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MiniMax Open-Sources H3 Video Model

MiniMax Open-Sources H3 Video Model

Open-Source Models

MiniMax open-sourced H3 on August 3, 2026, a unified video generation model that produces 2K clips with native stereo audio from mixed text, image, video, and audio references.

MiniMax open-sourced its flagship video generation model, H3, on August 3, 2026, giving anyone the ability to download and run a model that generates 2K video with native stereo audio from a mix of text, images, video, and audio references in a single prompt. It’s the first time the Chinese AI company has fully open-sourced its top video model.

Quick facts

  • MiniMax announced H3 (also called Hailuo 3.0) on July 31, 2026 as an API-only release, then open-sourced the weights on August 3.
  • H3 generates 4-15 second clips at up to 2K resolution with native stereo audio, and accepts up to 9 images, 3 video clips, and 3 audio tracks together in one generation.
  • It’s a single unified model rather than separate specialist models for text-to-video, image-to-video, and video editing, which was the norm for prior-generation video tools.
  • MiniMax says H3’s per-second pricing at 2K is less than a third of mainstream competing models.
  • The open-source release covers H3-Base under a MiniMax H3 Community License, with two task-specific checkpoints and quantized variants already supported in ComfyUI.

What makes H3 different from a typical video generator

Most video generation tools split tasks into separate expert models: one for text-to-video, another for image-to-video, another for editing an existing clip. Per MiniMax’s own announcement, H3 folds all of that into a single model built around what the company calls Contextual Omni Representation, treating the relationship between reference material and the target video as something described in natural language rather than handled by a separate specialist system for each task. In practice, that means a single prompt can reference a camera movement from one video, a character’s appearance from an image, and a voice from an audio clip, and H3 will carry all three through into one coherent result.

The model also supports what MiniMax calls 2K in-context regeneration, upscaling a 768p output back to 2K while reusing the original generation context, and covers 11 languages for text and dialogue.

Why open-sourcing it now is a strategic bet

Closed, proprietary models have dominated video generation specifically because the compute and data requirements are so much steeper than text models. MiniMax open-sourcing its flagship video model, rather than keeping it API-only the way most competitors do, is a deliberate move to build developer mindshare the same way open-weight language models have done: let anyone self-host, fine-tune, and build on it, and become the default choice by virtue of being both capable and unrestricted, rather than by API pricing alone. Same-day availability on hosting platforms like fal.ai and native ComfyUI support suggest MiniMax coordinated the release with the broader open-source tooling ecosystem rather than dropping weights and leaving integration to catch up later.

How it fits the broader video generation landscape

The timing is notable given that OpenAI’s Sora, once the most talked-about name in AI video, is in the process of being wound down entirely, with its API scheduled for shutdown in September. That leaves genuine room in the category, and H3 arrives alongside continued pushes from Google’s Veo line and Runway, competing on a mix of quality, price, and now, in MiniMax’s case, openness.

Common questions

Do I need to self-host H3 to use it? No. It’s available through the MiniMax Open Platform API and hosting partners like fal.ai from launch, in addition to the open weights for anyone who wants to run it themselves.

What license is it released under? The open weights are released under a MiniMax H3 Community License Agreement, covering the H3-Base checkpoints; check the license terms directly for any commercial-use conditions before deploying it in a paid product.

Is Hailuo 3.0 a different model from H3? No. MiniMax H3 is the official model name, and Hailuo 3.0 (also written Hailuo 03) is the name it’s marketed under inside MiniMax’s Hailuo AI consumer app. They’re the same model.

Key takeaway

If you build on video generation and have been locked into a single API provider, H3’s open weights are worth evaluating specifically because they remove that lock-in: you can self-host, fine-tune, or run it through a hosting provider of your choice, at pricing MiniMax says undercuts mainstream closed alternatives at comparable quality.

Up Next
OpenAI’s Astra Solved Ten Math Problems

OpenAI’s Astra Solved Ten Math Problems

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OpenAI introduced its next major model, Astra, by publishing ten new, independently verifiable results to decades-old open problems in mathematics and theoretical computer science.

OpenAI introduced its next major model on August 1, 2026, and it did so by publishing ten new solutions to open mathematics and theoretical computer science problems, several of which had gone unsolved for decades. The model, called Astra, is unreleased, but the results are already independently verifiable, because OpenAI published them as formal, machine-checked proofs rather than prose claims.

Quick facts

  • An internal, unreleased version of Astra, OpenAI’s next major model, produced new results on ten long-open problems spanning geometry, coding theory, group theory, complexity theory, and cryptography.
  • OpenAI says the compute needed to find the solutions would cost roughly $2,000 at its Sol API rates.
  • Each result was formalized into a Lean certificate, a machine-checkable proof format, and published on GitHub for anyone to verify independently.
  • Two of the results directly resolve named open problems: ErdÅ‘s problem 183 (multicolor Ramsey numbers) and ErdÅ‘s problems 146 and 180 (extremal graph theory).
  • OpenAI explicitly states it takes responsibility for the manuscripts’ correctness while the mathematical arguments themselves were generated by the system, not a human mathematician.

What was actually solved

Per OpenAI’s own publication, the ten results include new upper bounds on sphere-packing density, exponentially improved bounds on binary and spherical error-correcting codes, a construction establishing the existence of non-sofic groups (a central open question in group theory), a disproof of Connes’s rigidity conjecture, new lower bounds on arithmetic circuit complexity for computing the permanent, an exponential parallel repetition theorem for quantum games, polynomial-factor hardness results for the closest vector problem (a foundational post-quantum cryptography question), a resolution of Ehrhart’s volume conjecture, and the two ErdÅ‘s problems noted above. OpenAI describes all ten as problems that had seen no progress on their main result for at least a decade, and in most cases much longer.

Worth being precise about: this is a separate batch of results from the ErdÅ‘s unit-distance conjecture disproof OpenAI announced in May 2026, which several secondary outlets have conflated with this release. That earlier result is cited in this announcement as prior work that helped inspire further mathematics, not one of today’s ten.

Why the Lean proofs are the whole point

AI models are well known to produce confident, plausible-sounding claims that turn out to be wrong, which is exactly why the format of this announcement matters as much as its content. A Lean proof is written in a formal language a computer can check mechanically, step by step, with no room for hand-waving. OpenAI published the Lean certificates for all ten results on GitHub, meaning any mathematician, not just OpenAI, can run the verifier and confirm the logic holds. That’s a meaningfully different claim than a benchmark score, which can be gamed, memorized, or cherry-picked. A formally verified proof either checks out or it doesn’t.

How OpenAI is handling attribution

OpenAI’s own writeup addresses a question the mathematical community has been actively debating: who gets credit when an AI system generates a proof. The company points to the Leiden declaration on AI and Mathematics and states plainly that claiming human authorship for a proof generated entirely by an AI system would misrepresent both the system’s contribution and human intellectual work. OpenAI says it helped prepare the manuscripts and formalize the Lean proofs, and takes responsibility for their correctness, while the underlying mathematical arguments came from Astra itself.

Why $2,000 is the detail worth remembering

The cost figure reframes what this milestone actually means. Ten problems that resisted expert mathematicians for a decade or more were solved for roughly the cost of a mid-range laptop in compute. That doesn’t replace mathematicians, choosing which problems matter and interpreting what a result means still requires human judgment, but it does suggest that for a certain class of well-posed, verifiable problems, progress increasingly scales with available compute rather than being bottlenecked purely by the supply of specialists working on them.

Common questions

Is Astra publicly available? No. The math results came from an internal, unreleased version. OpenAI hasn’t given a public release date or a public spec sheet for Astra.

Did AI disprove the ErdÅ‘s unit-distance conjecture today? No, that result was announced separately in May 2026. Today’s announcement is ten different, new results.

How do I check these proofs myself? OpenAI published the Lean certificates on GitHub; running them through the Lean proof verifier confirms whether each argument holds, independent of OpenAI’s own claims.

Does this mean AI can do all mathematics now? No. These ten problems were selected in areas well suited to systematic search and construction, which is where current AI systems are strongest. Broader mathematical creativity and problem selection still rest with human mathematicians.

Key takeaway

This isn’t evidence of general intelligence, math has clear rules and mechanically checkable answers, which is exactly the kind of problem AI systems are well suited to. But it is a genuine, independently verifiable research contribution, not a benchmark stunt, and it’s a preview of how OpenAI plans to introduce Astra when the model itself eventually ships.