MiniMax releases MiniMax H3, a general-purpose multimodal generation model. MiniMax H3 is not a text-to-video model with add-ons. MiniMax describes it as a general-purpose multimodal generation model that reads text, images, video, and audio as one unified context and returns video with native stereo sound. The mains specs include: 2K output, 4–15 seconds, integer durations only.
Previous video stacks split into text-to-video, image-to-video, first-and-last-frame, subject reference, motion reference, and video editing, each often a separate expert model. MiniMax H3 folds those into one pretraining paradigm where reference and editing relationships are expressed in natural language. MiniMax’s example prompt makes the point: reference the camera movement from Video 1, have the character in Image 2 sing, match the vocals to Audio 3.
Is it deployable?
Today: yes, through the API and no, on your own hardware. MiniMax launched H3 on July 31, 2026 with the model live in the platform API under the model ID MiniMax-H3 and in the consumer Hailuo AI app.
Industries: MiniMax positions MiniMax H3 for advertising, branding, e-commerce, product design, UI/UX, and gaming along with film pre-visualization and retail catalog media.
Applications: Ad variant generation, product and listing videos, animated posters, film title sequences, website hero loops, character-consistent game cinematics, and video-to-video motion transfer.
The API surface
The video generation guide documents three entry modes: text-to-video, first/last-frame image-to-video, and reference generation. Behind one endpoint and an asynchronous three-step flow: create a task, poll task_id, download content.url.
Input limits worth designing around:
Reference images: up to 9. Reference videos: up to 3 clips, 2–15 s each, ≤15 s total. Reference audio: up to 3 clips, and audio cannot be sent without an accompanying image or video.
Mixed input caps at 12 files total. Prompt length ≤7,000 characters; request body ≤64 MB, with URL input recommended for large assets.
File sizes: video ≤50 MB, image ≤30 MB, audio ≤15 MB, per asset.
Formats: H.264/H.265 video, JPG/PNG/WEBP/HEIC/HEIF images, WAV/MP3 audio.
Four technical pieces doing the work
Contextual Omni Representation: MiniMax rebuilt captioning so it describes the relationship between context and target video, not just the target. Most source material requires roughly 100K tokens of inference, distilled to about 4K tokens on average. Language is the bridge that turns a fixed task set into an open, descriptive one.
H3-VAE: A full tokenizer overhaul. Its high compression ratio delivers a stated 4× gain in effective sequence length, cutting training and inference cost and it is the enabling technology for native 2K.
H3-Omni Transformer: MiniMax explicitly set aside the Hailuo-02 architecture here. Multimodal context tripled sequence-length variance, so the training architecture separates understanding and generation workloads and tunes hardware utilization for each. Reported result: end-to-end training throughput up nearly 30%.
In-Context Regeneration: Instead of a bolt-on super-resolution module, the base model regenerates its own low-resolution output in-context, re-reading the original multimodal context. That is what recovers small text and fine detail that conventional upscalers guess at — directly relevant to brand and product rendering.