Chinese-Fluency for Voice Agents outside of China

你好！I'm working from Taiwan for a couple weeks, while my two daughters experience the culture for the first time. I'm always impressed at my girls' dual-fluency, especially given how English and Chinese (Mandarin in our case) share basically no etymology or grammar overlap. It got me thinking about LLMs, their varied language abilities, and what would go into building the best voice agent to serve here in Taiwan.

For most languages, Western frontier AI Labs like OpenAI, Google, and Anthropic offer the obvious choice for best fluency. The major exception is Chinese. Chinese AI Labs generally outperform Western Labs in fluency. For the 50+mm native chinese speakers outside of mainland China, there is a major tradeoff between language fluency vs trust. This post unpacks why, explores three options, and shares how we at Prim approach the problem for global Chinese‑speaking customers.

Which model(s) is most fluent?

Chinese (as well as other English-distant languages) pose interesting challenges to LLMs because character set, tokenization quirks, and sparse morphology expose weaknesses that English‑centric pre‑training can't hide. Models from OpenAI, Google, and Anthropic haven't been benchmarked on Chinese-language benchmarks recently, but Chinese-fluency benchmarking had legacy models from Western labs lagging their Chinese peers.

The best thing we have to go on is the internet-vibe test, as well as our own intuition. Some of that intuition is obvious: all things equal, we'd expect an LLM trained by Chinese researchers, who have access to more Chinese language data, and are likely generating Chinese-language synthetic data to be more fluent. Further, we have some anecdotal evidence such as the way the Deepthink R1 model will switch to Chinese during its reasoning phase before eventually outputting an English question.

There is a significant fog of war in today's fast moving model development. All considered, if my only goal was to have an agent (voice or otherwise) with maximal Chinese fluency, I'd use a Chinese model like Deepseek or Qwen. But of course, we're never optimizing for a single trait...

Key Trade‑Offs

Particularly for products meant to serve the 50+ million native Chinese speakers that live in Taiwan, Singapore, Malaysia, North America, or Europe, there are major tradeoffs to consider before getting your Deepseek API key.

Latency & Voice UX – Humans notice anything above ~300 ms in a live conversation. A US call from a US server to access a model in Beijing server is going to add ~500 ms round‑trip before TTS even starts. This will create unacceptable latency, particularly depending on where (and whether) you've localized the rest of your infrastructure like STT, TTS and cloud hosting.

Training Bias & State Influence – Mainland‑hosted models embed policy filters that aren't fully understood. What we know is that post-training has created filters on hot-button issues like Tiananmen Square or Taiwanese sovereignty. Combine that with emerging research that shows things such as LLMs fine-tuned to write insecure code become broadly misaligned, and it's unclear exactly how or if these guard‑rails can leak even into innocuous topics.

Trust & Data Residency – Western labs offer "zero data retention" and SOC‑2 attestation which I think is generally trustworthy despite though notably the US Government does intervene. PRC law, by contrast, empowers the state to inspect or seize data at any time. If you handle any sensitive data, that risk is likely unacceptable.

Three Roads for Global Products

So, when if you want fluent Mandarin voice products, you have three real paths forward that really come down to tradeoffs between fluency, trust, and effort. Here are your three options:

1. Plug into a Mainland API

The easiest route is simply to call DeepSeek, Qwen, or similar mainland endpoints. Fluency is maximal because the weights are trained on vast Chinese web corpora and tuned by native speakers. If the latency, content filters, and data security laws don't bother you, here you go.

2. Stay with Western Frontier Labs, but deploy in Asia

OpenAI on Azure Hong Kong or Singapore, Gemini on Google Singapore, and Anthropic via Vertex AI Tokyo keep inference within ~80 ms of Taipei and Singapore POPs. You retain SOC‑2 and zero‑data‑retention options from vendors you know. English instruction‑following is state‑of‑the‑art, while as of late June 2025 at the time of this writing, Chinese fluency is improving.

3. Self‑host Chinese weights

DeepSeek and Qwen publish full checkpoints under permissive licenses that will likely need your own finetuning. Then, assuming you have access to top-of-the-line hardware, you'll need to host the models and update them as new versions come out.

Bottom-line: For 99% of products intended for users outside of Mainland China, I recommend sticking with the frontier Western Labs.

How We Solve It at Prim

Our engagement always begins with a consultative deep‑dive. That means, we sit down to understand what problems you are trying to solve and how you might think about Voice AI as part of your stack. We come with strong opinions on how to solve those problems, but ultimately the decision is up to you.

Beneath the surface, Prim's orchestration layer is fully modular. Any model that exposes an API endpoint, such as OpenAI, Gemini, DeepSeek, Qwen, or your own, can be dropped into our pipeline without touching the rest of your voice flow. All of this runs on Google‑backed global infrastructure. Notably, this includes Taipei as a hub for Asia. And when off‑the‑shelf isn't enough, we can help with white‑glove fine‑tuning and private model hosting.

Conclusion

The three paths I've outlined consider Mainland APIs, Western labs deployed in‑region, or self‑hosted OSS weights from Chinese labs. Each deliver a different blend of fluency, trust, and effort. You'll probably want to stick with Western Labs, but if you need higher fluency, you do have options.

再见！