Best Chinese LLM API (2026)

For most teams the best Chinese LLM API in 2026 is DeepSeek V4-Flash at $0.14 input and $0.28 output per million tokens, because it was both the cheapest credible general tier and the fastest model we measured, answering small calls in roughly 0.7 seconds. That rare both-wins result is the headline, but it is not the whole story. Qwen, GLM, Kimi K2, and MiniMax each win a different job, and the right pick depends on whether you optimize for price, latency, coding, long context, or US compliance. This hub compares five families on verified price and first-hand latency, then hands you a decision matrix so you can pick by job rather than by brand.

A note on evidence before the numbers. DeepSeek figures come from its official endpoint at api.deepseek.com. Every other family here was measured through OpenRouter, which routes to third-party hosts, so those prices and latencies are disclosed as measured via OpenRouter and carry needs_native_reverify until we re-test on each vendor's native key. We flag this everywhere it matters.

The multi-model comparison (verified 2026-06)

Here is the core rate card plus the latency we observed on small general calls. Prices are per million tokens in USD. Read the latency column as a rough tier, not a guarantee, since OpenRouter routing varies by host and time of day.

Model	Input $/1M	Output $/1M	Measured latency	Pricing source
DeepSeek V4-Flash	0.14	0.28	~0.7-2.3s	official api.deepseek.com
DeepSeek V4-Pro	0.435	0.87	2.64s	official
Qwen3-Coder	0.22	1.80	1.50s	via OpenRouter
Qwen-Plus	0.26	0.78	2.47s	via OpenRouter
Qwen3-Max	0.78	3.90	2.28-3.22s	via OpenRouter
GLM-4.6	0.43	1.74	3.78-8.58s	via OpenRouter
GLM-4.5-Air	0.13	0.85	8.07s	via OpenRouter
Kimi K2 (base)	0.57	2.30	4.55-7.03s	via OpenRouter
MiniMax-M2	0.255	1.00	2.95-5.08s	via OpenRouter

The pattern most buyers miss is that price rank and speed rank do not line up. DeepSeek V4-Flash leads both, but after it the cheapest models are not the fastest. GLM-4.5-Air ties DeepSeek on input price at $0.13 yet took eight seconds on the same kind of call, while Qwen3-Coder cost more per token but came back in 1.5 seconds. That gap is exactly why a single ranking is misleading and a pick-by-job matrix is not.

First-hand latency leaderboard (measured 2026-06-26)

Documentation lists prices. It does not tell you how a model feels under a real request, so we called each one and timed it. The full run is in our cost calculator. DeepSeek ran on its official endpoint; every other family was measured via OpenRouter, and we restate only numbers that exist in that artifact.

DeepSeek V4-Flash was the standout. On a cold cache call it returned in 0.733 seconds and on its alias calls in 1.58 to 1.81 seconds, consistently the fastest in the set. What surprised us was the "Flash" tiers from other vendors. GLM-4.7-Flash took 26.2 seconds to produce 200 output tokens, and Qwen3.5-Flash took 17.85 seconds because it generated 2,831 tokens off a two-sentence prompt. We re-read the runs to be sure: the name "Flash" predicts nothing about wall-clock speed here.

Among the mid-tier flagships, Qwen3-Coder was the quickest non-DeepSeek call at 1.497 seconds, MiniMax-M2 landed at 2.95 to 5.08 seconds across general and coding prompts, Kimi K2 base ran 4.55 to 7.03 seconds, and GLM-4.6 spread from 3.78 to 8.58 seconds depending on the prompt. The reasoning tiers were the slowest by design: Kimi K2-Thinking took 18.51 seconds and emitted 553 tokens for a prompt that needed a sentence.

That last point is a cost trap, not just a speed one. According to the MiniMax platform docs, reasoning models bill on completion tokens like any other call, so a thinking tier that spends 553 or 2,831 tokens on a short prompt costs many times its headline rate per useful answer. The practical upshot is that for high-volume short calls, a fast non-reasoning tier usually beats a cheap-looking reasoning tier on real spend.

Pick by job: the decision matrix

No single model wins every job, so match the model to the constraint that actually binds your workload. This synthesis draws across all nine micros below and is the part you cannot get from any one vendor's page.

Your priority	Pick	Why
Cheapest credible general tier	DeepSeek V4-Flash ($0.14/$0.28)	Lowest combined rate that also runs fast and on an official endpoint
Absolute lowest input rate	GLM-4.7-Flash ($0.06, free on z.ai) or Qwen3.5-Flash ($0.065)	Cheaper per input token, but both ran slow in our test
Fastest response	DeepSeek V4-Flash (~0.7s)	Sub-second cold call, fastest measured
Coding	Qwen3-Coder (1.5s) or Kimi K2	Fast coder tier; Kimi for agentic coding context
Long context	Kimi K2 / MiniMax-M2	Built for long-document and agent workloads
US-compliant procurement	Depends on hosting, see below	Data-residency and entity questions dominate price

According to the DeepSeek API pricing docs, the V4-Flash rate is $0.14 input and $0.28 output per million, which anchors the cheapest-credible row. Where you need a genuinely free tier rather than a low rate, GLM is the only family in our set that offers one. According to the z.ai platform, the GLM Flash tiers are available at no cost on the direct endpoint, a structural difference from DeepSeek's new-account credit or Qwen's time-limited free quota.

US availability is the row that trips up procurement most. It is rarely a price question and almost always a hosting and data-residency question, which is why we gave it a dedicated micro rather than a table cell. Route a Chinese model through a US-hosted gateway and the compliance posture changes even though the weights do not.

Where OpenRouter undercuts the official endpoint

One reusable finding from building this cluster: for several families the OpenRouter-routed price sat below the vendor's official international rate. For Qwen3-Max we measured roughly $0.78 input via routing, well under the official Qwen list price. According to Alibaba Cloud, the Qwen Max international rate sits near $2.40 input per million, and our GLM-4.6 measurement of about $0.43 routed also undercut z.ai's $0.60 official rate. For Kimi K2 the two were near parity. This is why needs_native_reverify matters: the routed number is real and we measured it, but it is not the price you pay on a native vendor key, and we will not present it as such.

Definitions worth pinning down

A Chinese LLM API is a hosted inference service that exposes a Chinese-developed foundation model (DeepSeek, Qwen, GLM, Kimi, MiniMax) over an HTTP endpoint, usually with an OpenAI-compatible schema so existing client code works with a base-URL swap. A reasoning tier is a model variant that emits intermediate "thinking" tokens before its answer, which improves hard-task accuracy but inflates completion-token cost and latency, as our 18-second Kimi K2-Thinking call showed.

Explore the full cluster

Each micro below answers one buyer question in depth. The hub gives you the verdict; the micros give you the workings.

Micro article	Question it answers
Cheapest Chinese LLM API	Which family wins on raw price
Chinese LLM API pricing comparison	Full rate-card table across vendors
Best Chinese LLM for coding	Coder-tier picks and benchmarks
Chinese LLM vs GPT-4o cost	Side-by-side spend against GPT-4o
Chinese LLM vs Claude cost	Side-by-side spend against Claude
Fastest Chinese LLM API	The full latency leaderboard
Chinese LLM API free tiers	Who actually offers free usage
Chinese LLM API US availability	Hosting and compliance for US teams
Chinese LLM API cost calculator	Estimate your own monthly bill

For per-family deep dives, see the model pricing hubs for DeepSeek, Qwen, GLM, Kimi, and MiniMax.

FAQ

What is the best Chinese LLM API overall in 2026? For general workloads, DeepSeek V4-Flash, because it was both the cheapest credible tier at $0.14/$0.28 per million and the fastest model we measured at roughly 0.7 seconds on a small call. Flagship-capability or long-context jobs may favor Qwen, Kimi, or MiniMax instead.

Which Chinese LLM API is cheapest? On raw input price, GLM-4.7-Flash ($0.06, free on z.ai) and Qwen3.5-Flash ($0.065) undercut everyone, but both ran slow in our latency test. DeepSeek V4-Flash at $0.14/$0.28 is the cheapest tier that is also fast and runs on an official endpoint.

Which is the fastest Chinese LLM API? DeepSeek V4-Flash, measured at 0.733 seconds on a cold call via its official endpoint. Counterintuitively, several models named "Flash" were the slowest we tested: GLM-4.7-Flash took 26 seconds and Qwen3.5-Flash took 17.8 seconds via OpenRouter.

Are these prices and latencies from official APIs? Only DeepSeek. Its numbers come from api.deepseek.com. Qwen, GLM, Kimi, and MiniMax were measured via OpenRouter and are disclosed as such, with needs_native_reverify set until we re-test on each vendor's native key.

Which Chinese LLM is best for coding? Qwen3-Coder was our fastest coder-tier call at 1.5 seconds, and Kimi K2 targets agentic coding with long context. See the best-Chinese-LLM-for-coding micro for benchmarks and per-task picks.

Can US companies use Chinese LLM APIs? Yes, but availability is a hosting and data-residency question more than a model question. Routing through a US-hosted gateway changes the compliance posture. The US-availability micro covers entity, region, and procurement specifics.

Why do reasoning tiers cost more than their headline rate suggests? They emit extra "thinking" tokens before the answer. Kimi K2-Thinking spent 553 completion tokens and Qwen3.5-Flash spent 2,831 on short prompts in our runs, so the real per-answer cost is far above the per-token rate.

Author: Kevin Fan, Customer Success Manager at China LLM Directory, specializing in Chinese LLM ecosystem pricing and benchmarking. Last verified: 2026-06-26.

For most teams the best Chinese LLM API in 2026 is DeepSeek V4-Flash at $0.14 input and $0.28 output per million tokens, because it was both the cheapest…

Written by Fan Chuanyu