Best Chinese AI Models in 2026: DeepSeek, Kimi, Qwen, GLM and MiniMax

Which Chinese lab ships the model you should build on today? The answer has changed more than once in 2026. The best Chinese AI models are no longer a one-horse race: DeepSeek’s current API models sit next to Moonshot’s Kimi K3 and K2.6, Alibaba’s Qwen family, Zhipu’s GLM-5 series and MiniMax’s M-series — each with a different focus. This guide compares them on what matters for English-speaking developers and teams: price per token, vendor-reported benchmark evidence, licensing, and where each one fits. Every competitor price below was read on the vendor’s own pricing page on 23 October 2026, and every benchmark is labelled with who reported it.

The shortlist: five labs worth your attention

If you’re building on a Chinese model in 2026, five labs cover most of the credible options. Other names (Baichuan, 01.AI, ByteDance Doubao, Tencent Hunyuan) ship interesting work, but the labs below publish open weights on Hugging Face and expose public APIs in English.

• DeepSeek — Hangzhou-based; two API models, deepseek-flash and deepseek-v4-pro, both with MIT-licensed weights.
• Moonshot AI (Kimi) — agentic-coding focus; Kimi K3 and K2.6 open weights (published on Hugging Face in June and April 2026).
• Alibaba (Qwen) — a wide family mixing Apache 2.0 open models with hosted Max tiers.
• Zhipu AI (GLM / Z.ai) — GLM-5 and GLM-5.1 under MIT, with GLM-5.3 (published on Hugging Face in August 2026) at the top of Z.ai’s “Latest Models” price list.
• MiniMax — agent- and coding-oriented; M2.5 and the multimodal M3 (published on Hugging Face in February and June 2026).

At-a-glance comparison (October 2026)

Each row is taken from the vendor’s own pricing page or Hugging Face model card, checked on 23 October 2026. Prices are standard USD per 1M tokens for the shortest context tier; DeepSeek rows show peak / off-peak. Cross-version comparisons are unavoidable here — vendors release on different schedules — and model sizes are as stated by each vendor.

Sources (all accessed 23 October 2026): DeepSeek pricing; Moonshot pricing (kimi-k3 also bills cache writes at $3.00 / $6.00 per 1M for 5-minute / 1-hour TTL); Alibaba Cloud Model Studio pricing (International, ≤1M-token tier); Z.ai pricing; MiniMax pricing (Standard tier, ≤512K input tokens, after the page’s “permanent 50% off” on $0.60 / $2.40); model sizes, context and licences from each model’s Hugging Face card. Prices change — verify before committing.

DeepSeek — two API models, peak and off-peak pricing

DeepSeek’s API serves two models: deepseek-flash, served by DeepSeek-V4.1-Flash-1017 (released 17 October 2026, the only current DeepSeek API model with image input), and deepseek-v4-pro, served by DeepSeek-V4-Pro-0813 (released 13 August 2026, the official release that superseded the April 2026 V4 preview). Both publish weights under MIT, and both offer a 1M-token context window with up to 384K tokens of output.

Thinking mode is a request parameter, not a separate model ID, and it is on by default. You hit the OpenAI-compatible POST /chat/completions endpoint and control it with "thinking": {"type": "enabled"} or {"type": "disabled"} plus reasoning_effort (low, high or max); the API returns reasoning_content alongside the final content. The legacy IDs deepseek-chat and deepseek-reasoner were scheduled for retirement on 2026-07-24 at 15:59 UTC and are no longer listed in the official docs.

As of October 2026, deepseek-flash lists $0.006 cache hit / $0.30 cache miss / $1.20 output per 1M tokens at peak and $0.003 / $0.15 / $0.60 off-peak; deepseek-v4-pro lists $0.044 / $1.32 / $3.96 at peak and $0.022 / $0.66 / $1.98 off-peak. Peak hours are 01:00–04:00 and 06:00–10:00 UTC, Monday to Friday, excluding Chinese public holidays. In the table above, deepseek-flash at peak matches MiniMax-M3’s discounted Standard rate and sits well below qwen3.8-max and GLM-5.3; off-peak it halves again. Smaller “Flash” tiers from other labs are priced in the same range — Z.ai lists GLM-5.3-Flash at $0.15 / $0.50 and MiniMax lists MiniMax-M3.1-Flash at $0.15 / $0.60. For deeper coverage, see our DeepSeek V4-Flash and DeepSeek V4-Pro pages, or the broader DeepSeek models hub.

import os
from openai import OpenAI

client = OpenAI(base_url="https://api.deepseek.com", api_key=os.environ["DEEPSEEK_API_KEY"])
resp = client.chat.completions.create(
    model="deepseek-flash",
    messages=[{"role": "user", "content": "Summarise this RFC."}],
    reasoning_effort="high",
    extra_body={"thinking": {"type": "enabled"}},
)
print(resp.choices[0].message.content)

The API is stateless — you resend the conversation history with every request. The web chat and mobile app maintain session history; the API does not.

Moonshot Kimi K3 and K2.6 — the agentic coding specialist

Moonshot’s focus is long-running coding agents. Its current flagship, Kimi K3 (published on Hugging Face in June 2026), is described on its model card as a 2.8T-parameter open-weight, natively multimodal model that activates 16 of 896 experts, with a 1-million-token context window. Its weights are released under Moonshot’s own Kimi K3 License rather than an MIT variant.

The earlier Kimi K2.6 (published on Hugging Face in April 2026) remains available under a Modified MIT License. Its card lists 1 trillion total parameters with 32 billion active, 384 experts with 8 selected per token, a 256K-token context window, and agent swarms of up to 300 sub-agents. Benchmarks Moonshot reported for K2.6 against the frontier models of that time:

• SWE-Bench Pro: 58.6, vs 57.7 for GPT-5.4 (xhigh), 53.4 for Claude Opus 4.6 (max effort), and 54.2 for Gemini 3.1 Pro (thinking high).
• SWE-Bench Verified: 80.2.
• Terminal-Bench 2.0 with the Terminus-2 agent framework: 66.7, vs 65.4 for GPT-5.4 and Claude Opus 4.6, and 68.5 for Gemini 3.1 Pro.
• Humanity’s Last Exam (HLE-Full) with tools: 54.0, the highest in Moonshot’s comparison (GPT-5.4 52.1, Claude Opus 4.6 53.0, Gemini 3.1 Pro 51.4).

As of 23 October 2026, Moonshot’s API pricing page lists kimi-k3 at $3.00 input / $15.00 output per 1M tokens (cached input $0.30, with cache writes billed separately) and kimi-k2.6 at $0.95 cache-miss input / $4.00 output ($0.16 cache hit). Both sets of weights are on Hugging Face, so self-hosting is on the table for teams with the GPUs.

Alibaba Qwen — open and hosted tiers

Alibaba’s Qwen lineup spans small open models and hosted frontier tiers. On the hosted side, Alibaba Cloud Model Studio lists qwen3.8-max at $2 input / $6 output per 1M tokens (International deployment, 0–1M-token tier) as of 23 October 2026, alongside older Max snapshots such as qwen3.7-max. These Max models are offered as a hosted API on Model Studio.

On the open side, Qwen3.6-35B-A3B (published on Hugging Face in April 2026) is published under the Apache 2.0 license; its model card lists 35B total and 3B activated parameters and a native context of 262,144 tokens, extensible to about 1M with RoPE scaling. If permissive licensing matters to you, Qwen’s Apache 2.0 models are among the most commercially flexible options in this guide. If you want the Max tier, you’re on a hosted API.

Zhipu GLM-5 / GLM-5.1 — reasoning and knowledge

Zhipu (branding its platform as Z.ai) published GLM-5 and its successor GLM-5.1 on Hugging Face in February and April 2026, both under MIT. According to the GLM-5 model card, it scales to 744B parameters with 40B active (from GLM-4.5’s 355B / 32B) and uses DeepSeek Sparse Attention to cut deployment cost while keeping long-context capacity. The card also documents deployment on Huawei Ascend NPUs.

Zhipu’s own evaluation in the GLM-5 card reports 92.7 on AIME 2026 I, 86.0 on GPQA-Diamond, and 50.4 on Humanity’s Last Exam with tools; the GLM-5.1 card reports 58.4 on SWE-Bench Pro. Treat these as vendor-reported. The newest release, GLM-5.3 (published on Hugging Face in August 2026), uses the same base model as GLM-5.2 with further post-training aimed at coding and long-horizon tasks, and ships under its own GLM-5.3 licence rather than MIT. Z.ai lists GLM-5.3 at $1.40 input / $4.40 output and GLM-5 at $1.00 / $3.20 per 1M tokens (23 October 2026).

MiniMax M2.5 and M3 — low-cost agent models

MiniMax leans into coding and agents. MiniMax-M2.5 (published on Hugging Face in February 2026; Modified MIT licence) reported 80.2% on SWE-Bench Verified on its model card. It is no longer listed on MiniMax’s own pricing page as of 23 October 2026, which now lists the newer models.

MiniMax-M3 (published on Hugging Face in June 2026) is a native multimodal model with a 1M-token context, about 428B parameters and about 23B activated, per its model card. On MiniMax’s pricing page, the Standard tier for requests of up to 512K input tokens is $0.30 per million input tokens and $1.20 per million output tokens (cache reads $0.06) after a “permanent 50% off” on the $0.60 / $2.40 list price; longer prompts and the Priority tier (1.5× Standard) cost more. Note the licence change: M3 ships under the MiniMax Community licence rather than M2.5’s Modified MIT, so read the licence on the version you actually download before commercial use.

A worked cost example: 1M agent calls

To make the price differences concrete, here’s a standard workload — 1,000,000 API calls with a 2,000-token cached system prompt, a 200-token user message (uncached miss), and a 300-token response — costed against deepseek-flash:

                       PEAK                                 OFF-PEAK
Cached input  : 2,000,000,000 × $0.006/M = $12.00     × $0.003/M = $6.00
Uncached input:   200,000,000 × $0.30/M  = $60.00     × $0.15/M  = $30.00
Output        :   300,000,000 × $1.20/M  = $360.00    × $0.60/M  = $180.00
                                           -------                 -------
Total                                      $432.00                 $216.00

The same workload on deepseek-v4-pro costs $1,540.00 entirely at peak ($88.00 + $264.00 + $1,188.00) and $770.00 entirely off-peak ($44.00 + $132.00 + $594.00). At the competitor list prices above, with each vendor’s cached-input rate applied to the system prompt, it comes to $540.00 on MiniMax-M3 Standard ($120.00 + $60.00 + $360.00, excluding any cache-write charges) and $1,560.00 on GLM-5 ($400.00 + $200.00 + $960.00). Caching rules and tokenizers differ between vendors, so treat cross-vendor totals as rough. Cache discipline and tier choice matter more than benchmark deltas for most budgets. See our DeepSeek pricing calculator to reproduce this against your own traffic shape.

Picking the right model

• Low-cost chat: deepseek-flash ($0.30 / $1.20 at peak, $0.15 / $0.60 off-peak), or the small Flash tiers from Zhipu and MiniMax at similar rates.
• Long-running coding agents: Kimi K3 or K2.6, which Moonshot’s model cards position for long-horizon coding; K2.6’s card reports strong Moonshot-reported SWE-Bench Pro and HLE-with-tools numbers.
• Reasoning and knowledge work with MIT weights: GLM-5 or GLM-5.1, based on Zhipu-reported results.
• Most permissive open licence: Qwen3.6-35B-A3B (Apache 2.0).
• Hosted frontier tier from Alibaba: qwen3.8-max on Model Studio.
• Multimodal agent model with 1M context at a low Standard rate: MiniMax-M3 ($0.30 / $1.20 for prompts up to 512K tokens).

Caveats every reader should keep in mind

Three honest warnings before you commit:

1. Data residency. DeepSeek’s hosted service processes conversations on servers subject to Chinese law. Check each vendor’s privacy policy and available data regions before sending regulated data to any of these APIs.
2. Regulatory status changes. Italy’s Garante ordered the DeepSeek app blocked in January 2025; several US states restrict DeepSeek on government devices. Check current status for your jurisdiction before procurement.
3. Licences vary by release. “Open-weight” is not “MIT”. DeepSeek’s current weights and GLM-5 / 5.1 are MIT; Kimi K2.6 and MiniMax-M2.5 use Modified MIT; Kimi K3, GLM-5.3 and MiniMax-M3 ship under their own licences; Qwen3.6-35B-A3B is Apache 2.0, while the Max tiers are hosted. Older DeepSeek models such as Coder-V2 split MIT code from a separate DeepSeek Model License on weights. Read the repo before you redistribute.

For deeper one-on-one comparisons, see DeepSeek vs Qwen, DeepSeek vs Kimi and DeepSeek vs GLM. The full DeepSeek alternatives hub also covers non-Chinese options like Llama and Mistral.