---
title: phi-4-eval-result
canonical_url: "https://www.modelscope.cn/models/easyllm/phi-4-eval-result"
md_url: "https://www.modelscope.cn/models/easyllm/phi-4-eval-result.md"
repository: easyllm/phi-4-eval-result
chinese_name: "phi-4评测结果"
last_updated: 2025-04-29
pipeline_tag: text-generation
tasks:
- text-generation
model_type:
- phi3
architectures:
- Phi3ForCausalLM
inference_backends:
- "deploy_task text/emb"
- "sglang 0.5.2"
- "vllm 0.9.2"
downloads: 2
stars: 0
---
# phi-4-eval-result
> phi-4-eval-result - easyllm 在 ModelScope 开源的模型。phi-4评测结果: 8大领域，300多项细分能力评测。
easyllm/phi-4-eval-result 是 ModelScope 魔搭社区上的text-generation模型，可用 deploy_task text/emb、sglang 0.5.2、vllm 0.9.2 部署。
- **Repository**: easyllm/phi-4-eval-result
- **Tasks**: text-generation
- **Inference backends**: deploy_task text/emb, sglang 0.5.2, vllm 0.9.2
- **Downloads**: 2
- **Stars**: 0
- **Last updated**: 2025-04-29
Source: https://www.modelscope.cn/models/easyllm/phi-4-eval-result
---
# 模型信息
## Model Summary
| | |
|-------------------------|-------------------------------------------------------------------------------|
| **Developers** | Microsoft Research |
| **Description** | `phi-4` is a state-of-the-art open model built upon a blend of synthetic datasets, data from filtered public domain websites, and acquired academic books and Q&A datasets. The goal of this approach was to ensure that small capable models were trained with data focused on high quality and advanced reasoning.
`phi-4` underwent a rigorous enhancement and alignment process, incorporating both supervised fine-tuning and direct preference optimization to ensure precise instruction adherence and robust safety measures |
| **Architecture** | 14B parameters, dense decoder-only Transformer model |
| **Inputs** | Text, best suited for prompts in the chat format |
| **Context length** | 16K tokens |
| **GPUs** | 1920 H100-80G |
| **Training time** | 21 days |
| **Training data** | 9.8T tokens |
| **Outputs** | Generated text in response to input |
| **Dates** | October 2024 – November 2024 |
| **Status** | Static model trained on an offline dataset with cutoff dates of June 2024 and earlier for publicly available data |
| **Release date** | December 12, 2024 |
| **License** | MIT |
## Intended Use
| | |
|-------------------------------|-------------------------------------------------------------------------|
| **Primary Use Cases** | Our model is designed to accelerate research on language models, for use as a building block for generative AI powered features. It provides uses for general purpose AI systems and applications (primarily in English) which require:
1. Memory/compute constrained environments.
2. Latency bound scenarios.
3. Reasoning and logic. |
| **Out-of-Scope Use Cases** | Our models is not specifically designed or evaluated for all downstream purposes, thus:
1. Developers should consider common limitations of language models as they select use cases, and evaluate and mitigate for accuracy, safety, and fairness before using within a specific downstream use case, particularly for high-risk scenarios.
2. Developers should be aware of and adhere to applicable laws or regulations (including privacy, trade compliance laws, etc.) that are relevant to their use case, including the model’s focus on English.
3. Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the license the model is released under. |
## Data Overview
### Training Datasets
Our training data is an extension of the data used for Phi-3 and includes a wide variety of sources from:
1. Publicly available documents filtered rigorously for quality, selected high-quality educational data, and code.
2. Newly created synthetic, “textbook-like” data for the purpose of teaching math, coding, common sense reasoning, general knowledge of the world (science, daily activities, theory of mind, etc.).
3. Acquired academic books and Q&A datasets.
4. High quality chat format supervised data covering various topics to reflect human preferences on different aspects such as instruct-following, truthfulness, honesty and helpfulness.
Multilingual data constitutes about 8% of our overall data. We are focusing on the quality of data that could potentially improve the reasoning ability for the model, and we filter the publicly available documents to contain the correct level of knowledge.
# 模型下载
[点击下载](https://modelscope.cn/models/LLM-Research/phi-4/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)