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Chinese AGI company building multimodal LLMs, Hailuo video, speech and music models, plus AI apps and open APIs.
AI-focused cloud offering NVIDIA GPU compute, storage and MLOps tooling for training and inference at scale, with usage-based pricing.
Unified pay-per-generation API for 500+ image, video and audio models like FLUX, Kling and Seedance at low cost.
Unified API to 500+ AI models (OpenAI, Anthropic, Google, etc.) with OpenAI-compatible calls priced ~20% below official rates.
Cloud platform from the makers of PyTorch Lightning for building, training and deploying AI in browser-based GPU Studios.
No public pricing
Free trial available
No public pricing
- ✦MiniMax M-series LLMs (M3, 1M context, MSA)
- ✦Hailuo AI video generation
- ✦Speech and music generation models
- ✦MiniMax Code agentic coding tool
- ✦Consumer apps (Hailuo, Xingye)
- ✦Open API and Token Plan for developers
- ✦NVIDIA GPU instances (H100, H200, B200, GB200)
- ✦On-demand and preemptible GPU pricing
- ✦High-performance and object storage
- ✦Managed Kubernetes and Slurm (Soperator)
- ✦Serverless and managed inference (Token Factory)
- ✦MLOps tooling and 24/7 expert support
- ✦Commitment discounts up to 35%
- ✦Single API for 500+ image, video and audio models
- ✦Pay-per-generation billing with no subscription
- ✦No charge on failed tasks
- ✦Workflows, agents and studio tools
- ✦MCP and CLI integrations, white-label option
- ✦One key for 500+ models
- ✦OpenAI-compatible API
- ✦Pay-as-you-go credits (~20% below list)
- ✦Multimodal: text, image, video, audio
- ✦Usage analytics and budget alerts
- ✦Integrations (Claude Code, n8n, Zapier, etc.)
- ✦Browser-based Lightning Studios with on-demand GPUs
- ✦PyTorch Lightning training framework
- ✦Model training, fine-tuning and deployment
- ✦Collaborative, shareable ML environments
- ✦Scalable multi-GPU/multi-node compute
- →Coding and agentic tasks
- →AI video generation
- →Text-to-speech and music creation
- →Building on MiniMax model APIs
- →Train large AI/ML models on GPU clusters
- →Run scalable inference workloads
- →Store and manage large training datasets
- →Run Slurm/Kubernetes AI pipelines
- →Building apps on top of many generative models via one API
- →Generating images, video and audio at scale
- →Cutting model API costs versus direct providers
- →Deploying white-label AI generation studios
- →Consolidating multi-provider AI billing
- →Switching models without re-integration
- →Powering apps and automation pipelines
- →Benchmarking models in one playground
- →Prototype and train ML models in the cloud
- →Fine-tune and deploy foundation models
- →Run reproducible AI experiments collaboratively