Chinese LLM Tool Calling Compatibility: A Systematic Comparison (as of Aug 2026)

A systematic comparison of Chinese LLM tool-calling compatibility as of August 2026 finds that while all five major families (DeepSeek, GLM, Qwen, Kimi, MiniMax) expose OpenAI-style endpoints, significant divergences remain in payload schema fidelity, parallel-call conventions, streaming deltas, and error recovery. The evaluation, using a unified harness with GPT-5.5 as baseline, shows GLM-5.1 and MiniMax M3 leading the BenchLM tool-use benchmark, while Qwen3.7-Max demonstrated a 35-hour autonomous agent run with 1,158 tool calls.

Chinese LLM providers have matured quickly. As of August 2026, all five major Chinese LLM families — DeepSeek, GLM, Qwen, Kimi, and MiniMax 10 production variants — expose OpenAI-style tool-calling endpoints. A bare API base URL swap will often give you a valid response. But compatibility is not binary: payload schema fidelity, parallel-call conventions, streaming tool-call deltas, and error recovery still diverge enough that migrating an existing GPT-based agent requires per-model parsing and orchestration review, not a one-line config change. /chat/completions endpoint, but compatibility is a spectrum.This comparison covers Chinese LLMs with publicly accessible function-calling APIs, as of Aug 2026: All 10 variants were exercised with a unified harness: identical tool schemas, prompts, and downstream mock services. GPT-5.5 was used as the compatibility baseline. We also tested three router layers — OpenRouter, Requesty, and Eden AI — to quantify how much they normalize protocol and semantic mismatches. Six dimensions were evaluated: | Tier | Models | Deployment | Notes | |---|---|---|---| | 1. Native OpenAI-compatible | DeepSeek-V4-Pro, GLM-5.3, Qwen3.x line | Hosted API | Accept standard function schemas out of the box; GLM-5.3 and DeepSeek-V4-Pro also offer 1M-token context with adjustable reasoning effort. | | 2. Open-weight, self-hostable | Kimi K2 family | Self-host / API | Kimi K2 is a 1T-parameter MoE with 32B active parameters, positioned for coding, tool calling, and agentic workloads. Deployment guides exist for vLLM, SGLang, KTransformers, and TensorRT-LLM. | | 3. Closed, API-only | Qwen3.7-Max | Alibaba Cloud Model Studio | Hosted exclusively in mainland China, which has direct data-residency implications for EU/US enterprise use. | Sources: DeepSeek API docs https://api-docs.deepseek.com/ , Zhipu GLM API docs https://open.bigmodel.cn/dev/api , Moonshot API docs https://platform.moonshot.cn/docs/ , Alibaba Cloud Model Studio https://help.aliyun.com/zh/model-studio/ , and Turing Post's Chinese LLMs in 2026 roundup https://www.turingpost.com/p/chinese-llms-2026 . Self-hosted Kimi K2 can be served with vLLM, SGLang, KTransformers, or TensorRT-LLM. At the protocol level, DeepSeek, GLM, and Qwen all accept OpenAI-format functions and tools schemas, including tool choice and multi-turn tool-result messages. The real differences show up in three places: Provider-specific notes: On the consolidated BenchLM tool-use benchmark updated Aug 21, 2026 , the Chinese LLM field has pulled ahead of GPT-5.5 at the top: | Model | BenchLM tool-use score | Rank | |---|---|---| | GLM-5.1 | ~70.1 | Top 4 | | MiniMax M3 | ~70.1 | Top 4 | | Qwen3.7-Max | 68.8 | 6 | | GPT-5.5 | 67.8 | baseline | | Kimi K2.6 | 60.5 | 11 | | GLM-5 legacy | 58.3 | 15 | For multi-step orchestration, Alibaba's internal demonstration of Qwen3.7-Max showed a single task running unattended for 35 hours with 1,158 cumulative tool calls. That demonstrates strong context retention across long agent loops. But remember: Qwen3.7-Max is only available via Alibaba Cloud Model Studio in mainland China. If you need a self-hosted agentic workhorse, Kimi K2's open weights and tool-calling-oriented architecture make it the most flexible option. Its benchmark gap of roughly 10 points versus GLM-5.1/MiniMax M3 has to be weighed against data-control requirements. In an agent loop, every tool call adds a round trip, so latency and throughput are not optional metrics. During our unified-harness tests, the dominant cost was often not time-to-first-token but parser stability: if a streamed tool-call delta arrives as a whole JSON object in one chunk, or as semantically different incremental pieces, your parser must adapt or you silently lose calls. Error recovery is the other silent killer. We specifically tested three failure modes: malformed JSON, downstream exceptions, and empty results. Models differ widely in whether they retry, recover, or emit a well-formed but unusable response. Router layers can normalize the error transport format, but they don't change the model's internal recovery behavior. Plan for validation and retry logic at the application layer, not at the API layer. As of Aug 2026, Chinese LLM tool calling is production-ready — but only if you treat compatibility as an integration project, not a configuration change.