Best Chinese LLM API for RAG (2026): Tested

For high-QPS RAG where the generation step must be cheap and fast, Qwen3-235B-A22B and DeepSeek V4-Flash are the picks: all six models we tested refused correctly when the answer was absent, so latency and cost, not faithfulness, separated them on this prompt. The choice is about how much you pay per answer and how long a user waits, because on grounded refusal they tied.

Retrieval-augmented generation (RAG) is an architecture that grounds a model's answer in retrieved passages rather than its parametric memory, so the generator's most important behavior is refusing to invent facts the context does not contain. We tested exactly that behavior first-hand and report what we measured, not a rate card.

Best Chinese LLM for RAG (verified 2026-07)

The single test we ran here is narrow and deliberate. We handed each model a short passage and asked a question the passage did not answer, then checked whether it said the answer was absent or fabricated a number. This is one grounded-refusal prompt, run once at temperature 0. It is a snapshot of faithfulness on this instruction, not proof that any model never hallucinates.

Every model got it right. That parity is the honest headline: none of the six invented a figure, so the thing that actually separates them for a RAG generation step is how fast and how cheaply each returned that refusal. The table below uses the grounding-refusal run for each model, pulled straight from our evidence artifact.

Read down the latency and cost columns and the recommendation writes itself. Qwen3-235B-A22B was both the fastest and the cheapest refusal, DeepSeek V4-Flash sat close behind on cost, and the two reasoning-heavy runs cost roughly ten to forty times more for the identical two-word answer.

What we measured first-hand (OpenRouter-routed, disclosed)

We ran this benchmark on 2026-07-10: six models, one identical zero-temperature grounding prompt each, billed and timed the same day. All six were routed through OpenRouter this run because api.deepseek.com was unreachable from the run environment, so every per-token figure here carries a native-reverify caveat and needs_native_reverify is set on this page.

On the grounding-refusal task, Qwen3-235B-A22B returned "NOT IN CONTEXT" in 1.26 seconds for a billed $0.0000164 across 94 input and 4 output tokens, the fastest and cheapest refusal in the set. Kimi K2 followed at 1.52 seconds and $0.0000571, and DeepSeek V4-Flash refused in 2.44 seconds for $0.0000385. The GPT-4o baseline matched their faithfulness but at $0.0002575, about sixteen times Qwen's cost for the same outcome.

The two slow runs are the interesting ones. GLM-5 and MiniMax-M2.5 both refused correctly, but they spent reasoning tokens deciding to do so: GLM-5 emitted 240 completion tokens and MiniMax-M2.5 emitted 167, against four or five for the instruct models. That is why GLM-5's single refusal billed $0.0006890 and took 5.51 seconds while Qwen's cost a rounding error.

Why reasoning overhead matters for a RAG generator

A RAG generator is called on every user turn, often many times per second at scale, and the grounded refusal is one of its cheapest jobs. Paying a reasoning model to deliberate over "is this answer in the passage" is like hiring a barrister to read a parking ticket: you get the right verdict and an invoice that does not fit the task.

GLM-5 is a reasoning model built by Zhipu AI, and it was correct on this prompt and on all six tasks in the wider benchmark. The neutral read is a fit question, not a quality knock. Reasoning models earn their overhead on hard multi-step problems where the extra tokens change the answer, not on high-volume simple refusals where the answer is a fixed two-word string. For a high-QPS RAG endpoint, that overhead is cost you do not need to buy.

What this test does not tell you

Grounded refusal is one slice of RAG quality. This prompt checks whether a model declines to answer when the context is silent, which is the failure mode that produces the most damaging hallucinations in a document assistant. It does not measure retrieval recall, multi-passage synthesis, citation formatting, or behavior on long contexts, and one clean pass at temperature 0 is a single sample, not a guarantee.

According to DeepSeek API Docs, the DeepSeek chat model bills input at $0.14 and output at $0.28 per million tokens on its official endpoint, so a native RAG deployment would likely land near the low cost we observed here once the routing caveat is removed. Treat our routed figure as directional until you reverify on a native key.

Vendor positioning explains the latency spread we saw. According to Alibaba Cloud, the Qwen3 line is positioned for high-throughput API use, which is consistent with the sub-1.3-second refusal we timed. MiniMax's slower turn is a design choice, not a defect. According to MiniMax Platform, MiniMax-M2.5 targets agentic and reasoning workloads, so its extra latency on this simple refusal reflects that focus rather than any faithfulness gap. The Western reference behaves as expected too. According to OpenAI, GPT-4o is a general-purpose model included here only as a neutral baseline, and it refused correctly at several times the cost of the cheapest Chinese options.

Our take for a RAG generation step

If your RAG system fires many short generation calls and the binding constraint is cost per answer and user-visible latency, choose Qwen3-235B-A22B first and DeepSeek V4-Flash as a near-equal alternative, then reverify DeepSeek on its native endpoint before you commit. Kimi K2 is a fine third option. Keep GLM-5 or MiniMax-M2.5 for the parts of your pipeline that involve genuine multi-step reasoning, and do not pay their overhead on the grounded-refusal path where every model tied.

FAQ

Does this mean these models never hallucinate in RAG? No. We tested one grounded-refusal prompt, once, at temperature 0. All six correctly said the answer was absent, but that is a single sample on one instruction. It shows they can refuse faithfully, not that they always will across every passage, question, and context length.

Which Chinese LLM is cheapest for the RAG generation step? On our 2026-07-10 run, Qwen3-235B-A22B was cheapest, billing $0.0000164 for its grounded refusal via OpenRouter, with DeepSeek V4-Flash close behind at $0.0000385. Both are routed figures, so reverify on native endpoints before treating them as vendor-direct rates.

Why were GLM-5 and MiniMax-M2.5 so much more expensive here? Reasoning tokens. Both refused correctly but generated 240 and 167 completion tokens deliberating over a two-word answer, versus four or five for the instruct models. GLM-5's single refusal billed $0.0006890, roughly forty times Qwen's, for the identical outcome.

What context window do I need for RAG? That depends on how many retrieved passages you stuff into each prompt, not on this test, which used a short passage. Check each vendor's current maximum context length before choosing, since a RAG generator's window caps how much retrieved evidence you can ground on per call.

Are these numbers from native vendor endpoints? No. Every model in this run was routed through OpenRouter because api.deepseek.com was unreachable that day, so needs_native_reverify is set. A prior native DeepSeek measurement of roughly $0.14/$0.28 per million tokens at about 0.7 seconds is cited separately as earlier first-hand data.

This page is part of the best Chinese LLM API by use case hub, which compares DeepSeek, Qwen, GLM, Kimi, MiniMax, and a GPT-4o baseline across six real tasks. For Qwen's full rate card, see the Qwen API pricing hub.

Author: Kevin Fan, Customer Success Manager at China LLM Directory, specializing in Chinese LLM ecosystem pricing and model benchmarking. Last verified: 2026-07-10.