Minimaxi Release Notes

We’re excited to announce that MiniMax (Hailuo AI) is partnering with VideoTube to make AI video creation faster and easier than ever. With Hailuo’s latest model, Hailuo-2.3, now built into AI Video Generator, you can turn your ideas into high-quality videos — no editing skills required.

What Is VideoTube?

VideoTube is an all-in-one AI video platform that helps you turn one simple idea into complete, ready-to-post shorts — for free.
You can add images, audio, and story elements in just a few clicks, and create professional YouTube content that helps your channel grow faster.

What's New with Hailuo-2.3

This partnership brings the newest Hailuo-2.3 model to VideoTube, offering:

• Text-to-video and image-to-video generation — just write a prompt or upload an image.
• Professional-level videos (up to 1080p) that render in under a minute.
• Realistic visuals — natural human motion, cinematic effects, and unique styles like Pixar or surreal waterlight.
• Smooth workflow — upload/type, choose style, generate, edit, and share directly within VideoTube.

Who Can Benefit

• Creators and influencers making short-form videos.
• Marketers needing quick, on-brand video content.
• Teachers and trainers who want visual, easy-to-understand lessons.
• Small teams or studios looking for big results without big budgets.

How to Use Hailuo-2.3 in VideoTube

• Visit videotube.ai/image-to-video
• Upload an image or type a short text prompt
• Choose your preferred resolution, clip length (e.g., 6 s or 10 s), and style
• Click “Generate” — thanks to MiniMax's optimized engine, typical image-to-video clips render in under a minute
• Use VideoTube's editing tools to add subtitles, logos, and transitions

Original source Report a problem

Since MiniMax-M2's launch last week, we have seen a surge in community adoption and usage. Yesterday M2 became one of the top 3 models in usage on OpenRouter. However, we have also observed incorrrect implementations of M2, especially regarding interleaved thinking, which significantly reduce the model's performance.

During the very early stage of developing M2, we discovered that interleaved thinking is important in both agentic and coding applications. Since most current models, apart from Anthropic Claude, do not fully support interleaved thinking, we believe it hasn't yet become a universal convention. From users' feedback, we've also noticed that interleaved thinking is sometimes not applied correctly in practice. To address this, we'd like to share our understanding on how to use it effectively across different API interfaces to achieve better results.

Why is Interleaved Thinking Important for M2?

Interleaved thinking is essential for LLM agents: it means alternating between explicit reasoning and tool use, while carrying that reasoning forward between steps. This process significantly enhances planning, self‑correction, and reliability in long workflows. (See Anthropic’s guidance on interleaved thinking for more background). In practice, it transforms long, tool‑heavy tasks into a stable plan act reflect loop, reducing state drift and repeated mistakes while keeping actions grounded in fresh evidence. Interleaved thinking also improves debuggability: reasoning snapshots make failures explainable and recoverable, and raise sample‑efficiency by reusing hypotheses, constraints, and partial conclusions instead of re‑deriving them each step. For best results, interleave thinking with tool feedback rather than front‑loading it, and persist the chain of thought so it compounds across turns.

From community feedback, we've often observed failures to preserve prior-round thinking state across multi-turn interactions with M2. The root cause is that the widely-used OpenAI Chat Completion API does not support passing reasoning content back in subsequent requests. Although the Anthropic API natively supports this capability, the community has provided less support for models beyond Claude, and many applications still omit passing back the previous turns' thinking in their Anthropic API implementations. This situation has resulted in poor support for Interleaved Thinking for new models. To fully unlock M2's capabilities, preserving the reasoning process across multi-turn interactions is essential.

In MiniMax-M2, interleaved CoT works most effectively when prior‑round reasoning is preserved and fed back across turns. The model reasons between tool calls and carries forward plans, hypotheses, constraints, and intermediate conclusions — this accumulated state is the backbone of reliability. When prior state is dropped, cumulative understanding breaks down, state drift increases, self‑correction weakens, and planning degrades — especially on long‑horizon toolchains and run‑and‑fix loops.

Retaining prior‑round thinking state improves performance significantly compared to discarding it, as evident across benchmarks: SWE‑Bench Verified 69.4 vs. 67.2 (9;=+2.2; +3.3%), Tau^2 87 vs. 64 (9;=+23; +35.9%), BrowseComp 44.0 vs. 31.4 (9;=+12.6; +40.1%), GAIA 75.7 vs. 67.9 (9;=+7.8; +11.5%), and xBench 72.0 vs. 66.0 (9;=+6.0; +9.1%).

Keep the interleaved thinking state intact is important. Reliability isn’t just about what LLM think now; it’s about whether LLM can revisit and revise what it thought before. Interleaved thinking operationalizes this: plan act reflect, with state preserved so reflection compounds and corrections propagate across turns.

Interleaved Thinking Implemented Correctly

Enabling Interleaved Thinking in MiniMax-M2

We provide best-in-class interleaved thinking support for MiniMax-M2 on our open API platform: https://platform.minimax.io. For best performance and compatibility, we strongly recommd using our official API. In general, MiniMax offers two API interfaces:

OpenAI-Compatible API

Now, when calling the M2 model through the MiniMax OpenAI-Compatible API, you can experience:

• A separate reasoning_details field: The model's reasoning process is returned in a separate reasoning_details field, no longer mixed with the content. This makes the API structure cleaner and easier to parse.
• A complete chain of thought: Passing the reasoning_details field in subsequent requests ensures that the model maintains a complete chain of thought across multiple tool calls, leading to more accurate judgments and planning.

Code examples are available in the official guide.

Anthropic-Compatible API

The Anthropic API natively supports Interleaved Thinking. Simply append the model's complete output from each round (including thinking_blocks) to the messages history and send it to the API in subsequent requests.

For more details, please refer to the official guide.

Advancing Industry Standards for the Future of Agents

In addition to our official API platform support of interleaved thinking, we are helping partners such as OpenRouter, Ollama, Droid, Vercel, Cline to test and implement interleaved thinking correctly. Through helping our ecosystem partners, we aim to establish a unified protocol paradigm for widely supporting Interleaved Thinking among applications, OpenAI-Compatible APIs, and Anthropic-Compatible APIs — setting a foundation for the industry to build on. We believe that an open and unified standard will empower developers worldwide to easily build more capable, reliable AI agents, and foster a thriving AI ecosystem.

For partnership and collaboration, please do not hesitate to contact us at [email protected].

Links

1. Anthropic's guidance on interleaved thinking: https://docs.claude.com/en/docs/build-with-claude/extended-thinking#interleaved-thinking
2. OpenAI-Compatible API: https://platform.minimax.io/docs/guides/text-m2-function-call#openai-sdk
3. Anthropic-Compatible API: https://platform.minimax.io/docs/guides/text-m2-function-call#anthropic-sdk
4. MiniMax Official Open Platform: http://platform.minimax.io

Intelligence with Everyone！

Original source Report a problem

Artificial Analysis is a comprehensive benchmark that reflects the diversity of models’ reasoning abilities. Our newly released model, MiniMax M2, ranks Top-1 among open-source models and Top-5 among all models.

In the past, community discussions on improving reasoning abilities often focused on optimizing RL algorithms or constructing verifiable data in domains like Math and Code. In the M2 project, we conducted more "general" explorations. As a member of the Reasoning team, I'd like to share some of our findings and thoughts on data — what makes good reasoning data.

Quality of CoT and Response

The quality of CoT is reflected in its logical completeness without excessive redundancy. For instance, in instruction following tasks, overly brief CoT often leads to models skipping steps or being overconfident, causing significant harm to the model's final performance and capability generalization. For responses, we noticed that most open-source work overfits certain benchmark format patterns to achieve better leaderboard scores. While this is effective for single data directions, it severely hinders capability generalization for a general-purpose model. Therefore, when synthesizing data, we introduced format diversity and observed significant gains in multi-directional fusion experiments. Meanwhile, for potential bad cases in CoT and responses, such as hallucinations, instruction-following failures, and logical errors. We performed data cleaning using rules + LLM-as-a-judge. By continuously iterating on this misalignment elimination pipeline, we've become increasingly convinced that every bad case has its corresponding dirty training data, and improvements in data quality will inevitably be reflected in model performance.

Difficulty and Diversity of Data Distribution

Like many discussions in the community, our experiments also found that math and code data are critical for improving reasoning capabilities. The reasoning abilities brought by these two types of data often benefit all tasks, such as STEM and IF. However, we also found that we still need sufficiently diverse data to cover more domains, such as logical reasoning, science, instruction following, and open-ended creative tasks. Tasks from different domains have different thinking paradigms, and the diversity of reasoning is the foundation for capability generalization. Additionally, we noticed in our experiments that harder and more complex queries are more effective for model training, so we adjusted data distribution based on pass rate (for verifiable tasks) or complexity scores (for non-verifiable tasks).

Data Scaling

Finally, an old but important topic:
Scaling. When data quality and diversity meet the standards, increasing data scale consistently brings significant gains. Whether it's increasing the number of queries, doing 1Q-multiple-A, multi-epoch training, or even mixing data from different directions to bring more training steps, the model steadily improves. In practice, data scaling is a highly engineering-oriented problem, so we attempted to consolidate all data based on task characteristics, dividing them into two data pipelines: Verifiable and Non-Verifiable, for automated data synthesis and processing. In fact, the Reasoning team is almost entirely composed of interns, and this data pipeline effectively ensured team collaboration efficiency and consistency in data output.

Future Work

Moving forward, we will continue to delve deeper in two directions. One is compound capabilities, such as knowledge + reasoning, and the enhancement of reasoning tasks by tools in Agent scenarios. The other is how to integrate Verifiable and Non-Verifiable tasks, such as the fusion of CoT across different domains and the generalization of reasoning capabilities, as well as the unification of training methods. Our team is continuously progressing and growing. We welcome interested colleagues to join the discussion. Happy to chat!

Intelligence with Everyone！

Original source Report a problem

We are excited to announce

We are excited to announce that VEED has become a day-one launch partner for Hailuo-2.3, our latest breakthrough AI video generation model. Starting today, creators can access Hailuo-2.3 directly inside VEED's AI Playground, combining professional-grade AI generation with VEED's intuitive online editing experience.

VEED is an online video editing platform that allows users to easily create, edit, and share videos directly from their browser. It offers features like adding subtitles, templates, screen recording, AI-powered editing tools, and supports team collaboration, making it popular among content creators, marketers, and businesses for quick and professional video production.

Now, with the integration of Hailuo-2.3, creators, marketers, and businesses can go from prompt to production ready video in one seamless workflow.

Models Available in VEED

Two models are now available for immediate use inside VEED:

• MiniMax-Hailuo-2.3

  • Supports both text and image model inputs. Generates videos in 768p or 1080p, with 6 or 10 second durations. Designed for maximum visual quality and creative control.

• MiniMax-Hailuo-2.3-Fast

  • Only supports image input but gets faster. Produces 6 second clips at 768p in around 55 seconds, delivering one of the fastest AI video generation speeds in the industry.

What Sets MiniMax-Hailuo-2.3 Apart

• Exceptional human physics, enabling dynamic and fluid movements such as flips, dancing sequences (including belly dancing and waltz), and more.
• Powerful VFX capabilities, delivering cinematic realism and immersive visual effects.
• Seamless style transformation, allowing for versatile and creative aesthetic shifts.
• Advanced stylization options, offering transformations like Pixar-style visuals and surrealist effects (e.g., waterlight).

How to Use MiniMax-Hailuo-2.3 in VEED

• Step 1: Navigate to VEED's AI playground.
• Step 2: Select MiniMax-Hailuo-2.3 from the available models.
• Step 3: Input your detailed text prompt or upload an image to animate. The more specific your prompt, the better your results.
• Step 4: Choose your video duration, aspect ratio, and style preferences.
• Step 5: Click Generate and wait as MiniMax-Hailuo-2.3 creates your video—typically in under a minute for image-to-video generation.
• Step 6: Once generated, use VEED's full editing suite to add finishing touches, brand elements, or combine with other footage.

Your project automatically saves to your workspace, so you can refine it anytime

A New Standard for AI Video

Hailuo-2.3 marks a turning point. AI video is no longer experimental, it is now viable for professional and commercial workflows. With VEED as a day one launch partner, this technology is available to creators everywhere.

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