SenseNova-6.8-Code2Video

We present SenseNova 6.8 Code2Video, a lightweight open model for code-driven video creation. The model builds and renders videos by generating executable code, and currently focuses on short videos implemented entirely in code. Preliminary results show good visual composition and motion choreography in motion design, suggesting the potential of lightweight models for programmatic video creation.

We chose code as the entry point to video creation partly for its ability to connect different models and creative tools. Beyond building and rendering visual content directly, code can call image generation, video generation, 3D modelling and video editing models and tools, chaining generation, editing and compositing into a complete creative workflow.

We believe code can become a common language between ideas and finished work. Our long-term goal is a model that coordinates different generative models and creative tools through code to create digital content across media. SenseNova 6.8 Code2Video is a first step in this direction. Next, we will scale the model further and extend these abilities to editing, compositing, animation and 3D content creation.

Released Resources

Deployment

LightLLM

docker run --rm --gpus all --ipc=host -p 8000:8000 \
  -v /path/to/SenseNova-6.8-Code2Video:/model \
  ghcr.io/modeltc/lightllm:main \
  python -m lightllm.server.api_server \
    --model_dir /model \
    --tp 4 \
    --max_req_total_len 524288 \
    --host 0.0.0.0 --port 8000

vLLM

docker run --rm --gpus all --ipc=host -p 8000:8000 \
  -e VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 \
  -v /path/to/SenseNova-6.8-Code2Video:/model \
  vllm/vllm-openai:latest \
    --model /model \
    --tensor-parallel-size 4 \
    --max-model-len 524288 \
    --reasoning-parser qwen3 \
    --enable-auto-tool-choice \
    --tool-call-parser qwen3_coder \
    --host 0.0.0.0 --port 8000

SGLang

docker run --rm --gpus all --ipc=host -p 8000:8000 \
  -e SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1 \
  -v /path/to/SenseNova-6.8-Code2Video:/model \
  lmsysorg/sglang:latest \
  python -m sglang.launch_server \
    --model-path /model \
    --tp-size 4 \
    --context-length 524288 \
    --reasoning-parser qwen3 \
    --tool-call-parser qwen3_coder \
    --host 0.0.0.0 --port 8000

Recommended harness: Claude Code

A complete film is a long-horizon task: the model writes and renders code, reviews the frames and revises, turn after turn. SenseNova 6.8 Code2Video sustains runs of more than 10 hours and more than 2,000 turns. Configure Claude Code for runs of that length:

Set the context window explicitly, since Claude Code otherwise treats an unknown model as a 200K model. With compaction at 0.8 and 64K of output, every request stays within the 512K server limit.

Put the endpoint and these variables in a private settings file, and supply the credential through ANTHROPIC_AUTH_TOKEN or ANTHROPIC_API_KEY:

{
  "env": {
    "ANTHROPIC_BASE_URL": "https://your-endpoint.example.com",
    "ANTHROPIC_MODEL": "your-deployed-model-name",
    "CLAUDE_CODE_MAX_CONTEXT_TOKENS": "524288",
    "CLAUDE_AUTOCOMPACT_PCT_OVERRIDE": "80",
    "CLAUDE_CODE_MAX_OUTPUT_TOKENS": "64000"
  },
  "model": "your-deployed-model-name"
}

Avoid slow shell initialization: every Bash call starts a new shell.

Usage

Producing a film has three stages:

Stage 2 with the settings above:

cd /path/to/task   # contains dense_prompt.md, assets/ and .claude/skills/code-video
claude -p --settings /path/to/private-settings.json --model your-deployed-model-name \
  --max-turns 2000 --permission-mode acceptEdits --allowedTools "Bash,Read,Write,Edit,Glob,Grep,Skill" \
  'Use the project code-video skill. Read .claude/skills/code-video/SKILL.md and dense_prompt.md, then the relevant craft guides. Produce the complete video and deliver out/final.mp4, SPEC.md and VERIFY.md.'

The run executes shell commands without asking, so start it in an isolated working environment. For a revision, run claude -p --continue in the same folder with the creator's notes as the prompt.

Copy the skill into .claude/skills/code-video unchanged and put task-specific requirements in the prompt. Setup steps and dependencies are in the GitHub repository.

Production trajectories

[trajectories/all.openai.jsonl](https