Chinese AI Models in 2026: Kimi, Qwen, GLM, DeepSeek

Chinese AI models had a busy 2026: the major labs released new flagships aimed at coding and agent work, and most of them shipped with open weights and one-million-token context windows. For a team outside China, the practical questions are narrower than the headlines: which model is meant for which job, what it costs per token, and how to call several of them without opening an account with each lab.

This guide covers seven labs whose current models are in the BoostRail catalog, as of 16 October 2026. Model descriptions come from each lab's own model card or announcement, linked in each section. Benchmark figures in those sources are reported by the labs themselves, so treat them as a starting point for your own tests. The capabilities described are the models' own; which input types a given API passes through depends on the provider serving it.

Why Chinese AI models are worth evaluating

• Open weights. Most of the models below publish their weights, under licences that range from MIT and Apache 2.0 to each lab's own terms. That gives you a way out if you later want to host a model yourself, and it lets the wider community test the models independently.
• Long context as standard. Kimi K3, Qwen3.8-Max, DeepSeek V4.1 Flash, MiniMax M3, MiMo V2.6 Pro and Hy4 preview all list a context window of about one million tokens.
• Price. On the BoostRail catalog, DeepSeek V4.1 Flash's output price is about one sixteenth of Claude Sonnet 5's. Prices vary: Kimi K3 costs more per output token than Claude Sonnet 5, while the other flagships in this guide cost less per output token.
• Coding and agents first. Nearly every flagship released this year leads its announcement with coding, tool use and long-running agent tasks.

The seven labs and their current models

Moonshot AI: Kimi

Kimi K3 is a mixture-of-experts model with 2.8 trillion total parameters, 104 billion of them active per token, and a context window of 1,048,576 tokens. Moonshot's model card describes it as built for long-horizon coding, agentic knowledge work and reasoning, accepts text and images, and notes that it works best with Moonshot's own Kimi Code CLI. The weights are released under the Kimi K3 License. The catalog also carries the earlier kimi-k2.6, kimi-k2.7-code and kimi-k2.5 at lower prices.

Alibaba: Qwen

Alibaba announced Qwen3.8-Max on 3 August 2026 as the most capable model in the Qwen series so far: about 2.4 trillion total parameters with roughly 95 billion active, a context window of up to one million tokens, and native image and video understanding. The announcement leads with autonomous coding and long-horizon execution, and said the weights would follow. Qwen's strength for builders is the ladder below the flagship: qwen3.7-plus, qwen3.7-flash and qwen3-coder-flash cover cheaper tiers from the same lab.

Z.ai: GLM

GLM-5.3 is a 753-billion-parameter open-weights model. Z.ai's model card calls it its most capable open-weights model for coding and says it shares GLM-5.2's base model, with the gains coming from post-training. The card highlights terminal and software-engineering benchmarks such as Terminal Bench and DeepSWE. For high-volume work, glm-5.3-flash is the low-cost option.

DeepSeek

DeepSeek V4.1 Flash arrived in September 2026 as the successor to V4 Flash. The model card describes 552 billion backbone parameters, with 8 billion active per token when reading input and 16 billion when writing output, a context of up to one million tokens, native image input, and an MIT licence. DeepSeek positions it for cost-efficient, input-heavy agent workloads. For how the V4 family split work between its two tiers, see DeepSeek V4 Pro vs Flash.

MiniMax

MiniMax M3 has about 428 billion total parameters with about 23 billion active, and a one-million-token context. The model card emphasises long-horizon agent work and coding, and long-context efficiency from MiniMax's sparse attention design. It accepts text, images and video. The weights are under the MiniMax Community License. minimax-m2.7 is the cheaper previous generation.

Xiaomi: MiMo

MiMo V2.6 Pro is Xiaomi's flagship: a sparse mixture-of-experts model with 1.02 trillion total and 42 billion active parameters, a one-million-token context, and input across text, images, video and audio. Xiaomi publishes it under the MIT licence; see the model card. mimo-v2.6-flash is the smaller companion model.

Tencent: Hy

Hy4 preview is Tencent's new flagship, with 770 billion total parameters, about 49 billion active, a one-million-token context and an Apache 2.0 licence. The model card is explicit that this is an early version, and lists known issues including overly long reasoning and over-verification. Worth testing, with that in mind.

Prices on BoostRail

Prices in USD per 1M tokens on 16 October 2026. Live prices for every model are on the Models page.

For reference, claude-sonnet-5 is 1.9 input and 9.5 output on the same date. For how these prices turn into a monthly bill, see LLM API Pricing Explained.

Which model for which job

This is a starting shortlist based on what each lab says the model is built for. Your own test is what decides.

Several of these models also work as the main model inside Claude Code through the Anthropic format; see Claude Code with Other Models for the setup and our test results.

Calling Chinese AI models from outside China

There are three routes, each with a real trade-off.

1. Each lab's international platform. Most of these labs run their own international API platform. You get the lab's own pricing and features, and you manage one account, key, balance and set of docs per lab.
2. Self-hosting the open weights. Full control, and full responsibility. A model with hundreds of billions or trillions of parameters needs a multi-GPU cluster to serve at useful speed, so this pays off mainly at large, steady volume.
3. One API in front of all of them. BoostRail serves every model above through one OpenAI-compatible endpoint and one Anthropic-compatible endpoint, with one key and one USD balance. Switching models is a change to the model string. The OpenAI-compatible API page has the details.

Testing them on your own workload

Because the endpoint is the same for every model, a side-by-side test is a loop. This streams one prompt to five models and prints each answer as it arrives, followed by its token usage:

from openai import OpenAI

client = OpenAI(api_key="YOUR_API_KEY", base_url="https://api.boostrail.com/v1")
prompt = "Write a Python function that parses ISO 8601 durations such as PT1H30M."

for model in ["kimi-k3", "glm-5.3", "qwen3.8-max", "deepseek-v4.1-flash", "minimax-m3"]:
    print(f"\n=== {model}")
    stream = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": prompt}],
        stream=True,
    )
    for chunk in stream:
        if chunk.choices:
            print(chunk.choices[0].delta.content or "", end="")
        if chunk.usage:
            print("\n", chunk.usage)

Streaming matters here. Reasoning models differ a lot in how much they think before the first visible word, and on BoostRail a non-streaming request stops at 180 seconds. When we ran this exact prompt on 16 October 2026, GLM-5.3 reasoned for about ten minutes and used 29,570 output tokens before finishing its answer, and Qwen3.8-Max took about three minutes and 17,329 output tokens. With max_tokens set to 4000, both spent the whole budget on reasoning and returned no text.

Most reasoning models also accept a reasoning effort setting, which BoostRail's Chat Completions endpoint takes as reasoning_effort. With reasoning_effort set to low, the same prompt took GLM-5.3 3,348 output tokens and Qwen3.8-Max 1,918, about a ninth of what they used by default. The effect varies by model, so measure it on each one.

Use prompts from your real workload, not puzzles, and compare three things: whether the answer is right, how many output tokens it took, and how long it took. All three show up in the bill and in what users wait for.

Frequently asked questions

Are Chinese AI models open source?

Most of the current flagships publish their weights. Licences differ: DeepSeek V4.1 Flash and MiMo V2.6 Pro use MIT, Hy4 preview uses Apache 2.0, and Kimi K3, MiniMax M3 and GLM-5.3 use each lab's own licence. Read the licence file before you build on self-hosted weights.

Can I use Chinese AI models from outside China?

Yes. Most of these labs run an international API, and BoostRail serves all the models in this guide through one key with USD billing.

Do they support tool calling?

On 16 October 2026 we ran a two-turn tool call through BoostRail's Anthropic-format endpoint on kimi-k3, glm-5.3, qwen3.8-max, minimax-m3 and deepseek-v4.1-flash, and all five called the tool and used its result correctly. The Models page also marks capabilities that passed automated checks on every route serving a model.

Which one is the cheapest?

In the table above, qwen3.7-flash has the lowest input and output prices, with glm-5.3-flash and deepseek-v4.1-flash close behind. Price per token is only half the comparison; check how many tokens each one needs to finish your task.

How do I switch between them?

Change the model string. The endpoint, the key and the request format stay the same, so you can route different tasks to different models in the same application.