Model reference · open weights
MiniMax-Text-01 is an open-weight language model from MiniMax. MiniMax-Text-01-hf (BF16) weighs 915 GB; the smallest configuration that runs it is 8× H200 141 GB.
Summary of the MiniMaxAI/MiniMax-Text-01-hf model card, 2026-10-01
What it is
| Released by | MiniMax |
|---|---|
| Released | 2025-06-03 |
| Parameters | 456.1B |
| VRAM | 915 GB for the weights |
What it runs on
How much memory each request adds isn't estimated yet for this architecture. The weights need at least the cards below, plus room for the context.
| Card | Weights alone |
|---|---|
| RTX 3060 12 GB … 8× A100 80 GB 14 smaller cards | does not fit |
| 8× H200 141 GB tensor parallel | tight |
| 8× B200 180 GB tensor parallel | fits |
From the model card
This repository is primarily for the Transformers framework. If you're using other open-source frameworks, please use the alternative repository: MiniMax-Text-01
WeChat
MiniMax-Text-01 is a powerful language model with 456 billion total parameters, of which 45.9 billion are activated per token. To better unlock the long context capabilities of the model, MiniMax-Text-01 adopts a hybrid architecture that combines Lightning Attention, Softmax Attention and Mixture-of-Experts (MoE). Leveraging advanced parallel strategies and innovative compute-communication overlap methods—such as Linear Attention Sequence Parallelism Plus (LASP+), varlen ring attention, Expert Tensor Parallel (ETP), etc., MiniMax-Text-01's training context length is extended to 1 million tokens, and it can handle a context of up to 4 million tokens during the inference. On various academic benchmarks, MiniMax-Text-01 also demonstrates the performance of a top-tier model.
The architecture of MiniMax-Text-01 is briefly described as follows:
| Tasks | GPT-4o (11-20) | Claude-3.5-Sonnet (10-22) | Gemini-1.5-Pro (002) | Gemini-2.0-Flash (exp) | Qwen2.5-72B-Inst. | DeepSeek-V3 | Llama-3.1-405B-Inst. | MiniMax-Text-01 |
|---|---|---|---|---|---|---|---|---|
| General | ||||||||
| MMLU* | 85.7 | 88.3 | 86.8 | 86.5 | 86.1 | 88.5 | 88.6 | 88.5 |
| MMLU-Pro* | 74.4 | 78.0 | 75.8 | 76.4 | 71.1 | 75.9 | 73.3 | 75.7 |
| SimpleQA | 39.0 | 28.1 | 23.4 | 26.6 | 10.3 | 24.9 | 23.2 | 23.7 |
| C-SimpleQA | 64.6 | 56.8 | 59.4 | 63.3 | 52.2 | 64.8 | 54.7 | 67.4 |
| IFEval (avg) | 84.1 | 90.1 | 89.4 | 88.4 | 87.2 | 87.3 | 86.4 | 89.1 |
| Arena-Hard | 92.4 | 87.6 | 85.3 | 72.7 | 81.2 | 91.4 | 63.5 | 89.1 |
| Reasoning | ||||||||
| GPQA* (diamond) | 46.0 | 65.0 | 59.1 | 62.1 | 49.0 | 59.1 | 50.7 | 54.4 |
| DROP* (F1) | 89.2 | 88.8 | 89.2 | 89.3 | 85.0 | 91.0 | 92.5 | 87.8 |
| Mathematics | ||||||||
| GSM8k* | 95.6 | 96.9 | 95.2 | 95.4 | 95.8 | 96.7 | 96.7 | 94.8 |
| MATH* | 76.6 | 74.1 | 84.6 | 83.9 | 81.8 | 84.6 | 73.8 | 77.4 |
| Coding | ||||||||
| MBPP + | 76.2 | 75.1 | 75.4 | 75.9 | 77.0 | 78.8 | 73.0 | 71.7 |
| HumanEval | 90.2 | 93.7 | 86.6 | 89.6 | 86.6 | 92.1 | 89.0 | 86.9 |
| Model | 4k | 8k | 16k |
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.