}
data: {"choices":[{"delta":{"content":null,"reasoning_content":","},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"reated":1757937336,"system_fingerprint":null,"model":"qwen3-omni-flash","id":"chatcmpl-ce3d6fe5-e717-4b7e-8b40-3aef12288d4c"}
......
data: {"choices":[{"delta":{"content":"Tell me"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1757937336,"tem_fingerprint":null,"model":"qwen3-omni-flash","id":"chatcmpl-ce3d6fe5-e717-4b7e-8b40-3aef12288d4c"}
data: {"choices":[{"delta":{"content":"!"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1757937336,"systm_fingerprint":null,"model":"qwen3-omni-flash","id":"chatcmpl-ce3d6fe5-e717-4b7e-8b40-3aef12288d4c"}
data: {"choices":[{"finish_reason":"stop","delta":{"content":"","reasoning_content":null},"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1757937336,"system_fingerprint":null,"model":"qwen3-omni-flash","id":"chatcmpl-ce3d6fe5-e717-4b7e-8b40-3aef12288d4c"}
data: {"choices":[],"object":"chat.completion.chunk","usage":{"prompt_tokens":11,"completion_tokens":363,"total_tokens":374,"completion_tokens_details":{"reasoning_tokens":195,"text_tokens":168},"prompt_tokens_details":{"text_tokens":11}},"created":1757937336,"system_fingerprint":null,"model":"qwen3-omni-flash","id":"chatcmpl-ce3d6fe5-e717-4b7e-8b40-3aef12288d4c"}

Multi-turn conversation

For Qwen3.8-Omni-Flash multi-turn conversations, preserve_thinking is enabled by default. Put the previous answer and reasoning in the historical assistant message's content and reasoning_content fields, respectively, and include that message in messages for the next request. When supplied and enabled, historical reasoning counts toward input tokens and billing. For how to pass this parameter and a complete multi-turn example, see Pass the thinking process. The following multi-turn example uses Qwen3.8-Omni-Flash with reasoning_effort="none" to disable thinking. It passes text responses in historical assistant messages and text plus one modality in each user message. For combined input, see Combined multimodal input:
  • Assistant Message The content of an assistant message in the messages array can contain only text data; reasoning is passed in the reasoning_content field.
  • User Message In this example, each user message contains text and one other modality. In multi-turn conversations, you can input different modalities in different user messages.
import os
from openai import OpenAI

# Initialize the OpenAI client
client = OpenAI(
    # If no environment variable is set, replace the next line with your Model Studio API key: api_key="sk-xxx"
    # API keys for the Singapore and Beijing regions are different. Get an API key: https://help.aliyun.com/en/model-studio/get-api-key
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # DASHSCOPE_BASE_URL is your workspace endpoint (see Getting started). China (Beijing) looks like https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1. URLs vary by region.
    base_url=os.environ["DASHSCOPE_BASE_URL"],
)

completion = client.chat.completions.create(
    model="qwen3.8-omni-flash",
    reasoning_effort="none",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "input_audio",
                    "input_audio": {
                        "data": "https://dashscope.oss-cn-beijing.aliyuncs.com/audios/welcome.mp3",
                        "format": "mp3",
                    },
                },
                {"type": "text", "text": "What is this audio about"},
            ],
        },
        {
            "role": "assistant",
            "content": [{"type": "text", "text": "This audio says: Welcome to Alibaba Cloud"}],
        },
        {
            "role": "user",
            "content": [{"type": "text", "text": "Tell me about this company."}],
        },
    ],

    modalities=["text"],

    stream=True,
    stream_options={"include_usage": True},
)

for chunk in completion:
    if chunk.choices:
        print(chunk.choices[0].delta)
    else:
        print(chunk.usage)

Parsing output Base64-encoded audio data

qwen3.5-omni-plus, qwen3.5-omni-flash, and qwen3-omni-flash (non-thinking mode) output audio as streaming Base64-encoded data. During generation, maintain a string variable and append the Base64-encoded data from each returned chunk. After generation completes, Base64-decode the complete string to get the audio file. Alternatively, decode and play each chunk in real time.
Qwen3.5-Omni and Qwen3-Omni-Flash support streaming output only. The examples in this section require stream=True; otherwise the request fails.
# Installation instructions for pyaudio:
# APPLE Mac OS X
#   brew install portaudio
#   pip install pyaudio
# Debian/Ubuntu
#   sudo apt-get install python-pyaudio python3-pyaudio
#   or
#   pip install pyaudio
# CentOS
#   sudo yum install -y portaudio portaudio-devel && pip install pyaudio
# Microsoft Windows
#   python -m pip install pyaudio

import os
from openai import OpenAI
import base64
import numpy as np
import soundfile as sf

# Initialize the OpenAI client
client = OpenAI(
    # If no environment variable is set, replace the next line with your Model Studio API key: api_key="sk-xxx"
    # API keys for the Singapore and Beijing regions are different. Get an API key: https://help.aliyun.com/en/model-studio/get-api-key
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # DASHSCOPE_BASE_URL is your workspace endpoint (see Getting started). China (Beijing) looks like https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1. URLs vary by region.
    base_url=os.environ["DASHSCOPE_BASE_URL"],
)

completion = client.chat.completions.create(
    model="qwen3.5-omni-plus", # For Qwen3-Omni-Flash, run in non-thinking mode.
    messages=[{"role": "user", "content": "Who are you?"}],
    # Set the output modality. Two options are currently supported: ["text","audio"] and ["text"]
    modalities=["text", "audio"],
    audio={"voice": "Tina", "format": "wav"},
    # stream must be set to True, otherwise an error occurs.
    stream=True,
    stream_options={"include_usage": True},
)

# Method 1: Decode after generation is complete
audio_string = ""
for chunk in completion:
    if chunk.choices:
        if hasattr(chunk.choices[0].delta, "audio"):
            try:
                audio_string += chunk.choices[0].delta.audio["data"]
            except Exception as e:
                print(chunk.choices[0].delta.content)
    else:
        print(chunk.usage)

wav_bytes = base64.b64decode(audio_string)
audio_np = np.frombuffer(wav_bytes, dtype=np.int16)
sf.write("audio_assistant_py.wav", audio_np, samplerate=24000)

# Method 2: Decode while generating (comment out the code for Method 1 to use Method 2)
# # Initialize PyAudio
# import pyaudio
# import time
# p = pyaudio.PyAudio()
# # Create an audio stream
# stream = p.open(format=pyaudio.paInt16,
#                 channels=1,
#                 rate=24000,
#                 output=True)

# for chunk in completion:
#     if chunk.choices:
#         if hasattr(chunk.choices[0].delta, "audio"):
#             try:
#                 audio_string = chunk.choices[0].delta.audio["data"]
#                 wav_bytes = base64.b64decode(audio_string)
#                 audio_np = np.frombuffer(wav_bytes, dtype=np.int16)
#                 # Play the audio data directly
#                 stream.write(audio_np.tobytes())
#             except Exception as e:
#                 print(chunk.choices[0].delta.content)

# time.sleep(0.8)
# # Clean up resources
# stream.stop_stream()
# stream.close()
# p.terminate()

Input Base64-encoded local file

The following examples use Qwen3.8-Omni-Flash to analyze Base64-encoded local files and return text. For audio and video, the encoded Base64 string must be smaller than 10 MB. This limit also applies to image input for Qwen3.5-Omni and Qwen3-Omni-Flash.
  • Images
  • Audio
  • Video
This example uses the locally saved file eagle.png.
import os
from openai import OpenAI
import base64

client = OpenAI(
    # If no environment variable is set, replace the next line with your Model Studio API key: api_key="sk-xxx"
    # API keys for the Singapore and Beijing regions are different. Get an API key: https://help.aliyun.com/en/model-studio/get-api-key
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # DASHSCOPE_BASE_URL is your workspace endpoint (see Getting started). China (Beijing) looks like https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1. URLs vary by region.
    base_url=os.environ["DASHSCOPE_BASE_URL"],
)

# Base64 encoding format
def encode_image(image_path):
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode("utf-8")

base64_image = encode_image("eagle.png")

completion = client.chat.completions.create(
    model="qwen3.8-omni-flash",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image_url",
                    "image_url": {"url": f"data:image/png;base64,{base64_image}"},
                },
                {"type": "text", "text": "What scene is depicted in the image?"},
            ],
        },
    ],

    modalities=["text"],


    stream=True,
    stream_options={"include_usage": True},
)

for chunk in completion:
    if chunk.choices:
        print(chunk.choices[0].delta)
    else:
        print(chunk.usage)

API reference

  • Chat Completions: Input and output parameters for the models on this page.
  • Responses: Input and output parameters for Qwen3.8-Omni-Flash.

Billing and rate limits

Qwen3.8-Omni-Flash supports automatic implicit caching and Responses Session caching. For configuration, see Session cache. Billing rules The models on this page are billed by the number of tokens for each modality (audio, image, video). For per-region pricing, see Model pricing.
Token conversion rules and applicable models are listed by modality below.
  • Audio
  • Images
  • Video
  • Qwen3.8-Omni-Flash: Input audio formula: Total tokens = Audio duration (seconds) * 7.
  • Qwen3.5-Omni series:
    • Input audio formula: Total tokens = Audio duration (seconds) * 7
    • Output audio formula: Total tokens = Audio duration (seconds) * 12.5
  • Qwen3-Omni-Flash: For both input and output audio, Total tokens = Audio duration (seconds) * 12.5
If the audio duration is less than 1 second, it is counted as 1 second.
Free quota To claim, query, or use your free quota, see Free quota for new users. Rate limits For rate limiting rules and FAQ, see Rate limiting.

FAQ

Q: How do I include conversation history?

Add user and assistant messages to the messages array in conversation order. The following examples use qwen3.5-omni-plus.
Qwen3.5-Omni and Qwen3-Omni-Flash support streaming output only. The examples in this section require stream=True; otherwise the request fails.
import os
from openai import OpenAI

client = OpenAI(
    # If no environment variable is set, replace the next line with your Model Studio API key: api_key="sk-xxx"
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # DASHSCOPE_BASE_URL is your workspace endpoint (see Getting started). China (Beijing) looks like https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1. URLs vary by region.
    base_url=os.environ["DASHSCOPE_BASE_URL"],
)

completion = client.chat.completions.create(
    model="qwen3.5-omni-plus",
    messages=[
        {"role": "user", "content": "You are a shopping mall salesperson, and your products include mobile phones, computers, and refrigerators"},
        {"role": "assistant", "content": "Okay, I remember your setting."},
        {"role": "user", "content": "Who are you?"},
    ],
    # Set the output modality. Two options are currently supported: ["text","audio"] and ["text"]
    modalities=["text", "audio"],
    audio={"voice": "Tina", "format": "wav"},
    # stream must be set to True, otherwise an error occurs.
    stream=True,
    stream_options={"include_usage": True},
)

for chunk in completion:
    if chunk.choices:
        print(chunk.choices[0].delta)
    else:
        print(chunk.usage)

Error codes

If the model call fails and returns an error message, see Error codes for resolution.

Voice list

qwen3.5-omni-plus, qwen3.5-omni-flash, and qwen3-omni-flash support audio output. For their available voices, see Voice list.
Token Plan
Model Playground
Fine-tuning
Model Compression
Statistics and Monitoring
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