MiniMax H3 is an omni-modal generative system from MiniMaxAI that produces video with native stereo audio from text, images, video, and audio inputs. It understands multimodal context as a single unified sequence rather than treating each input type separately, and predicts video and audio latents jointly in one forward pass so speech, sound effects, and music land in sync with the picture.

Key Features

• Native audio-video generation: Video and 32 kHz stereo audio are modeled together, not stitched from separate systems
• Multiple input modes: Text-only, first-frame, last-frame, first-and-last-frame, and omni-reference conditioning
• Flexible output shapes: A wide range of aspect ratios including 21:9, 16:9, 4:3, 1:1, 3:4, and 9:16
• 24 FPS output with durations from 4 to 15 seconds
• Multilingual dialogue: Stable support for Arabic, Chinese, English, French, German, Italian, Japanese, Korean, Portuguese, Russian, and Spanish, with partial support for additional languages

Architecture

The open-weights release is H3-Base, which generates 768p audio-video output. It encodes each modality with its own encoder or VAE, packs the encoded representations into one multimodal sequence, and applies three-dimensional multimodal rotary position embeddings across the temporal and two spatial dimensions before handing the sequence to the H3-Omni-Transformer.

The H3-Omni-Transformer is a 33B-parameter dense, single-stream transformer. Its attention and feed-forward layers carry no modality-specific structure; modality-specific parameters are confined to the input and output layers and to the AdaLN branches. Roughly 13B of the parameters sit in AdaLN-related branches whose modulation outputs can be precomputed and cached, so they do not need to be loaded for inference-only deployment.

Text and visual inputs are encoded by the H3-Encoder, which uses the full pretrained weights of Qwen3-VL-32B and passes hidden states from its 50th layer into the transformer. Visual inputs are additionally encoded by H3-VisualVAE, a temporally causal video autoencoder with 16x spatial compression, 4x temporal compression, and 24 latent channels. H3-AudioVAE compresses 32 kHz audio into latent tokens at a 40 Hz temporal rate, running the left and right channels independently through a shared encoder and decoder and recombining them for stereo.

Model Variants

MiniMax H3 ships as two task-specific checkpoints:

• H3-Base-FL2VA handles first-and-last-frame mode. With no image input it runs text-to-video; with one image it generates from a first or last frame; with two images it generates between a first and last frame.
• H3-Base-Ref2VA handles omni-reference mode, accepting up to 9 images, up to 3 video clips, and up to 3 audio clips as references, with a maximum of 12 files across all input types and a total duration of 15 seconds.

Both checkpoints are CFG-distilled and released at BF16 precision.

Use Cases

• Short-form video with dialogue, sound effects, and music generated together
• Character and product consistency across shots using omni-reference conditioning
• First-and-last-frame interpolation for controlled transitions
• Storyboard-to-motion previews from a single still frame
• Multilingual dialogue scenes without separate voice generation
• Creative experimentation with mixed image, video, and audio conditioning

Prompting

Output quality depends heavily on how the multimodal context is described. MiniMaxAI ships a hosted preprocessing system, H3-Context-IR, that parses instructions, associates inputs across modalities, and serializes its understanding into a structured representation the base model consumes. That component is not part of the open-weights release, so detailed, explicit prompts that spell out the relationship between each reference input and the intended output matter more here than with single-modality video models.

Integration

MiniMax H3 runs in ComfyUI through native nodes added in ComfyUI 0.30.0, with text-to-video, image-to-video, and reference-to-video workflow templates. Repackaged single-file weights for ComfyUI are published at Comfy-Org/MiniMax-H3.

For more details about the model architecture and capabilities, see the model page on Hugging Face.