August 13, 2025The Best Chinese Open Agentic/Reasoning Models (2025): Expanded Review, Comparative Insights & Use Cases
China continues to set the pace in open-source large-language-model innovation, especially for agentic architectures and deep reasoning. Here is a comprehensive, up-to-date guide to the best Chinese open agentic/reasoning models, expanded with the newest and most influential entrants.
1. Kimi K2 (Moonshot AI)
- Profile: Mixture-of-Experts architecture, up to 128K context, superior agentic ability and bilingual (Chinese/English) fluency.
- Strengths:
- High benchmark performance in reasoning, coding, mathematics, and long-document workflows.
- Well-rounded agentic skills: tool-use, multi-step automation, protocol adherence.
- Use Cases: General-purpose agentic workflows, document intelligence, code generation, multi-language enterprise.
- Why Pick: The most balanced all-rounder for open source agentic systems.
2. GLM‑4.5 (Zhipu AI)
- Profile: 355B total parameters, native agentic design, long-context support.
- Strengths:
- Purpose-built for complex agent execution, workflow automation, and tool orchestration.
- MIT-licensed, established ecosystem (700,000+ developers), rapid community adoption.
- Use Cases: Multi-agent applications, cost-effective autonomous agents, research requiring agent-native logic.
- Why Pick: For building deeply agentic, tool-integrated, open LLM apps at scale.
3. Qwen3 / Qwen3-Coder (Alibaba DAMO)
- Profile: Next-gen Mixture-of-Experts, control over reasoning depth/modes, dominant multilingual model (119+ languages), repo-scale coding specialist.
- Strengths:
- Dynamic “thinking/non-thinking” switching, advanced function-calling, top scores in math/code/tool tasks.
- Qwen3-Coder: Handles 1M tokens for code, excels at step-by-step repo analysis and complex dev workflows.
- Use Cases: Multilingual tools, global SaaS, multi-modal logic/coding apps, Chinese-centric dev teams.
- Why Pick: Precise control, best multilingual support, world-class code agent.
4. DeepSeek-R1 / V3
- Profile: Reasoning-first, multi-stage RLHF training, 37B activated parameters per query (R1); V3 expands to 671B for world-class math/code.
- Strengths:
- State-of-the-art on logic and chain-of-thought reasoning, surpasses most Western rivals in scientific tasks.
- “Agentic Deep Research” protocols for fully autonomous planning/searching/synthesizing information.
- Use Cases: Technical/scientific research, factual analytics, environments that value interpretability.
- Why Pick: Maximum reasoning accuracy, agentic extensions for research and planning.
5. Wu Dao 3.0 (BAAI)
- Profile: Modular family (AquilaChat, EVA, AquilaCode), open-source, strong long-context and multimodal capabilities.
- Strengths:
- Handles both text and images, supports multilingual workflows, well suited for startups and low-compute users.
- Use Cases: Multimodal agentic deployment, SMEs, flexible application development.
- Why Pick: Most practical and modular for multimodal and smaller-scope agentic tasks.
6. ChatGLM (Zhipu AI)
- Profile: Edge-ready, bilingual, context windows up to 1M, quantized for low-memory hardware.
- Strengths:
- Best for on-device agentic applications, long-document reasoning, mobile deployments.
- Use Cases: Local/gov deployments, privacy-sensitive scenarios, resource-constrained environments.
- Why Pick: Flexible scaling from the cloud to edge/mobile, strong bilingual proficiency.
7. Manus & OpenManus (Monica AI / Community)
- Profile: China’s new benchmark for general AI agents: independent reasoning, real-world tool use, and agentic orchestration. OpenManus enables agentic workflows based on many underlying models (Llama variants, GLM, DeepSeek).
- Strengths:
- Natural autonomous behavior: web search, travel planning, research writing, voice commands.
- OpenManus is highly modular, integrating Chinese open models or proprietary LLMs for tailored agentic tasks.
- Use Cases: True mission-completion agents, multi-agent orchestration, open-source agentic frameworks.
- Why Pick: First major step towards AGI-like agentic applications in China.
8. Doubao 1.5 Pro
- Profile: Known for superior fact consistency and reasoning logic structure, high context window (expected 1M+ tokens).
- Strengths:
- Real-time problem-solving, superior logic structure, scalable to multiple enterprise deployments.
- Use Cases: Scenarios emphasizing logical rigor, enterprise-level automation.
- Why Pick: Enhanced reasoning and logic, strong in scalable business environments.
9. Baichuan, Stepfun, Minimax, 01.AI
- Profile: “Six Tigers” of Chinese open AI (per MIT Tech Review), each offering strong reasoning/agentic features in their domain (Stepfun/AIGC, Minimax/memory, Baichuan/multilingual legal).
- Strengths:
- Diverse applications: from conversational agents to domain-specific logic in law/finance/science.
- Why Pick: Choose for sector-specific requirements, especially high-value business apps.
Comparative Table
| Model | Best For | Agentic? | Multilingual? | Context Window | Coding | Reasoning | Unique Features |
|---|---|---|---|---|---|---|---|
| Kimi K2 | All-purpose agentic | Yes | Yes | 128K | High | High | Mixture-of-Experts, fast, open |
| GLM-4.5 | Agent-native applications | Yes | Yes | 128K+ | High | High | Native task/planning API |
| Qwen3 | Control, multilingual, SaaS | Yes | Yes (119+) | 32K–1M | Top | Top | Fast mode switching |
| Qwen3-Coder | Repo-scale coding | Yes | Yes | Up to 1M | Top | High | Step-by-step repo analysis |
| DeepSeek-R1/V3 | Reasoning/math/science | Some | Yes | Large | Top | Highest | RLHF, agentic science, V3: 671B |
| Wu Dao 3.0 | Modular, multimodal, SME | Yes | Yes | Large | Mid | High | Text/image, code, modular builds |
| ChatGLM | Edge/mobile agentic use | Yes | Yes | 1M | Mid | High | Quantized, resource-efficient |
| Manus | Autonomous agents/voice | Yes | Yes | Large | Task | Top | Voice/smartphone, real-world AGI |
| Doubao 1.5 Pro | Logic-heavy enterprise | Yes | Yes | 1M+ | Mid | Top | 1M+ tokens, logic structure |
| Baichuan/etc | Industry-specific logic | Yes | Yes | Varies | Varies | High | Sector specialization |
Key Takeaways & When to Use Which Model
- Kimi K2: Best all-rounder—if you want balanced agentic power and reasoning, long context, broad language support.
- GLM-4.5: Native agent, great for autonomous task apps or tool orchestration; open-source ecosystem leader.
- Qwen3/Qwen3-Coder: Superior for agile control, multilingual/enterprise tasks, and high-level code agentics.
- DeepSeek-R1/V3: Gold standard for chain-of-thought reasoning, math/science, and research-grade logic.
- Wu Dao 3.0: Most practical for SMEs/startups, especially for multimodal (text/image/code) agentic solutions.
- ChatGLM/Manus/OpenManus: Field deployment, privacy, and truly autonomous agents—recommended for cutting-edge real-world use, on-device, or collaborative multi-agent tasks.
- Doubao 1.5 Pro/Baichuan/Six Tigers: Consider for sector-specific deployments or if factual consistency and specialized logic are critical.
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Gemini API key configured successfully!")
Downloading sample research papers...")
for filename, url in papers.items():
filepath = papers_dir / filename
if not filepath.exists():
try:
response = requests.get(url, stream=True, timeout=30)
response.raise_for_status()
with open(filepath, 'wb') as f:
for chunk in response.iter_content(chunk_size=8192):
f.write(chunk)
print(f"
Failed to download {filename}: {e}")
else:
print(f"
Already exists: {filename}")
return str(papers_dir)
papers_directory = download_sample_papers()
def create_gemini_settings(paper_dir: str, temperature: float = 0.1):
"""Create optimized settings for PaperQA2 with Gemini models"""
return Settings(
llm="gemini/gemini-1.5-flash",
summary_llm="gemini/gemini-1.5-flash",
agent=AgentSettings(
agent_llm="gemini/gemini-1.5-flash",
search_count=6,
timeout=300.0,
),
embedding="gemini/text-embedding-004",
temperature=temperature,
paper_directory=paper_dir,
answer=dict(
evidence_k=8,
answer_max_sources=4,
evidence_summary_length="about 80 words",
answer_length="about 150 words, but can be longer",
max_concurrent_requests=2,
),
parsing=dict(
chunk_size=4000,
overlap=200,
),
verbosity=1,
)
PaperQA Agent initialized with papers from: {papers_directory}")
async def ask_question(self, question: str, use_agent: bool = True):
"""Ask a question about the research papers"""
print(f"n
Question: {question}")
print("
Searching through research papers...")
try:
if use_agent:
response = await agent_query(query=question, settings=self.settings)
else:
response = ask(question, settings=self.settings)
return response
except Exception as e:
print(f"
ANSWER:")
print("="*60)
answer_text = getattr(response, 'answer', str(response))
print(f"n{answer_text}")
contexts = getattr(response, 'contexts', getattr(response, 'context', []))
if contexts:
print("n" + "-"*40)
print("
SOURCES USED:")
print("-"*40)
for i, context in enumerate(contexts[:3], 1):
context_name = getattr(context, 'name', getattr(context, 'doc', f'Source {i}'))
context_text = getattr(context, 'text', getattr(context, 'content', str(context)))
print(f"n{i}. {context_name}")
print(f" Text preview: {context_text[:150]}...")
async def multi_question_analysis(self, questions: list):
"""Analyze multiple questions in sequence"""
results = {}
for i, question in enumerate(questions, 1):
print(f"n
Processing question {i}/{len(questions)}")
response = await self.ask_question(question)
results = response
if response:
print(f"
Starting comparative analysis on: {topic}")
return await self.multi_question_analysis(questions)
async def basic_demo():
"""Demonstrate basic PaperQA functionality"""
agent = PaperQAAgent(papers_directory)
question = "What is the transformer architecture and why is it important?"
response = await agent.ask_question(question)
agent.display_answer(response)
print("
Running basic demonstration...")
await basic_demo()
async def advanced_demo():
"""Demonstrate advanced multi-question analysis"""
agent = PaperQAAgent(papers_directory, temperature=0.2)
questions = [
"How do attention mechanisms work in transformers?",
"What are the computational challenges of large language models?",
"How has pre-training evolved in natural language processing?"
]
print("
Running advanced multi-question analysis...")
results = await agent.multi_question_analysis(questions)
for question, response in results.items():
print(f"n{'='*80}")
print(f"Q: {question}")
print('='*80)
if response:
answer_text = getattr(response, 'answer', str(response))
display_text = answer_text[:300] + "..." if len(answer_text) > 300 else answer_text
print(display_text)
else:
print("
COMPARATIVE ANALYSIS RESULTS")
print("="*80)
for question, response in results.items():
print(f"n
Interactive agent ready! You can now ask custom questions:")
print("Example: await interactive_query('How do transformers handle long sequences?')")
def print_usage_tips():
"""Print helpful usage tips"""
tips = """
Question Formulation:
- Be specific about what you want to know
- Ask about comparisons, mechanisms, or implications
- Use domain-specific terminology
2. 
Model Configuration:
- Gemini 1.5 Flash is free and reliable
- Adjust temperature (0.0-1.0) for creativity vs precision
- Use smaller chunk_size for better processing
3. 
Performance Optimization:
- Limit concurrent requests for free tier
- Use smaller evidence_k values for faster responses
- Cache results by saving the agent state
5. 
Example Questions to Try:
- "Compare the attention mechanisms in BERT vs GPT models"
- "What are the computational bottlenecks in transformer training?"
- "How has pre-training evolved from word2vec to modern LLMs?"
- "What are the key innovations that made transformers successful?"
"""
print(tips)
print_usage_tips()
def save_analysis_results(results: dict, filename: str = "paperqa_analysis.txt"):
"""Save analysis results to a file"""
with open(filename, 'w', encoding='utf-8') as f:
f.write("PaperQA2 Analysis Resultsn")
f.write("=" * 50 + "nn")
for question, response in results.items():
f.write(f"Question: {question}n")
f.write("-" * 30 + "n")
if response:
answer_text = getattr(response, 'answer', str(response))
f.write(f"Answer: {answer_text}n")
contexts = getattr(response, 'contexts', getattr(response, 'context', []))
if contexts:
f.write(f"nSources ({len(contexts)}):n")
for i, ctx in enumerate(contexts, 1):
ctx_name = getattr(ctx, 'name', getattr(ctx, 'doc', f'Source {i}'))
f.write(f" {i}. {ctx_name}n")
else:
f.write("Answer: No response availablen")
f.write("n" + "="*50 + "nn")
print(f"
Results saved to: {filename}")
print("
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