> ## Documentation Index
> Fetch the complete documentation index at: https://docs.oxen.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Kling 2.6 Pro - Text to Video

> Cinematic text-to-video with native audio

<CardGroup cols={1}>
  <Card title="Try Kling 2.6 Pro - Text to Video in the Workbench" icon="flask" href="https://www.oxen.ai/ai/workbench?model=kling-video-v2-6-pro-text-to-video">
    Run this model interactively, tune parameters, and compare outputs.
  </Card>
</CardGroup>

**Model ID:** `kling-video-v2-6-pro-text-to-video`

**Kling 2.6 Pro - Text to Video is a video generation model.** It excels in generating short 1080p videos from text prompts with optional native synchronized audio, including lip-sync, dialogue, sound effects, and ambient sounds, reducing the need for post-production editing compared to models that output silent clips.

Some other noteworthy features of Kling 2.6 Pro - Text to Video include support for multiple aspect ratios (16:9, 9:16, 1:1), bilingual voice output (English/Chinese), image-to-video prompts, and Pro mode for higher detail.

| Metric             | Value   |
| ------------------ | ------- |
| Parameter Count    | Unknown |
| Mixture of Experts | Unknown |
| Context Length     | Unknown |
| Multilingual       | Yes     |
| Quantized\*        | Unknown |

\**Quantization is specific to the inference provider and the model may be offered with different quantization levels by other providers.*

## Example request

<Tip>
  Use the [Workbench](https://www.oxen.ai/ai/workbench?model=kling-video-v2-6-pro-text-to-video) as a request builder: configure parameters for this model in the UI, then open the **API** tab to copy the exact cURL or Python call.
</Tip>

<Tabs>
  <Tab title="Sync">
    This blocks until the video is ready (typically 5-15 minutes). Prefer **Async** or **Async with SSE** for anything beyond quick experimentation.

    See the [video generation reference](/inference-api/reference/video_generation) for more details.

    <Tabs>
      <Tab title="Minimal">
        <CodeGroup>
          ```bash cURL theme={null}
          curl -X POST https://hub.oxen.ai/api/ai/videos/generate \
            -H "Content-Type: application/json" \
            -H "Authorization: Bearer $OXEN_API_KEY" \
            -d '{
            "model": "kling-video-v2-6-pro-text-to-video",
            "prompt": "A close up of a blonde woman surfer in a wet suit, with a wave behind her, paddling to get momentum before standing up, on her stomach, looking back over her shoulder at the wave coming in."
          }'
          ```

          ```python Python theme={null}
          import os
          import requests

          response = requests.post(
              "https://hub.oxen.ai/api/ai/videos/generate",
              headers={
                  "Content-Type": "application/json",
                  "Authorization": f"Bearer {os.environ['OXEN_API_KEY']}",
              },
              json={
                  "model": "kling-video-v2-6-pro-text-to-video",
                  "prompt": "A close up of a blonde woman surfer in a wet suit, with a wave behind her, paddling to get momentum before standing up, on her stomach, looking back over her shoulder at the wave coming in."
              },
          )
          response.raise_for_status()
          print(response.json())
          ```
        </CodeGroup>
      </Tab>

      <Tab title="All parameters">
        <CodeGroup>
          ```bash cURL theme={null}
          curl -X POST https://hub.oxen.ai/api/ai/videos/generate \
            -H "Content-Type: application/json" \
            -H "Authorization: Bearer $OXEN_API_KEY" \
            -d '{
            "model": "kling-video-v2-6-pro-text-to-video",
            "prompt": "A close up of a blonde woman surfer in a wet suit, with a wave behind her, paddling to get momentum before standing up, on her stomach, looking back over her shoulder at the wave coming in.",
            "negative_prompt": "blur, distort, and low quality",
            "aspect_ratio": "16:9",
            "duration": 5,
            "generate_audio": false,
            "cfg_scale": 0.5
          }'
          ```

          ```python Python theme={null}
          import os
          import requests

          response = requests.post(
              "https://hub.oxen.ai/api/ai/videos/generate",
              headers={
                  "Content-Type": "application/json",
                  "Authorization": f"Bearer {os.environ['OXEN_API_KEY']}",
              },
              json={
                  "model": "kling-video-v2-6-pro-text-to-video",
                  "prompt": "A close up of a blonde woman surfer in a wet suit, with a wave behind her, paddling to get momentum before standing up, on her stomach, looking back over her shoulder at the wave coming in.",
                  "negative_prompt": "blur, distort, and low quality",
                  "aspect_ratio": "16:9",
                  "duration": 5,
                  "generate_audio": false,
                  "cfg_scale": 0.5
              },
          )
          response.raise_for_status()
          print(response.json())
          ```
        </CodeGroup>
      </Tab>
    </Tabs>
  </Tab>

  <Tab title="Async">
    See the [async queue reference](/inference-api/reference/async_queue) for more details.

    <Tabs>
      <Tab title="Minimal">
        <CodeGroup>
          ```bash cURL theme={null}
          # Enqueue, capture the generation id.
          GEN_ID=$(curl -s -X POST https://hub.oxen.ai/api/ai/queue \
            -H "Content-Type: application/json" \
            -H "Authorization: Bearer $OXEN_API_KEY" \
            -d '{
            "model": "kling-video-v2-6-pro-text-to-video",
            "prompt": "A close up of a blonde woman surfer in a wet suit, with a wave behind her, paddling to get momentum before standing up, on her stomach, looking back over her shoulder at the wave coming in."
          }' | jq -r '.generations[0].generation_id')

          # Poll until the generation reaches a terminal status.
          while true; do
            STATUS=$(curl -s -H "Authorization: Bearer $OXEN_API_KEY" \
              "https://hub.oxen.ai/api/ai/queue/$GEN_ID" | jq -r '.status')
            echo "Status: $STATUS"
            case $STATUS in succeeded|failed|cancelled) break;; esac
            sleep 5
          done

          # Print the result.
          curl -s -H "Authorization: Bearer $OXEN_API_KEY" \
            "https://hub.oxen.ai/api/ai/queue/$GEN_ID" | jq .
          ```

          ```python Python theme={null}
          import os
          import time
          import requests

          HEADERS = {
              "Content-Type": "application/json",
              "Authorization": f"Bearer {os.environ['OXEN_API_KEY']}",
          }

          enqueue = requests.post(
              "https://hub.oxen.ai/api/ai/queue",
              headers=HEADERS,
              json={
                  "model": "kling-video-v2-6-pro-text-to-video",
                  "prompt": "A close up of a blonde woman surfer in a wet suit, with a wave behind her, paddling to get momentum before standing up, on her stomach, looking back over her shoulder at the wave coming in."
              },
          )
          enqueue.raise_for_status()
          generation_id = enqueue.json()["generations"][0]["generation_id"]

          while True:
              data = requests.get(
                  f"https://hub.oxen.ai/api/ai/queue/{generation_id}",
                  headers=HEADERS,
              ).json()
              if data["status"] in {"succeeded", "failed", "cancelled"}:
                  break
              time.sleep(5)

          if data["status"] == "succeeded":
              print(f"Result: {data['result_url']}")
          else:
              print(f"Generation {data['status']}: {data.get('error_message')}")
          ```
        </CodeGroup>
      </Tab>

      <Tab title="All parameters">
        <CodeGroup>
          ```bash cURL theme={null}
          # Enqueue, capture the generation id.
          GEN_ID=$(curl -s -X POST https://hub.oxen.ai/api/ai/queue \
            -H "Content-Type: application/json" \
            -H "Authorization: Bearer $OXEN_API_KEY" \
            -d '{
            "model": "kling-video-v2-6-pro-text-to-video",
            "prompt": "A close up of a blonde woman surfer in a wet suit, with a wave behind her, paddling to get momentum before standing up, on her stomach, looking back over her shoulder at the wave coming in.",
            "negative_prompt": "blur, distort, and low quality",
            "aspect_ratio": "16:9",
            "duration": 5,
            "generate_audio": false,
            "cfg_scale": 0.5
          }' | jq -r '.generations[0].generation_id')

          # Poll until the generation reaches a terminal status.
          while true; do
            STATUS=$(curl -s -H "Authorization: Bearer $OXEN_API_KEY" \
              "https://hub.oxen.ai/api/ai/queue/$GEN_ID" | jq -r '.status')
            echo "Status: $STATUS"
            case $STATUS in succeeded|failed|cancelled) break;; esac
            sleep 5
          done

          # Print the result.
          curl -s -H "Authorization: Bearer $OXEN_API_KEY" \
            "https://hub.oxen.ai/api/ai/queue/$GEN_ID" | jq .
          ```

          ```python Python theme={null}
          import os
          import time
          import requests

          HEADERS = {
              "Content-Type": "application/json",
              "Authorization": f"Bearer {os.environ['OXEN_API_KEY']}",
          }

          enqueue = requests.post(
              "https://hub.oxen.ai/api/ai/queue",
              headers=HEADERS,
              json={
                  "model": "kling-video-v2-6-pro-text-to-video",
                  "prompt": "A close up of a blonde woman surfer in a wet suit, with a wave behind her, paddling to get momentum before standing up, on her stomach, looking back over her shoulder at the wave coming in.",
                  "negative_prompt": "blur, distort, and low quality",
                  "aspect_ratio": "16:9",
                  "duration": 5,
                  "generate_audio": false,
                  "cfg_scale": 0.5
              },
          )
          enqueue.raise_for_status()
          generation_id = enqueue.json()["generations"][0]["generation_id"]

          while True:
              data = requests.get(
                  f"https://hub.oxen.ai/api/ai/queue/{generation_id}",
                  headers=HEADERS,
              ).json()
              if data["status"] in {"succeeded", "failed", "cancelled"}:
                  break
              time.sleep(5)

          if data["status"] == "succeeded":
              print(f"Result: {data['result_url']}")
          else:
              print(f"Generation {data['status']}: {data.get('error_message')}")
          ```
        </CodeGroup>
      </Tab>
    </Tabs>
  </Tab>

  <Tab title="Async with SSE">
    See the [async queue reference](/inference-api/reference/async_queue) for more details.

    <Tabs>
      <Tab title="Minimal">
        <CodeGroup>
          ```bash cURL theme={null}
          # Enqueue, capture the generation id.
          GEN_ID=$(curl -s -X POST https://hub.oxen.ai/api/ai/queue \
            -H "Content-Type: application/json" \
            -H "Authorization: Bearer $OXEN_API_KEY" \
            -d '{
            "model": "kling-video-v2-6-pro-text-to-video",
            "prompt": "A close up of a blonde woman surfer in a wet suit, with a wave behind her, paddling to get momentum before standing up, on her stomach, looking back over her shoulder at the wave coming in."
          }' | jq -r '.generations[0].generation_id')

          # Stream the SSE channel, grab the data line that follows a
          # media_generation_completed event for our id, and pretty-print it.
          curl -sN -H "Authorization: Bearer $OXEN_API_KEY" https://hub.oxen.ai/api/events \
            | awk -v id="$GEN_ID" '
              /^event: media_generation_completed$/ { expect=1; next }
              /^data: / && expect {
                payload = substr($0, 7)
                if (index(payload, "\"generation_id\":\"" id "\"")) { print payload; exit }
                expect = 0
              }
            ' | jq .
          ```

          ```python Python theme={null}
          import json
          import os
          import requests

          API_KEY = os.environ["OXEN_API_KEY"]
          AUTH = {"Authorization": f"Bearer {API_KEY}"}

          enqueue = requests.post(
              "https://hub.oxen.ai/api/ai/queue",
              headers={**AUTH, "Content-Type": "application/json"},
              json={
                  "model": "kling-video-v2-6-pro-text-to-video",
                  "prompt": "A close up of a blonde woman surfer in a wet suit, with a wave behind her, paddling to get momentum before standing up, on her stomach, looking back over her shoulder at the wave coming in."
              },
          )
          enqueue.raise_for_status()
          generation_id = enqueue.json()["generations"][0]["generation_id"]

          with requests.get(
              "https://hub.oxen.ai/api/events",
              headers=AUTH,
              stream=True,
          ) as stream:
              event_name = None
              for line in stream.iter_lines(decode_unicode=True):
                  if line.startswith("event: "):
                      event_name = line.removeprefix("event: ")
                  elif line.startswith("data: ") and event_name == "media_generation_completed":
                      payload = json.loads(line.removeprefix("data: "))
                      if payload.get("generation_id") == generation_id:
                          print(payload)
                          break
          ```
        </CodeGroup>
      </Tab>

      <Tab title="All parameters">
        <CodeGroup>
          ```bash cURL theme={null}
          # Enqueue, capture the generation id.
          GEN_ID=$(curl -s -X POST https://hub.oxen.ai/api/ai/queue \
            -H "Content-Type: application/json" \
            -H "Authorization: Bearer $OXEN_API_KEY" \
            -d '{
            "model": "kling-video-v2-6-pro-text-to-video",
            "prompt": "A close up of a blonde woman surfer in a wet suit, with a wave behind her, paddling to get momentum before standing up, on her stomach, looking back over her shoulder at the wave coming in.",
            "negative_prompt": "blur, distort, and low quality",
            "aspect_ratio": "16:9",
            "duration": 5,
            "generate_audio": false,
            "cfg_scale": 0.5
          }' | jq -r '.generations[0].generation_id')

          # Stream the SSE channel, grab the data line that follows a
          # media_generation_completed event for our id, and pretty-print it.
          curl -sN -H "Authorization: Bearer $OXEN_API_KEY" https://hub.oxen.ai/api/events \
            | awk -v id="$GEN_ID" '
              /^event: media_generation_completed$/ { expect=1; next }
              /^data: / && expect {
                payload = substr($0, 7)
                if (index(payload, "\"generation_id\":\"" id "\"")) { print payload; exit }
                expect = 0
              }
            ' | jq .
          ```

          ```python Python theme={null}
          import json
          import os
          import requests

          API_KEY = os.environ["OXEN_API_KEY"]
          AUTH = {"Authorization": f"Bearer {API_KEY}"}

          enqueue = requests.post(
              "https://hub.oxen.ai/api/ai/queue",
              headers={**AUTH, "Content-Type": "application/json"},
              json={
                  "model": "kling-video-v2-6-pro-text-to-video",
                  "prompt": "A close up of a blonde woman surfer in a wet suit, with a wave behind her, paddling to get momentum before standing up, on her stomach, looking back over her shoulder at the wave coming in.",
                  "negative_prompt": "blur, distort, and low quality",
                  "aspect_ratio": "16:9",
                  "duration": 5,
                  "generate_audio": false,
                  "cfg_scale": 0.5
              },
          )
          enqueue.raise_for_status()
          generation_id = enqueue.json()["generations"][0]["generation_id"]

          with requests.get(
              "https://hub.oxen.ai/api/events",
              headers=AUTH,
              stream=True,
          ) as stream:
              event_name = None
              for line in stream.iter_lines(decode_unicode=True):
                  if line.startswith("event: "):
                      event_name = line.removeprefix("event: ")
                  elif line.startswith("data: ") and event_name == "media_generation_completed":
                      payload = json.loads(line.removeprefix("data: "))
                      if payload.get("generation_id") == generation_id:
                          print(payload)
                          break
          ```
        </CodeGroup>
      </Tab>
    </Tabs>
  </Tab>
</Tabs>

## Fetch model details

The [models endpoint](/inference-api/reference/models/overview) returns the full model object, including its `json_request_schema`.

```bash theme={null}
curl -H "Authorization: Bearer $OXEN_API_KEY" https://hub.oxen.ai/api/ai/models/kling-video-v2-6-pro-text-to-video
```

## Request parameters

### Required parameters

| Field    | Type     | Default                                                                                                                                                                                           | Description                                                                                       |
| -------- | -------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------- |
| `prompt` | `string` | `"A close up of a blonde woman surfer in a wet suit, with a wave behind her, paddling to get momentum before standing up, on her stomach, looking back over her shoulder at the wave coming in."` | Text description of what you want to generate, or the instruction on how to edit the given image. |

### Optional parameters

| Field             | Type      | Default                            | Description                                                                                                                                      |
| ----------------- | --------- | ---------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------ |
| `negative_prompt` | `string`  | `"blur, distort, and low quality"` | Text description of what you *do not* want the model to do.                                                                                      |
| `aspect_ratio`    | `string`  | `"16:9"`                           | Video aspect ratio One of: 9:16, 16:9, 1:1.                                                                                                      |
| `duration`        | `integer` | `5`                                | Video duration in seconds One of: 5, 10.                                                                                                         |
| `generate_audio`  | `boolean` | `false`                            | Generate audio with the video.                                                                                                                   |
| `cfg_scale`       | `number`  | `0.5`                              | The CFG (Classifier Free Guidance) scale is a measure of how close you want the model to stick to your prompt. Default value: 0.5 Range: 0 – 10. |