MiniMax M3
Overview
MiniMax M3 is MiniMax's next-generation large language model in the M series. It introduces MiniMax Sparse Attention (MSA), a sub-quadratic attention mechanism designed to improve inference efficiency for million-token context workloads while maintaining output quality comparable to the M2 series. Availability may vary by B.AI model catalog and rollout status.
Key Features
- MiniMax Sparse Attention (MSA): Uses a two-stage GQA-based sparse attention architecture. A lightweight index branch selects relevant KV blocks, while the sparse branch computes attention only on those selected blocks to reduce attention cost.
- Million-Token Context: Designed for 1M-token context scenarios, with preview materials reporting substantially faster decoding and prefill compared with the M2 series at long context lengths.
- Multimodal Support: Image-input support depends on platform rollout and model configuration, extending the M series beyond text-only workflows where available.
- Agent & Office Focus: Optimized for document understanding, spreadsheet processing, presentation generation, and enterprise agent workflows.
Best Use Cases
- Ultra-Long Context Processing: Multi-document analysis, long conversation history, and large codebase understanding.
- AI Agent Deployment: Autonomous task execution that combines tool use, reasoning, and long-context state.
- Office Automation: Intelligent processing and generation of documents, spreadsheets, and presentations.
Capabilities and Limitations
| Capability | Description |
|---|---|
| Reasoning | Designed for frontier-tier reasoning and agent workflows; final benchmark details may vary |
| Creative Writing | Not specified |
| Multimodal | Text workflows; image-input support depends on platform rollout and model configuration |
| Response Speed | Designed for faster long-context prefill and decoding than the M2 series |
| Context Window | 1,000,000 tokens |
| Max Output | Not specified |
| Tool Use | Designed for function calling and agent workflow support |
| Multilingual | Not specified |
Known Limitations
- Final availability, model limits, and benchmark scores may change during rollout.
- Some capabilities described in preview materials may depend on platform support and model configuration.
Standard Pricing
The token prices below are shown in USD per 1 million tokens; web search is billed in USD per use.
| Model | Input (USD / 1M Tokens) | Cache Write (USD / 1M Tokens) | Cache Read (USD / 1M Tokens) | Output (USD / 1M Tokens) | Web Search (USD / use) |
|---|---|---|---|---|---|
| MiniMax M3 | $0.30 | $0.30 | $0.06 | $1.20 | - |
Credits settlement: B.AI converts charges at 1 USD = 1,000,000 Credits and deducts Credits from the account balance.
Prices shown in the documentation are B.AI standard reference prices for base billing purposes. B.AI may provide lower actual usage costs through account benefits. Specific prices and account benefits are subject to the platform display and final billing records.