---
base_model:
- Qwen/Qwen3-Next-80B-A3B-Thinking
base_model_relation: finetune
frameworks: PyTorch
language:
- zh
license: apache-2.0
metrics:
- accuracy
tags:
- 中医大模型
- 心语心言
- 医疗
- 医疗大模型
tasks:
  - text-generation
---
# DeepPulse-80B TCM Large Model Series

**DeepPulse (深度把脉)** is the core achievement of 心语心言's open-source Traditional Chinese Medicine (TCM) large model series. This series of models uses Qwen3-Next-80B as the base model and has undergone deep fine-tuning using a self-built high-quality TCM clinical medical dataset. This release includes two versions:

* **DeepPulse-80B-Thinking-V0.1**: Focuses on complex clinical reasoning and assisted diagnosis, achieving first place in total score in public evaluations, demonstrating top-tier logical reasoning capabilities in the TCM domain.
* **DeepPulse-80B-Instruct-V0.1**: Possesses excellent TCM instruction-following capabilities, suitable for a wide range of TCM Q&A and interactive scenarios, with a comprehensive ranking of sixth.

# Public TCM Benchmark Metrics Comparison (MedBench - TCM-5CEval)

TCM-5CEval is an authoritative evaluation benchmark for TCM large models, comprising the following five subtasks that comprehensively assess the model's TCM capabilities:

* **TCM-Exam (中医考试)**: Evaluates the mastery and application of fundamental TCM theories (Yin-Yang, Zang-Fu organs, etc.) and diagnostics knowledge.
* **TCM-LitQA (典籍问答)**: Tests deep understanding and reasoning of classic TCM texts such as "Huangdi Neijing" and "Shanghan Lun".
* **TCM-MRCD (临床诊疗)**: Simulates real clinical scenarios, evaluating the model's ability to analyze medical cases, perform pattern differentiation, and make prescription decisions.
* **TCM-CMM (中药方剂)**: Measures the model's knowledge of Chinese materia medica properties, effects, compatibility contraindications, and formula applications.
* **TCM-ClinNPT (非药物疗法)**: Assesses ability in acupoint selection for acupuncture, Tuina massage techniques, and pattern-based treatment for specific clinical scenarios.

| No. | Model Name | Organization/Team Name | Release Date | Type | Parameters | Total Score | TCM-Exam | TCM-LitQA | TCM-MRCD | TCM-CMM | TCM-ClinNPT |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1 | <font color="red">DeepPulse-80B-Thinking-V0.1</font> | <font color="red">心语心言</font> | <font color="red">2025/12/23</font> | <font color="red">开源</font> | <font color="red">80B</font> | <font color="red">71.3</font> | <font color="red">83.0</font> | <font color="red">45.5</font> | <font color="red">75.4</font> | <font color="red">84.9</font> | <font color="red">67.6</font> |
| 2 | HKR_TCM_HW_v1 | 港仔机器人主动健管团队 | 2025/12/12 | 闭源 | 671B | 70.8 | 85.4 | 44.2 | 73.1 | 83.8 | 67.5 |
| 3 | Gemini-2.5-Pro-nothinking | Google | 2025/03/25 | 闭源 | N/A | 69.2 | 77.9 | 62.0 | 72.4 | 72.6 | 61.2 |
| 4 | DeepSeek-V3.2 | DeepSeek | 2025/12/01 | 开源 | 671B | 66.8 | 74.5 | 44.4 | 66.8 | 80.0 | 68.3 |
| 5 | Grok-4 | xAI | 2025/07/09 | 闭源 | N/A | 66.6 | 73.0 | 59.3 | 68.4 | 68.0 | 64.2 |
| 6 | <font color="red">DeepPulse-80B-Instruct-V0.1</font> | <font color="red">心语心言</font> | <font color="red">2025/12/23</font> | <font color="red">开源</font> | <font color="red">80B</font> | <font color="red">66.2</font> | <font color="red">74.4</font> | <font color="red">40.7</font> | <font color="red">70.6</font> | <font color="red">79.7</font> | <font color="red">65.6</font> |
| 7 | Qwen3-235B-A22B-Thinking-2507 | Alibaba | 2025/08/17 | 开源 | 235B | 64.8 | 75.5 | 40.3 | 68.5 | 78.2 | 61.5 |
| 8 | Claude-Sonnet-4.5 | Anthropic | 2025/09/29 | 闭源 | N/A | 64.8 | 69.8 | 59.3 | 67.2 | 71.7 | 56.0 |
| 9 | GPT-5 | OpenAI | 2025/08/07 | 闭源 | N/A | 63.6 | 75.0 | 51.9 | 64.1 | 66.6 | 60.6 |
| 10 | Qwen3-Next-80B-A3B-Thinking | Alibaba | 2025/09/15 | 开源 | 80B | 63.5 | 76.0 | 38.2 | 66.2 | 77.9 | 59.4 |
| 11 | Llama-4-maverick | Meta | 2025/04/06 | 开源 | 400B | 57.2 | 72.1 | 51.3 | 63.8 | 54.4 | 44.3 |
| 12 | GPT-4o | OpenAI | 2025/05/13 | 闭源 | 200B | 55.9 | 66.5 | 46.9 | 60.9 | 57.1 | 47.9 |

> Note: "N/A" in the Parameters column indicates that the model's parameter count has not been publicly disclosed.
>
> Except for `DeepSeek-V3.2`, `Qwen3-235B-A22B-Thinking-2507`, `Qwen3-Next-80B-A3B-Thinking` which are self-tested deployment data, other models reference publicly available leaderboard data.
>
> TCM-5CEval: https://medbench.opencompass.org.cn/track-detail/tcmeval