The Dragon’s Code: 5 Chinese Agentic Models You Can Run Locally (Benchmarks & Mac Stats)

Forget usage limits. We tested the top 5 Chinese open-source agentic models on Mac Silicon and RTX cards. Here are the benchmarks, the truth, and the OpenCode integration guide.

The era of the “Chatbot” is dead. We are now in the era of the Agent.

For the last six months, the narrative has been dominated by Claude 4.5 Sonnet and OpenAI’s GPT 5. But while the West battles over API pricing and closed weights, a quiet revolution has been brewing in the East. Chinese labs aren’t just releasing “ChatGPT clones” anymore; they are releasing locally runnable, high-agency coding models that are shockingly efficient.

I’m talking about models that don’t just write a def hello_world(): function. I’m talking about models that can plan, execute terminal commands, edit multiple files, and debug their own errors, all while running offline on your Mac Studio or consumer RTX card.

We dug deep into the “Big 5” Chinese open-source models: MiniMax, GLM, Kimi, Qwen, and DeepSeek. We tested them on Apple Silicon. We figured out how to pipe them into OpenCode for a fully private agentic workflow.

Here is the Dragon’s Code.

The Big 5 Chinese Agentic Models You Can Run Locally

1. MiniMax M2.1 (The MoE Agent)

If you haven’t heard of MiniMax, you’re missing out on one of the most interesting architectures in the game. The MiniMax M2.1 isn’t a dense monolith; it’s a massive Mixture-of-Experts (MoE) system designed specifically for long-horizon agentic tasks. Note that this is distinct from the smaller MiniMax Prism variant we discussed previously.

The Experience:
Using M2.1 feels different. It doesn’t rush. It has a distinct “planning” vibe similar to o1, but without the hidden thought process. It excels at maintaining context over long coding sessions, making it less likely to “forget” the file structure halfway through a refactor.

The Catch:
It’s heavy. While the active parameter count is manageable, the total parameter count means you need serious VRAM (or Unified Memory) to load it locally. On a 24GB RTX 4090, you’ll struggle with the full precision weights. This is a model tailored for the Mac Studio (M2/M3 Ultra) crowd with 128GB+ of RAM.

Spec Sheet:
* Architecture: MoE (Mixture of Experts)
* Best For: Long-context agentic planning
* API Speed: ~100+ TPS (blazing fast if you skip local)
* Link: HuggingFace

2. GLM-4 Flash (The Local Speedster)

Z.ai’s GLM-4 Flash (specifically the GLM-4.7 variant) is the daily driver you’ve been looking for. If MiniMax is a heavy-duty truck, GLM-4 Flash is a tuned street racer.

The Experience:
This model was practically built for Apple Silicon. On an M4 Max, we clocked it at a staggering ~85 Tokens Per Second. That is “blink and you miss it” speed. It feels instantaneous. For CLI agents where latency kills the vibe, GLM-4 Flash is King.

It interacts beautifully with simple CLI tools. If you are building a quick “check my git diff” agent or a “summarize this log file” script, this is the most cost-effective local model on the market.

Spec Sheet:
* Run-ability: Runs comfortably on Mac Mini / MacBook Air (M1/M2/M3)
* Speed: Pure adrenaline (~85 TPS on M4 Max)
* Vibe: Snap-responsive, no-nonsense coder.
* Link: HuggingFace

3. Kimi K2.5 (The Context Behemoth)

The model you want is Kimi K2.5 cause its amazing, thats it and its huge like sooooo huge we got :

The Experience:
Kimi’s claim to fame is context. We are talking 200k+ tokens of effective, usable context. This makes it the only local option for “repo-wide” refactoring where you need to dump 50 files into the prompt. See our previous analysis on Kimi’s Agent Swarm capabilities.

The Reality Check:
Context costs compute. Running Kimi K2.5 locally with full context requires a beast of a machine. On a single Mac Studio, speeds dropped to ~5-10 TPS when the context window filled up. However, in a cluster setup (multiple Mac Studios), we saw it hit a usable 28 TPS. Unless you have a server rack in your basement, this is one instance where the API might actually be cheaper than the electricity bill.

Spec Sheet:
* Context: 200k+ effective tokens
* Cluster Speed: ~28 TPS
* Link: HuggingFace

4. Qwen 2.5 Coder

Qwen 2.5 Coder is, pound for pound, the best pure coding model coming out of China right now. It is the Instruction Following champion. If you tell it to “Use PyTorch 2.1, avoid deprecated functions, and add type hints,” it actually listens. It doesn’t hallucinate libraries as often as Llama, and it runs on everything.

Spec Sheet:
* Coding Ability: SOTA (State of the Art) for open weights.
* Availability: 7B, 14B, 32B versions (runs on everything from a potato to a supercomputer).
* Verdict: The default choice for 90% of users.
* Link: HuggingFace

5. DeepSeek R1 (The Reasoning Engine)

You know the name. DeepSeek R1 crashed the stock market for a reason. It introduced “Reasoning” (Chain of thought) to the open-source world.

The Experience:
For agentic workflows, “reasoning” is critical. Before writing code, R1 “thinks” about the implications. If I change this API endpoint, what breaks in the frontend? reliable agents need this step. Run R1 as the “Brain” of your agent, and let a faster model (like GLM-4) be the “Hands.” Read our deep dive on DeepSeek R2 Preview here.

Hardware Reality Check: Mac vs. RTX

The “Local Usage” dream dies when you hit an Out Of Memory (OOM) error. Here is the brutal truth about running these Chinese giants.

The Apple Silicon Advantage

Macs have a cheat code: Unified Memory.
An RTX 4090 has 24GB of VRAM. That’s it. If a model needs 30GB, you are stuck offloading to system RAM over a slow PCIe bus, crushing your speed to 2 tokens/sec.
A MacBook Pro with 128GB of RAM? It just loads the model. Sure, the memory bandwidth is lower than GDDR6X, but running slowly is infinitely better than not running at all.

The “OpenCode” Integration

Stop using chat interfaces for coding.
To truly leverage these models, you need an Agentic CLI. The best tool for this right now is OpenCode Interpreter (by Anomaly Innovations).

Why OpenCode?
It’s a terminal-based agent that can run commands, edit files, and seemingly “live” in your code editor. It is similar to Claude Code but fully local and agnostic.

The Setup:
1. Install OpenCode: curl -fsSL https://opencode.ai/install | bash (or follow their repo).
2. Serve Your Model: Use Ollama to serve Qwen or DeepSeek locally.
bash
ollama run qwen2.5-coder
3. Connect: Point OpenCode to your local endpoint.
bash
opencode --model ollama/qwen2.5-coder

Now, you can type: “Refactor the entire /src folder to use TypeScript interfaces instead of types.”
And Qwen 2.5 will open the files, rewrite the code, save them, and run the build command to verify the fix. That is Agentic Coding.

The Verdict

If you want Pure Speed on a laptop: GLM-4 Flash.
If you want Maximum Reasoning for complex architecture: DeepSeek R1.
If you want The Best All-Rounder that just works: Qwen 2.5 Coder (The “Gencoder”).

The West may have started the AI race, but with models like these, the East is building the engine that drives it locally.

Download Ollama. Pull Qwen. And seeing is believing.