Baichuan M2 is a new generation of large language model launched by Baichuan Intelligence, which continues to iterate in Chinese understanding, knowledge question and answer and reasoning capabilities. Founded by former Sogou CEO Wang Xiaochuan, it evolved from the open source Baichuan series to the commercial version M2.

百川M2

Core parameters and statistics of Baichuan M2

Baichuan-M2 is a medically-enhanced Reasoning Model launched by Baichuan Intelligence, which is specially designed for real-world medical reasoning tasks. It is based on the Qwen2.5-32B base and uses an innovative Large Verifier System for post-training alignment in the medical field, achieving breakthrough improvements in medical effects while maintaining universal capabilities. This is not a general chatbot, but a vertical reasoning model for medical scenarios.

Parameters	Official verifiable information
Product Positioning	Medical Enhanced Inference Model
Model architecture	Dense architecture based on Qwen2.5-32B
Parameter amount	32 billion (32B)
Context length	32K tokens
Open source license	Apache 2.0 (available for commercial use)
Core Evaluation	HealthBench 60.1 (No. 1 in the world’s open source medical model)
Deployment threshold	Single RTX 4090 (4bit quantization)
MTP acceleration	single-user scenario token throughput increased by 58.5%
Domestic computing power	Adapted to Huawei Ascend 910B (8bit quantification)
Official entrance	baichuan-ai.com
Open source warehouse	github.com/baichuan-inc/Baichuan-M2-32B
Paper	arXiv:2509.02208

HealthBench comparison: Baichuan-M2 surpassed all open source models and many cutting-edge closed source models (including o3, Grok 3, Gemini 2.5 Pro, GPT-4.1) with a score of 60.1 on the HealthBench medical evaluation set released by OpenAI. It is currently the open source large model closest to the medical capabilities of GPT-5. On the HealthBench Hard subset, Baichuan-M2 and GPT-5 are currently the only two models in the world that score more than 32 points.

Model	HealthBench	HealthBench Hard	HealthBench Consensus
Baichuan-M2 (32B)	60.1	34.7	91.5
gpt-oss-120b	57.6	30.0	90.0
Qwen3-235B-A22B	55.2	25.9	90.6
DeepSeek-R1-0528	53.6	22.6	91.5
GLM-4.5	47.8	18.7	85.3
Kimi-K2	43.0	10.7	90.9

The actual meaning of hardware efficiency: 32B parameter volume combined with 4bit quantization can complete inference on a single RTX 4090 (24GB video memory), which means that individual developers or small clinics can also afford private deployment - no multi-card cluster is required. Quantized accuracy is nearly lossless on medical and general benchmarks. For high-frequency scenarios that require large-scale concurrency, the MTP (Multi-Token Prediction) version increases token throughput by 58.5% in single-user scenarios, directly reducing the computing cost of unit queries.

User and market recognition

Baichuan-M2 is not a mass consumer chat product, but a technical infrastructure for the medical industry. Its recognition is reflected in both the open source community and industry benchmarks.

Open source community feedback: GitHub received 211 stars and 15 Forks. Hugging Face provides full weight and 4bit GPTQ quantified versions, supporting mainstream inference engines such as vLLM and SGLang. Baichuan also opened a technical blog and paper (arXiv:2509.02208), detailing the implementation details of a large-scale verifier system, patient simulator and multi-stage reinforcement learning - a relatively high level of transparency among medical AI open source projects.

Industry Benchmark Validation: HealthBench was released by OpenAI in 2025 and contains 5,000 real multi-round medical conversations with 48,562 judging criteria written by 262 human doctors. Baichuan-M2 comprehensively surpasses top open source models such as gpt-oss-120b and DeepSeek-R1-0528 on this benchmark, and even leads in core medical scenarios such as emergency triage (74.6, ranked first), medical context understanding, and doctor-patient communication (68.6, ranked first). In the evaluation of clinical diagnosis and treatment scenarios in China, Baichuan-M2's adaptability to the Chinese medical system and compliance with Chinese clinical guidelines were better than general models of the same scale.

Baichuan Intelligent Company Background: Baichuan Intelligent was founded in March 2023 by former Sogou CEO Wang Xiaochuan and has received multiple rounds of financing from Alibaba, Tencent and others. The company's core team comes from leading technology companies such as Sogou, Baidu, Huawei, Microsoft, and ByteDance. From Baichuan-7B/13B (2023) to Baichuan2-7B/13B, and then to the M series medical models (M1→M2→M3→M4), Baichuan has embarked on a differentiated route of "general open source to establish influence → deepen capabilities in the medical vertical field".

Cost advantage of Baichuan M2

The cost advantage of Baichuan-M2 comes from two dimensions: the open source license eliminates software licensing fees, and the lightweight architecture lowers the threshold for hardware deployment.

C client/individual: Free to use through Baixiaoyi: The family-oriented AI health assistant "Baixiaoyi" provides core functions such as symptom inquiry, medical preparation, report interpretation, and medication reminders. Basic functions are free and no registration is required. The Free Truth: The free version provides basic consultation suggestions, but does not guarantee medical accuracy; in-depth health management requires paid expansion.

Developer/Open Source Model: Zero License Fee, Infrastructure Only: Baichuan-M2 is open source under the Apache 2.0 license, allowing commercial use, modification, and redistribution. This means developers only have to pay the cost of GPU computing power. Taking a single card RTX 4090 as a benchmark, the cloud GPU rental price is about 5-10 yuan/hour, a typical medical consultation inference takes about 2-5 seconds, and the cost of a single inference is less than 0.01 yuan. Compared with calling closed-source APIs such as GPT-5 (about $30/million token input level), the marginal cost of self-deploying M2 in medical scenarios is as low as 1-2 orders of magnitude. But please note: Hidden costs in the open source model include operation and maintenance manpower, model update and maintenance, and inference engine tuning—these may be barriers to entry for medical institutions with non-technical backgrounds.

Enterprise/Private: Deploy once, use continuously: For medical institutions with highly sensitive data, privatized deployment is a must. Baichuan-M2 can run on a single card A100-80G (full precision) or a single card RTX 4090 (4bit quantization), and an 8-card A100 cluster can support production-level inference services with medium concurrency. The adapted version of Huawei Ascend 910B (8bit quantification) further reduces the political and supply chain risks of localized deployment. Implicit costs that need to be verified: Model version updates may require re-fine-tuning or alignment; the inference delay and memory consumption of long context scenarios (32K full) need to be stress tested in advance; manual review must still be retained in key diagnostic scenarios, and this part of the labor cost cannot be replaced by the model.

Baichuan M3 Plus API Free Plan: Baichuan Intelligence has launched the "Inclusive of All Rivers" plan, providing free Baichuan-M3 Plus API to medical worker service organizations. It uses "evidence anchoring" technology and accurately matches 95% of the evidence paragraphs, aiming to lower the clinical implementation threshold of medical AI. As an open source version, M2 can also be deployed by users themselves.

Main functions of Baichuan M2

The functions of Baichuan-M2 are designed around the core scenario of "medical reasoning". It is not a simple transplantation of general chat capabilities, but an in-depth adaptation for clinical diagnosis logic, doctor-patient communication and medical knowledge retrieval.

• Clinical Diagnostic Reasoning (Thinking Mode): M2’s most differentiated ability. The built-in thinking chain reasoning mechanism enables a complete clinical reasoning process before answering - from symptom analysis, sign interpretation, auxiliary examination and evaluation to differential diagnosis. In real case testing, M2 can follow the SOAP (Subjective-Objective-Assessment-Plan) structured clinical workflow and automatically generate a complete analysis including "diagnosis basis" and "differential diagnosis list". Use value: Assist junior doctors and medical students in clinical thinking training to reduce diagnostic errors.

• Patient Simulator Aligned: Based on patient simulator training, M2 has robust doctor-patient interaction capabilities. It can proactively ask for key medical history information (course of disease, aggravating and alleviating factors, past medical history, medication history, allergy history, etc.) instead of passively waiting for complete medical records to be entered. In the simulated consultation, M2 showed empathy for human doctors - first expressing understanding and then providing professional advice to anxious patients, which is a higher-level ability in medical AI. Use value: Standardized patient simulation, pre-consultation system and online health consultation in medical education.

• Medical knowledge retrieval and Q&A: covering authoritative knowledge from basic medicine to clinical specialties. For specific conditions such as liver cancer treatment and diabetes management, M2 can give specific recommendations based on Chinese clinical guidelines (such as the 2024 edition of the National Health Commission's "Guidelines for the Diagnosis and Treatment of Primary Liver Cancer"). Use value: Clinical decision-making aid reference, medication safety tips.

• Emergency Triage and Risk Warning: In the HealthBench evaluation, M2's "Emergency Triage" dimension scored 74.6, ranking first. It can identify red flags from patient descriptions (e.g., metastatic right lower quadrant pain suggesting appendicitis, chest pain radiating to the left arm suggesting myocardial infarction) and provide clear recommendations on priority ("Go to the emergency department immediately, don't wait for an outpatient appointment"). Use value: Online pre-examination and triage system, emergency center assisted decision-making.

• Medical document generation: Supports automatic generation of standard documents such as outpatient medical records, admission records, and disease course records. M2 can convert unstructured doctor-patient conversations into structured electronic medical records, including standard modules such as chief complaint, history of current illness, preliminary diagnosis and treatment opinions. Use value: Reduce the paperwork burden of doctors and improve the consistency of medical records.

• General Capability Retention: Through the 2:2:1 data ratio (medical data: general data: mathematical reasoning data) in the Mid-Training stage and the domain self-constraint training mechanism, M2 retains better general understanding and reasoning capabilities while strengthening medical capabilities. It reached 83.4 (AIME24) and 72.9 (AIME25) on the AIME mathematical benchmark, and reached 45.8 on the Arena-Hard dialogue evaluation, indicating that the model is not a "partial" medical-specific model, but a general reasoning model that is deepened in the medical field.

Model and version evolution of Baichuan M2

Baichuan Intelligent's model evolution has taken a clear route: starting from a general open source model and gradually deepening into the medical vertical field. M2 is at a critical point in the transition from "universal" to "medical" on this route.

Baichuan Series: General Open Source Foundation (2023-06 to 2024-09)

• Baichuan-7B / Baichuan-13B (2023-06): The first batch of open source large models, 7B and 13B dual versions, 1.2T/1.4T multi-language corpus. Apache 2.0 is open source, and GitHub has received a large number of stars.
• Baichuan2-7B/Baichuan2-13B (~2024-01): Second generation, training data expanded to 2.6T tokens, context 4K. Publish a complete technical report and mid-training checkpoints.

M Series: Medical Enhancement and Deepening (2025-02 to present)

• Baichuan-M1-14B (~2025-02): The first medical enhancement model, 14B parameters, 20T medical enhancement corpus, 32K context. Open sourced with Apache 2.0 (arXiv:2502.12671), the feasibility of the "base + post-medical training" route is verified.
• Baichuan-M2-32B (~2025-09): Based on Qwen2.5-32B, introducing a large-scale verifier system and multi-stage RL. 32B parameters, 32K context. HealthBench 60.1, surpassing all open source models.
• Baichuan-M3-235B (~2026-02): 235B parameters, HealthBench 65.1, reaching "full blood" consultation capability for the first time. APIs are provided free of charge to medical service institutions through the "Hainan Baichuan" plan.
• Baichuan-M4 (~2026-07): HealthBench ranks first in the world, evolving from "asking right once" to "long-term trust".

Version map summary: Baichuan-7B (2023-06) → Baichuan2-7B (~2024-01) → M1-14B (~2025-02) → M2-32B (~2025-09) → M3-235B (~2026-02) → M4 (~2026-07). It can be seen that starting from M1, the focus of Baichuan's model has shifted from the general field to the medical vertical field. The number of parameters has gradually expanded from 14B to 235B, and the evaluation score has achieved continuous jumps from M1 to M4.

Technical advantages of Baichuan M2

The technological breakthrough of Baichuan-M2 does not lie in the extreme expansion of the model scale, but in its creation of a "virtual clinical context + large-scale validator" training paradigm, which allows the model to obtain real diagnostic reasoning capabilities in simulated clinical practice.

Large Verifier System: M2’s core technological innovation. Traditional RLVR (reinforcement learning based on verifiable rewards) is effective in closed fields such as mathematics and coding, but static question bank verification cannot reproduce the dynamic complexity of real diagnosis and treatment. M2’s solution is to build a large-scale, high-fidelity, dynamic reinforcement learning context:

• Patient Simulator: Build a virtual patient library based on desensitized medical records, clinical guidelines, case reports and real doctor-patient conversation records. Contextual training is no longer a single round of question and answer, but a continuous interaction full of uncertainty, allowing the model to grow in "simulated clinical practice".
• Clinical Score Generator: Continuous and dynamic quantitative evaluation of model interaction performance from dimensions such as diagnostic accuracy, consultation logic coherence, treatment plan rationality, communication and empathy capabilities, and medical ethics - replacing the traditional "right/wrong" binary judgment.
• Fully asynchronous Rollout + Reward pipeline: A fully asynchronous pipeline developed on the verl framework, which eliminates training idle caused by waiting for scores, making large-scale medical RL feasible in engineering.

Multi-Stage RL: Decompose complex RL tasks into controllable hierarchical stages. Each stage is designed around specific capability goals and data sources to gradually guide the development of model skills. An improved version of GRPO (Group Relative Policy Optimization) is adopted, and optimizations such as elimination of KL divergence Clip-higher, Loss normalization, and dynamic length reward are introduced to make the model progressively improve in medical common sense, reasoning, and patient interaction capabilities.

Mid-Training combines general and specialized skills: Mix high-quality medical data, general data and mathematical reasoning data at a ratio of 2:2:1, and introduce a field self-restraint training mechanism to prevent the degradation of general capabilities while injecting medical knowledge. Medical data adopts a dual-task paradigm - regular text renewal training and ICL note reasoning training, so that the model learns to "think like a doctor".

Lightweight deployment project: M2 adopts the PTQ (Post-Training Quantization) strategy for 4-bit weight quantization and 8-bit KV Cache quantization. Quantitative models can be deployed directly on a single RTX 4090 with virtually no loss in accuracy. The MTP version based on Eagle-3 training increases single-user token throughput by 58.5%. The 8-bit quantitative adaptation version of Huawei's Ascend 910B NPU has also been released, providing a complete path for localized deployment. Project Implementation Tips: 4bit quantification may cause latency fluctuations in large-volume concurrency scenarios. It is recommended to use vLLM or SGLang for stress testing before production deployment to confirm that P99 latency meets the business SLA.

How to use

The usage path of Baichuan-M2 is divided into three levels: open source self-deployment C-side product experience and commercial API.

How to use	Suitable for the crowd	Entrance	Fees
Open source model download	Developer/research institution	Hugging Face: baichuan-inc/Baichuan-M2-32B	Free (Apache 2.0)
Quantitative model deployment	Individual developers/small institutions	Hugging Face: baichuan-inc/Baichuan-M2-32B-GPTQ-Int4	Free, only requires GPU hardware
Baixiaoyi Web/App	Ordinary users	baichuan-ai.com, or search "Baichuanyi" in the app store	Basic functions are free
Baichuan-M3 Plus API	Medical service organization	Apply through the "Hainan Baichuan" program	Free call after passing the review
Huawei Ascend deployment	Localization demand unit	modelers.cn/models/Baichuan/Baichuan-M2-32B-W8A8	Free, Ascend hardware required

Quick Start (Open Source Model): Developers can directly load models through Hugging Face Transformers:

from transformers import AutoTokenizer, AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained(
    "baichuan-inc/Baichuan-M2-32B",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("baichuan-inc/Baichuan-M2-32B")

prompt = "Got a big swelling after a bug bite. Need help reducing it."
messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    thinking_mode='on' # on/off/auto
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)

Key parameter description: thinking_mode supports three modes - on (forced opening of the thinking chain, suitable for complex diagnosis scenarios), off (straight out mode, suitable for simple question and answer), auto (model independent judgment); max_new_tokens controls the length of a single output, 4K-8K is recommended for complex cases, and can be reduced to 1K-2K for simple queries. In production environments, it is recommended to use vLLM or SGLang deployment for better concurrency performance and memory management. For details on the deployment process of the quantitative model, see the README in the Hugging Face model card.

Product Pricing

The pricing of Baichuan-M2 consists of two layers: open source license and productized services, and the overall model follows the model of "free model + pay-as-you-go service".

Open Source Model Layer: Apache 2.0 licensed, completely free. Includes weight download, commercial license, modification and redistribution rights. Zero licensing fees are M2’s core pricing advantage over closed-source medical AI services. However, you need to bear the cost of GPU computing power yourself - the rental fee for a single card RTX 4090 is 5-10 yuan, and the cost of a single inference can be controlled within 0.01 yuan.

C-side service layer (Baixiaoyi): Basic consultation functions are free, covering common symptom inquiry, medical preparation, report interpretation and other scenarios. The specific paid functions are subject to the in-app page. C-side users can use Baixiaoyi as an auxiliary tool for daily health management.

B-end API layer (M3 Plus): Through the "Hainan Baichuan" plan, the M3 Plus API for medical worker service organizations can be called permanently free of charge after passing the review. However, this API is limited to clinical decision-making and medical education scenarios, and may not be used for data production or unauthorized commercial purposes. The usage requirements include: the product must clearly display "Powered by Baichuan", and the model output must not be modified in any way that affects accuracy.

Enterprise Privatized Deployment: There is no license fee for the open source model, but the enterprise needs to evaluate its own infrastructure investment, operation and maintenance team configuration, and model version update strategy. Before purchasing, it is recommended to complete the cost calculation of the deployment plan: comprehensively consider GPU server rental, network bandwidth, operation and maintenance manpower and model update frequency, and compare the "total self-deployment cost vs annual API call fee" before making a decision.

Application scenarios

The implementation scenario of Baichuan-M2 is centered on the medical field and covers four directions: clinical assistance, medical education, health management and medical informatization.

• Clinical decision-making aid: Assist doctors in diagnostic reasoning, treatment plan selection and risk warning. M2's capabilities in emergency triage, differential diagnosis, medication safety, etc. have been quantitatively verified on HealthBench. Falling Tips: The output of M2 should be used as a "second opinion" reference, not as an independent basis for decision-making. In scenarios where diagnostic consistency is high (such as the formulation of a tumor treatment plan), final confirmation must be made by the attending physician. It is recommended to clearly mark "AI-assisted suggestions, for reference only" in the system.

• Medical Education and Simulation Training: The M2’s patient simulation capabilities make it a natural fit for standardized patient (SP) scenarios. Medical students can practice interviewing skills, history taking, and clinical reasoning through multiple conversations with M2s. The model can simulate virtual patients with different illnesses, personalities and backgrounds and provide scoring feedback. Implementation Tips: M2’s doctor-patient interaction is currently mainly in Chinese/English, and the performance in multi-lingual mixed scenarios needs further verification. In teaching scenarios, it is recommended to cooperate with teachers’ real-time guidance and comments.

• Internet medical treatment and pre-consultation: The online consultation platform can integrate M2 for pre-consultation - before patients communicate with doctors in real time, the model first completes symptom collection, medical history sorting and treatment priority assessment, and generates structured medical record summaries, directly reducing the doctor's information entry time. Implementation Tips: The accuracy of pre-consultation directly affects the efficiency of subsequent diagnosis and treatment. It is recommended to set clear "switch to manual" rules - when the model identifies high-risk signals (such as chest pain, dyspnea, severe bleeding) or the model confidence is too low, it will automatically upgrade to manual processing.

• Health management and chronic disease follow-up: Through C-end products such as Baixiaoyi, users can obtain guideline-based health advice, medication reminders, follow-up reminders and lifestyle guidance. The "Family Memory" function of M2 supports the management of health information of family members and is suitable for continuous care scenarios for chronically ill families. Implementation Tips: Health management scenarios have high requirements on the long-term memory and personalization capabilities of the model. The current context window of M2 is 32K, and long-term tracking may require the combination of external databases or summary technologies. It is recommended that this scenario be used in conjunction with Baichuan's M series API products rather than a pure self-deployment solution.

• Medical Research and Literature Analysis: M2's reasoning ability and medical knowledge coverage make it potentially applicable in medical research scenarios, including comparative analysis of clinical guidelines, structured extraction of literature reviews, and case retrospective research assistance. Implementation Tips: Scientific research scenarios have extremely high requirements for citation accuracy and traceability. The citations and suggestions output by the model must be manually verified item by item.

Applicable people

• Doctors and Clinicians: As a clinical decision-making aid, it is used for differential diagnosis reference, treatment plan tips, and quick medical knowledge query. Unsuitable Boundary: M2 cannot replace the clinical judgment and professional qualifications of doctors, and it is not recommended to use its output directly for patient treatment decisions without manual review. In high-risk scenarios such as emergency ICU, it should be strictly limited to "reference information" rather than "decision basis".

• Medical Students and Medical Education Institutions: Leverage M2's patient simulation capabilities for interview training, clinical reasoning exercises, and OSCE (Objective Structured Clinical Examination) preparation. Prerequisite: Educational institutions need to have basic AI deployment capabilities (or access through Baichuan API), and have teachers to explain and supplement the model output.

• Medical AI Developer and Algorithm Engineer: Fine-tune, quantify and deploy based on open source models to customize privatized AI services for medical institutions. M2's Apache 2.0 license provides maximum commercial flexibility and freedom of modification. Implementation Tips: It is recommended that developers first try out the M3 Plus API from the Baichuan Open Platform to verify the product concept, and then evaluate the cost-effectiveness of self-deploying M2. Pay attention to the truncation strategy of 32K context when processing long history.

• Patients and ordinary users: Get daily health consultation, symptom analysis and medical advice through Baixiaoyi App. Misfit Boundary: AI health assistants cannot replace physician diagnosis. For emergency symptoms (severe chest pain, heavy bleeding, disturbance of consciousness, severe allergic reaction, etc.), call the emergency number immediately rather than rely on AI consultation. The free version of 100 Little Doctors may have limitations in functionality and answer depth.

• Hospital Information Department and Medical Information Enterprise: Evaluate the feasibility of M2 implementation in the hospital, including computing power planning (a single card RTX 4090 can support a small amount of concurrency, and a multi-card cluster is required for production level), local adaptation (Huawei Ascend 910B), and data security compliance (privatized deployment to avoid data going out of the domain). Unfit boundary: Currently, M2 is not specifically optimized for native integration of hospital information systems (HIS/EMR), and interface development and data mapping require additional engineering investment.

Summary and Outlook

Baichuan-M2 is Baichuan Intelligence’s core milestone in the field of medical AI. It proves that "medium-sized parameter volume + innovative training paradigm" can reach or even surpass the clinical capabilities of very large models in vertical fields - 32B parameters surpass 120B or even closed-source 100 billion-level models on HealthBench. This is a completely different technical route from "violent expansion of parameter scale".

Core Advantages: The large-scale validator system provides a scalable and reusable paradigm for medical RL training; the results of HealthBench 60.1 and HealthBench Hard 34.7 verify its leading level in medical reasoning, emergency triage, and doctor-patient communication; the Apache 2.0 open source license and the deployment threshold of single card 4090 significantly reduce the acquisition cost of medical AI; Huawei Ascend Adaptation provides a complete path for localization replacement.

Current limitations: The context window of M2 is 32K, which may be insufficient in ultra-long medical record analysis and long-term health management tracking scenarios; the model is mainly optimized for medical scenarios, and its performance in general dialogue, creative writing, and non-medical field tasks is not as good as that of general models of the same scale; although Baichuan's M series iterates rapidly (M1→M2→M3→M4), the independent deployment and maintenance of each version poses a version fragmentation risk to users; M2's Tool Calling and Agent Capabilities are not fully optimized in this release, limiting the scope of integration into automated workflows.

Unfit Boundary: It should not be used as an independent diagnostic tool for direct patient treatment decisions; it is not suitable for general dialogue and content creation in non-medical fields; its performance in multi-lingual mixed medical scenarios has not been fully verified; there is a lag in real-time updated medical knowledge and guideline changes - the knowledge of the static model is as of the training data time.

Procurement and Adoption Risk Assessment: For medical institutions with a technical background, Baichuan-M2 is currently the best choice among open source medical models - obtaining medical reasoning capabilities close to GPT-5 at very low licensing and hardware costs. The recommended adoption path is: first verify the medical effect on Baixiaoyi C-side or M3 Plus API, and then evaluate the cost-effectiveness of self-deploying M2. For non-technical medical units, the hidden costs (operation and maintenance, security compliance, version management) of "open source model self-deployment" may be higher than expected. It is recommended to give priority to Baichuan's managed API (M3 Plus free plan) as the entrance, and then evaluate privatized deployment after the usage and demand are clear. In any scenario, AI output cannot replace the professional judgment of a licensed doctor, and medical liability clauses should be clearly demarcated in the procurement contract.

Related tools: deepseek, chatgpt

Version Info

• Baichuan M2 ：A new generation of commercial version of the large language model, Chinese understanding and reasoning capabilities continue to iterate. （~2026-01）
• bLove to wear 2 ：Previous version, open source large language model. （~2025-06）