Hugging Peers

MiniMax-Text-01-eval-result

☆ Save
by easyllm
58 downloads 0 likes on ModelScope updated

Model card

模型信息

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:

  • Total Parameters: 456B
  • Activated Parameters per Token: 45.9B
  • Number Layers: 80
  • Hybrid Attention: a softmax attention is positioned after every 7 lightning attention.
    • Number of attention heads: 64
    • Attention head dimension: 128
  • Mixture of Experts:
    • Number of experts: 32
    • Expert hidden dimension: 9216
    • Top-2 routing strategy
  • Positional Encoding: Rotary Position Embedding (RoPE) applied to half of the attention head dimension with a base frequency of 10,000,000
  • Hidden Size: 6144
  • Vocab Size: 200,064

模型下载

点击下载

评测结果

开源模型对比

排名 大模型 机构 size 总分 医疗 教育 金融 法律 行政公务 心理健康 推理与数学计算 语言与指令遵从
1 DeepSeek-R1 深度求索 671B 80.8 81.9 89.2 82.9 73.8 83.0 61.5 89.2 84.8
2 hunyuan-large 腾讯 389B 80.2 88.6 85.5 84.6 82.4 70.4 73.2 76.8 80.1
3 deepseek-chat-v3-0324(new) 深度求索 671B 77.7 78.0 87.0 76.1 61.7 81.2 64.6 87.6 85.4
4 qwq-32b 阿里巴巴 32B 77.5 76.5 87.2 78.3 60.9 82.2 63.0 87.2 85.2
5 GLM-Z1-32B-0414(new) 智谱AI 32B 75.6 72.6 81.9 74.8 62.2 80.0 63.3 88.5 81.3
6 DeepSeek-R1-Distill-Qwen-32B 深度求索 32B 71.8 69.4 85.1 73.8 51.8 76.0 53.8 83.6 81.4
7 GLM-4-32B-0414(new) 智谱AI 32B 71.8 71.7 76.5 72.3 54.5 76.0 60.9 81.2 81.3
8 Llama-4-Maverick-17B-128E-Instruct-FP8(new) Meta 400B 71.3 75.8 83.0 71.1 48.1 69.0 59.0 82.7 81.6
9 qwen2.5-72b-instruct 阿里巴巴 72B 71.2 74.1 80.0 75.6 51.6 67.0 59.5 79.7 81.7
10 MiniMax-Text-01 minimax 456B 69.7 70.6 79.8 70.0 50.7 71.0 57.8 76.0 81.3
11 qwen2.5-32b-instruct 阿里巴巴 32B 69.1 71.6 82.0 71.8 50.8 64.5 57.8 73.5 80.7
12 DeepSeek-R1-Distill-Qwen-14B 深度求索 14B 66.5 62.8 79.3 67.5 40.2 66.5 55.6 81.0 78.7
13 qwen2.5-14b-instruct 阿里巴巴 14B 65.9 67.7 78.6 68.0 45.4 61.0 56.1 70.4 79.9
14 GLM-Z1-9B-0414(new) 智谱AI 9B 63.2 58.0 66.9 63.8 31.5 75.5 48.8 84.8 76.2
15 qwen2.5-7b-instruct 阿里巴巴 7B 62.5 64.5 74.8 66.5 41.8 53.0 56.0 67.6 76.1
16 Llama-4-Scout-17B-16E-Instruct(new) Meta 109B 62.4 65.4 78.0 62.1 31.8 55.5 54.0 76.0 76.2
17 DeepSeek-R1-Distill-Llama-70B 深度求索 70B 62.2 54.1 72.9 60.6 34.7 70.0 46.2 81.8 77.0
18 internlm2_5-20b-chat 上海人工智能实验室 20B 61.6 65.4 73.9 62.9 44.7 53.5 52.7 62.4 77.2
19 Meta-Llama-3.1-405B-Instruct Meta 405B 61.0 66.1 64.1 59.3 34.7 59.0 53.9 73.2 77.9
20 internlm2_5-7b-chat 上海人工智能实验室 7B 60.6 64.8 66.1 64.1 43.1 55.5 51.0 64.3 76.3
21 GLM-4-9B-0414(new) 智谱AI 9B 60.3 59.3 72.0 62.1 40.8 53.5 49.2 68.3 76.8
22 Llama-3.3-70B-Instruct Meta 70B 59.0 59.2 63.8 56.9 29.9 60.5 49.6 74.2 78.0
23 Mistral-Small-3.1-24B-Instruct-2503(new) Mistral 24B 58.7 53.1 68.0 57.6 33.3 57.0 47.1 76.3 77.1
24 Llama-3.3-70B-Instruct-fp8 Meta 70B 58.4 58.7 63.0 56.0 29.2 59.0 48.5 74.7 78.1
25 GLM-Z1-Rumination-32B-0414(new) 智谱AI 32B 57.5 47.9 69.6 54.3 38.9 56.7 44.1 78.3 70.2
26 Hermes-3-Llama-3.1-405B NousResearch 405B 57.4 55.6 62.3 59.2 29.8 52.5 48.9 72.7 78.0
27 glm-4-9b-chat 智谱AI 9B 56.8 56.0 73.4 57.3 36.5 52.0 47.1 60.1 71.7
28 gemma-3-27b-it Google 27B 56.4 48.2 64.3 56.1 21.3 66.5 44.5 77.4 73.1
29 phi-4 微软 14B 55.3 43.4 59.7 56.0 24.9 64.5 43.2 77.3 73.7
30 Mistral-Small-24B-Instruct-2501 Mistral 24B 53.2 45.8 60.5 53.1 26.3 53.5 40.2 71.4 74.4
31 gemma-3-12b-it Google 12B 52.2 43.6 58.2 46.6 20.4 59.0 41.1 77.3 71.6
32 qwen2.5-3b-instruct 阿里巴巴 3B 50.6 49.8 62.4 53.8 26.9 42.0 43.8 59.1 67.6
33 DeepSeek-R1-Distill-Qwen-7B 深度求索 7B 45.4 28.8 52.8 45.8 19.2 54.0 30.4 70.9 61.2
34 DeepSeek-R1-Distill-Llama-8B 深度求索 8B 45.2 29.7 51.4 42.6 20.3 52.0 31.9 68.5 64.8
35 Llama-3.1-8B-Instruct Meta 8B 44.0 39.9 44.3 40.9 21.3 43.0 37.2 59.9 65.8
36 Meta-Llama-3.1-8B-Instruct-fp8 Meta 8B 42.9 39.2 43.0 40.8 19.5 37.5 34.0 63.1 65.7
37 qwen2.5-1.5b-instruct 阿里巴巴 2B 42.3 43.8 56.0 48.6 25.6 35.0 39.6 38.5 51.5
38 gemma-3-4b-it Google 4B 41.1 31.8 46.6 39.7 16.6 39.5 29.2 67.6 58.0
39 Llama-3.2-3B-Instruct Meta 3B 38.5 34.6 37.5 35.0 17.1 34.0 29.6 57.6 62.7
40 DeepSeek-R1-Distill-Qwen-1.5B 深度求索 2B 35.5 23.8 42.8 34.5 15.4 34.5 23.9 61.8 47.1
41 Llama-3.2-1B-Instruct Meta 1B 30.1 25.4 28.3 28.8 14.6 29.0 21.1 41.6 51.9
42 qwen2.5-0.5b-instruct 阿里巴巴 1B 30.0 29.2 36.8 33.9 18.4 22.5 24.5 35.2 39.2
43 gemma-3-1b-it Google 1B 28.6 23.6 31.9 28.7 15.4 29.0 20.6 31.5 48.2

精益评测：更细分的维度

  • 医疗
    • 医师
    • 护理
    • 药师
    • 医技
    • 医学基础知识
    • 医学考研
  • 教育
    • 小学学科
    • 初中学科
    • 中考
    • 高中学科
    • 高考
    • 高等教育
    • 考研
    • 教师资格
  • 金融排行榜
    • 财务
    • 银行
    • 保险
    • 证券
    • 其他金融资格考试
    • 金融基础知识
    • 金融应用
  • 法律排行榜
    • 律师资格考试
  • 行政公务排行榜
    • 公务员考试
  • 心理健康排行榜
  • 推理与数学计算排行榜
    • 演绎推理
    • 常识推理
    • 符号推理BBH
    • 算术能力
    • 表格问答
    • 高中奥数
    • 初中奥数
    • 小学奥数
  • 语言与指令遵从排行榜
    • 成语理解
    • 情感分析
    • 文本蕴含
    • 分类能力
    • 信息抽取
    • 阅读理解
    • C3中文阅读理解
    • 代词理解CLUEWSC
    • 诗词匹配CCPM
    • 中文指令遵从

以上细分能力评测结果，详见github