In This Article
Introduction
OpenClaw's open-source and model-agnostic nature has made it the primary gateway for the adoption of Chinese LLMs in 2026. As of February, the framework has released support for several competitive Chinese models, including Kimi K2.5, GLM-5, and DeepSeek V3.2. These models offer "reasoning quality" that rivals proprietary US models at a fraction of the cost — often 1/10th the price of Claude or GPT-4 APIs.
Supported Chinese Models
| Model | Origin | SWE-bench |
|---|---|---|
| Kimi K2.5 | Moonshot (China) | 76.8% (top open-source) |
| DeepSeek V3.2 | DeepSeek (China) | 73.1% |
| GLM-4.7 | Zhipu (China) | 73.8% |
Kimi K2.5 achieved 76.8% on SWE-bench in early 2026, making it the highest-performing open-source model available. DeepSeek V3.2 is favored for extreme cost efficiency.
SWE-bench Performance
SWE-bench measures software engineering capability — fixing real GitHub issues. Kimi K2.5's 76.8% approaches Claude 4.6 Opus (80.8%) and GPT-5.2 (80.0%). For many agent tasks (summarization, routing, simple tool use), the gap is negligible. For complex coding, US models still lead — but the margin is narrowing.
Cost Comparison
DeepSeek V3.2 is frequently cited at 1/10th the cost of Claude or GPT-4 APIs. A user running 50K tokens/day might pay $50/month with Claude vs $5 with DeepSeek. For high-volume Heartbeat cycles and multi-agent setups, the savings are substantial.
Trade-off: Chinese models may have weaker performance on non-English tasks, and API availability varies by region. US users should verify latency and compliance before committing.
China Market Integrations
The Chinese developer community has pushed for native integrations with domestic messaging platforms: WeChat, DingTalk, and Feishu (Lark). These are essential for OpenClaw to become the "de facto" workplace assistant in the region. US users favor Slack and WhatsApp; the agentic economy in China scales through domestic "super-apps."
OpenClaw Foundation has these integrations on the roadmap. Community forks already provide experimental WeChat support.
Configuration
Add to config.yaml:
llm:
provider: deepseek # or kimi, zhipu
model: deepseek-v3.2
api_key: ${DEEPSEEK_API_KEY}Provider-specific docs: API endpoints, rate limits, and model names vary. Check OpenClaw docs for current configuration. See Ollama for local model setup if you prefer to avoid API costs entirely.
When to Use Chinese Models
Chinese models excel when: (1) cost is a primary concern — DeepSeek at 1/10th the price of Claude, (2) you need strong Chinese language support — Kimi and GLM are native, (3) you're doing high-volume Heartbeat or multi-agent work — the savings compound. Consider US models when: (1) you need the absolute best on complex reasoning, (2) latency to China-based APIs is an issue, (3) compliance requires US-hosted inference. See OpenClaw AI models for the full comparison.
Kimi Claw vs Self-Hosted
Kimi Claw is Moonshot's managed service — they host the agent, you use it via WeChat. If you want K2.5 without running infrastructure, Kimi Claw is the option. Self-hosted OpenClaw with K2.5 API gives you full control. Choose managed for convenience; self-hosted for control and data residency. Both use the same model.
Wrapping Up
Chinese LLMs offer a compelling cost/performance trade-off for OpenClaw users. Kimi K2.5 leads on benchmarks; DeepSeek leads on price. See OpenClaw AI models and cost pricing for full comparison.