> ## Documentation Index
> Fetch the complete documentation index at: https://docs.videodb.io/llms.txt
> Use this file to discover all available pages before exploring further.
# Sandbox Models
> Available models for VideoDB Sandbox Compute and their minimum tiers.
Sandbox models run on VideoDB Sandbox Compute. Pick a model, create a sandbox on a compatible tier, wait until it is active, and pass the sandbox ID to a supported workflow.
Sandbox models can be used across VideoDB workflows, including understanding analyzers, indexing pipelines, text-to-speech, image generation, audio generation, and text generation.
Use model names exactly as listed unless your VideoDB team contact gives you an alias.
***
## Model catalog
| Model | Type | Minimum tier | Good for |
| ----------------------------------- | --------------------- | ------------ | ----------------------------------------- |
| `google/gemma-4-E2B-it-FP8` | VLM | `small` | lightweight visual scene understanding |
| `Qwen/Qwen3.5-9B-FP8` | VLM / text generation | `small` | lightweight visual and text reasoning |
| `google/gemma-4-12B-it-FP8` | VLM | `medium` | balanced visual scene understanding |
| `google/gemma-4-26B-A4B-it-FP8` | VLM | `medium` | higher-quality visual scene understanding |
| `google/gemma-4-31B-it-FP8` | VLM | `medium` | advanced visual scene understanding |
| `Qwen/Qwen3.5-27B-FP8` | VLM / text generation | `medium` | larger visual and text reasoning |
| `openai/whisper-large-v3-turbo` | speech-to-text | `small` | transcription and speech recognition |
| `k2-fsa/OmniVoice` | text-to-speech | `small` | TTS, voice design, and voice cloning |
| `black-forest-labs/FLUX.1-dev` | image generation | `medium` | text-to-image generation |
| `stabilityai/stable-audio-open-1.0` | audio generation | `small` | text-to-audio and sound generation |
| `rtdetr-v2-r50vd` | object detection | `small` | object detection workflows |
***
## Choose a tier
Use the smallest tier that supports the largest model in your workflow.
```python theme={null}
from videodb import SandboxTier
# Small-tier models: OmniVoice, Whisper, Qwen 9B, lightweight VLMs, and object detection
small_sandbox = conn.create_sandbox(
tier=SandboxTier.small,
model_categories=["vlm", "text_to_speech", "speech_to_text", "object_detection"],
)
# Medium-tier models: FLUX and larger VLMs
medium_sandbox = conn.create_sandbox(
tier=SandboxTier.medium,
model_categories=["vlm", "image_generation"],
)
```
If a workflow uses both small and medium models, create a `medium` sandbox and reuse it across those jobs.
***
## Where to pass the model name
| Workflow | Model field | Sandbox field |
| ----------------------- | ----------------------- | ---------------------------- |
| Understanding analyzers | analyzer `config.model` | analyzer `config.sandbox_id` |
| Generation APIs | `model_name` | `sandbox_id` |
For hosted/default models, omit `sandbox_id`. For models listed on this page, pass a compatible sandbox ID.
***
## Use a model in Understanding and Indexing
Use the sandbox model during understanding, then index the produced artifact with the standard `video.index(...)` interface.
```python theme={null}
sandbox = conn.create_sandbox(
tier=SandboxTier.medium,
models=["google/gemma-4-31B-it-FP8"],
)
sandbox.wait_for_ready()
understanding = video.understand(
analyzers=[
{
"type": "vlm",
"name": "scene",
"config": {
"model": "google/gemma-4-31B-it-FP8",
"sandbox_id": sandbox.id,
"prompt": "Describe each scene in detail.",
"schema": {
"scene_description": "string",
"activity": "string",
"setting": "string",
},
},
}
],
)
understanding.wait_until_complete()
scene = understanding.get_analyzer("scene")
video.index(
name="scene",
source=scene,
use_for=["semantic", "query"],
fields={
"semantic": ["scene_description", "activity", "setting"],
"filter": ["activity", "setting"],
},
)
```
For object detection, configure the model and sandbox in the analyzer, then choose filter and aggregation fields at indexing time:
```python theme={null}
understanding = video.understand(
analyzers=[
{
"type": "object_detection",
"name": "objects",
"config": {
"model": "rtdetr-v2-r50vd",
"sandbox_id": sandbox.id,
},
}
],
)
understanding.wait_until_complete()
objects = understanding.get_analyzer("objects")
video.index(
name="objects",
source=objects,
use_for=["query", "aggregate"],
fields={
"filter": ["object_labels", "object_counts"],
"aggregate": ["object_labels"],
},
)
```
***
## Use a model in generation APIs
Generation APIs use `model_name` and `sandbox_id`.
```python theme={null}
job = coll.generate_image(
prompt="A cinematic product photo of a smart camera",
model_name="black-forest-labs/FLUX.1-dev",
sandbox_id=sandbox.id,
)
image = job.wait(timeout=900, interval=5)
```
```python theme={null}
job = coll.generate_voice(
text="Welcome to VideoDB Sandbox Compute.",
model_name="k2-fsa/OmniVoice",
sandbox_id=sandbox.id,
)
audio = job.wait(timeout=900, interval=5)
```
***
## Pricing and limits
Sandbox pricing and concurrency limits are based on the sandbox tier, not the individual model. See [Sandbox Compute](/pages/core-concepts/sandbox-compute#pricing-and-limits) for current pricing and limits.
Create, use, and stop sandbox compute.
Configure analyzers and model selection.
Index artifacts produced by sandbox models.