MiniMax Family Overview: Text M2.5-M3.1, Video H3, and Audio Music3

Among open Chinese model families, MiniMax is a unique case: while Qwen, DeepSeek, and Kimi all draw their biggest download numbers from text models, MiniMax's highest-downloaded product is a video generation model — MiniMax-H3 at roughly 6.4 million downloads. The text line (M2.5 → M2.7 → M3 → M3.1-Flash) keeps iterating, plus Music3 for audio generation. This "video-first, text-following" structure gives MiniMax a genuinely differentiated position. This article maps the family and its selection logic.

How MiniMax compares to Qwen and DeepSeek

MiniMax takes a genuinely different route from the two dominant text-first families. Here is how they stack up:

The key insight: MiniMax is not trying to out-Qwen Qwen in text. It found a gap (video generation) where it leads, while maintaining a credible text line (M2.5 at 1.2M downloads is solid) and pushing into multimodal with the M3 series. For developers who need video generation, MiniMax is the only serious open Chinese option. For text-heavy workloads, Qwen and DeepSeek remain the stronger picks.

The evolution timeline

MiniMax's model releases tell a story of rapid evolution:

• M1 series (2025-06): the first reasoning models, 40K and 80K context variants. These established MiniMax as a serious text-model player.
• M2 series (2025-10 to 2026-02): the M2 line replaced M1, with M2.5 becoming the text mainstay at 1.2M downloads.
• M2.7 (2026-04): an iteration on M2.5, maintaining the text-first focus.
• M3 (2026-06): the multimodal entry — image-plus-text input capability.
• H3 (2026-07): the video generation model that became the family's biggest hit.
• M3.1-Flash (2026-10): the newest text/multimodal model, adding a lightweight Flash tier.
• Music3 (2026-08): audio generation, a rare direction in Chinese open AI.

This cadence — roughly a major release every two months — shows an active development team covering multiple modalities simultaneously.

Family map: video breakout, text following

The text line: M2.5 → M2.7 → M3 → M3.1-Flash

M2.5's download count ranks well among Chinese text models, indicating a substantial local deployment user base. The M3 line's entry into image input marks MiniMax's text line moving from pure text to multimodal — later than Qwen's VL line, but moving in the right direction.

Video generation: the H3 ecosystem

H3's community ecosystem is MiniMax's most valuable asset:

• ComfyUI workflows: extensive community integrations, accelerated builds, and pruned variants
• GGUF quantizations: unsloth and others provide multiple tiers (2.8M downloads)
• Post-processing: latent upscalers, pruned variants, and more

If you need local video generation, H3 plus ComfyUI is currently the most mature open Chinese solution. Deployment follows ComfyUI workflows rather than traditional llama.cpp — a fundamental deployment-shape difference from text models.

Audio generation: Music3

Music3 is a rare audio-direction model in Chinese open AI (text-to-audio). Downloads are modest (5.7K), but it represents MiniMax's ambition for full-modality coverage. Community GGUF builds exist (audio-cpp), enabling local deployment.

Deployment notes by product line

Because MiniMax spans text, video, and audio, deployment varies significantly by which model you target. Understanding these differences before you start saves hours of trial and error, since each line uses a different toolchain and has different hardware requirements.

Text models (M2.5, M2.7) follow the standard GGUF route — download from HuggingFace, load with Ollama or llama.cpp. The quantization ecosystem is less mature than Qwen's (fewer community quantizers), but the mainline models have usable builds.

Multimodal models (M3, M3.1-Flash) need a runtime that supports image input, typically transformers or a multimodal-capable llama.cpp build. Community GGUF for multimodal models is less common; you may need the original safetensors format for vision input.

Video model (H3) requires the most specialized toolchain: ComfyUI with custom nodes, GPU with substantial VRAM (video generation is more compute-intensive than text inference), and patience for generation time. The payoff is a fully local video generation pipeline with no per-video API cost.

Audio (Music3) uses audio-cpp or ComfyUI extensions. The smallest community of the three, but functional for basic text-to-audio workflows.

If you are choosing your first MiniMax model to deploy locally, start with M2.5 text GGUF — the same flow as any other open model — before venturing into the video or audio toolchains. The text models are also the safest for testing hardware configurations, since text inference has the lowest VRAM and compute requirements.

Local deployment notes

Deployment varies significantly by product line:

Text-line deployment is the simplest — identical to other Chinese models, with community GGUF coverage. Video-line complexity is the highest, but ComfyUI's ecosystem lowers the bar.

Selection quick reference

FAQ

Related reading

• Qwen3.8 vs DeepSeek V4 — the text champion pair
• Qwen vs Kimi — multimodal family vs long-context specialist
• ERNIE vs Hunyuan — Baidu vs Tencent routes
• Qwen GGUF Local Deployment Primer — text model deployment basics
• Model Catalog — currently callable models on EXAIV