--- title: MiniMax-Text-01-eval-result canonical_url: "https://www.modelscope.cn/models/easyllm/MiniMax-Text-01-eval-result" md_url: "https://www.modelscope.cn/models/easyllm/MiniMax-Text-01-eval-result.md" repository: easyllm/MiniMax-Text-01-eval-result chinese_name: "MiniMax-Text-01评测结果" last_updated: 2025-04-29 model_type: - minimax_text_01 architectures: - MiniMaxText01ForCausalLM inference_backends: - "deploy_task text/emb" - "vllm 0.9.2" downloads: 58 stars: 0 --- # MiniMax-Text-01-eval-result > MiniMax-Text-01-eval-result - easyllm 在 ModelScope 开源的模型。MiniMax-Text-01评测结果: 8大领域，300多项细分能力评测。 easyllm/MiniMax-Text-01-eval-result 是 ModelScope 魔搭社区上的机器学习模型，可用 deploy_task text/emb、vllm 0.9.2 部署。 - **Repository**: easyllm/MiniMax-Text-01-eval-result - **Inference backends**: deploy_task text/emb, vllm 0.9.2 - **Downloads**: 58 - **Stars**: 0 - **Last updated**: 2025-04-29 Source: https://www.modelscope.cn/models/easyllm/MiniMax-Text-01-eval-result --- # 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](https://modelscope.cn/models/MiniMax/MiniMax-Text-01/files) # 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](https://github.com/jeinlee1991/chinese-llm-benchmark)