Best Chinese LLM API for JSON Extraction (2026)

For high-volume JSON extraction, Qwen3-235B-A22B is the pick: it returned valid, exact-key JSON in 2.2 seconds for $0.000018 on our test, the cheapest of six and fastest of the Chinese models, with DeepSeek V4-Flash the close-second value option. Every model we tried produced correct structured output, so the real choice is speed and cost, not accuracy.

We answer that with one set of first-hand runs: an identical JSON-extraction prompt sent to five Chinese model families plus a GPT-4o baseline, billed and timed the same way on the same day. All six calls were routed through OpenRouter this run because api.deepseek.com was unreachable from our environment, so every per-token cost below carries a native-reverify caveat we flag again at the end.

Best Chinese LLM for JSON extraction (verified 2026-07)

Here is the question most buyers actually have: for one realistic extraction turn, which model returns clean JSON, how fast, and at what cost? We sent each model the same prompt asking it to extract a name, role, and company into a flat JSON object, then checked the output for parseability and exact key names. The correctness result is blunt: all six passed. What separates them is the latency and cost columns.

Two things stand out. GPT-4o was actually the quickest single call at 1.89 seconds, but it billed roughly 23 times Qwen3-235B's cost for the same three-key object, which is why the neutral baseline is a reference point here and not a recommendation. And GLM-5, a reasoning model, was correct but roughly 40 times pricier and more than three times slower than Qwen on this trivial task, for reasons we unpack below.

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

The numbers above are not scraped from a rate card. We ran the extraction prompt once per model at temperature 0 with max_tokens=6000 on 2026-07-10, and recorded the billed cost and wall-clock latency each call returned. Because api.deepseek.com was unreachable, DeepSeek V4-Flash was served as deepseek/deepseek-chat through OpenRouter alongside the others, so its rate here is a routed figure, not its native price.

On that run, Qwen3-235B-A22B consumed 50 input and 24 output tokens, returned bare valid JSON with the exact keys name, role, and company, and billed $0.000018 in 2.2 seconds, the cheapest and fastest extraction in the set. DeepSeek V4-Flash used 47 input and 34 output tokens for $0.0000366 in 2.88 seconds, wrapping its object in a markdown code fence that still parsed cleanly. GLM-5, by contrast, spent 890 reasoning tokens before emitting a 262-token completion, billing $0.00071275 across 7.5 seconds for the identical three-key answer.

That GLM-5 gap is the single most useful finding for an extraction workload. It was not wrong. It reached the same correct object every other model did. It simply thought its way there, and on a task that Qwen finished in about two seconds, that overhead is pure cost with no accuracy payoff.

Why every model passed, and what "passed" means

A JSON-extraction pass here means the model followed this one instruction on this one prompt: it emitted text that parsed as JSON and carried exactly the three requested keys, no more and no fewer. It is not proof the model never drifts. This was a single-run, one-prompt-per-task, temperature-0 snapshot, so treat it as a strong signal for a simple flat schema, not a stress test of deeply nested objects or adversarial inputs.

With that scope stated plainly, the correctness result held across all six models. DeepSeek V4-Flash and GPT-4o pretty-printed their objects with indentation; Qwen3-235B, Kimi K2, and MiniMax-M2.5 returned compact single-line JSON; GLM-5 returned compact JSON after a hidden reasoning pass. All six parsed and all six carried the exact keys, which is why we lead with speed and cost rather than any accuracy ranking.

The reasoning-model fit question (GLM-5)

A reasoning model is a large language model that spends extra internal tokens working through a problem before answering, which suits hard multi-step logic but adds latency and cost to simple tasks. GLM-5, built by Zhipu AI, is one, and its behavior on this extraction prompt is the textbook illustration.

According to Z.ai, the GLM series is positioned for advanced reasoning and agentic workloads, and that design goal is exactly what we saw: 890 reasoning tokens spent to produce a three-field object. For a workload where you are extracting structure from thousands of documents an hour, that overhead compounds fast. For a workload where the extraction itself requires multi-step inference over messy source text, the same reasoning depth may be worth paying for. It is a fit question, not a quality knock, because GLM-5 got the answer right.

How the vendors describe their structured-output support

Beyond our run, the providers document explicit structured-output modes worth checking before you commit to a schema. According to Alibaba Cloud, the Qwen model family supports structured JSON output through its API, which lines up with the clean, exact-key object Qwen3-235B returned for us. According to DeepSeek API Docs, DeepSeek exposes a JSON output mode that constrains responses to valid JSON, consistent with the parseable object V4-Flash produced even inside a code fence.

The GPT-4o baseline also passed cleanly, which tracks with OpenAI documenting both a JSON mode and stricter structured outputs for the model. Kimi K2 from Moonshot AI and MiniMax-M2.5 both returned compact valid JSON on our prompt as well, so at the flat-schema level the whole field is capable; the differentiator is what each call costs and how long it takes.

Verdict by extraction workload

The honest answer is that there is no single best Chinese model for JSON extraction, only a best one for your volume. For high-throughput pipelines that fire many extraction calls, choose Qwen3-235B-A22B: it was the fastest and cheapest here and its cost advantage compounds across millions of calls. DeepSeek V4-Flash is the close-second value option at roughly double Qwen's cost but still a fraction of a cent, and its native endpoint historically ran faster and cheaper than any routed figure, so reverify it on a direct key. Reach for GLM-5 only when your extraction genuinely needs multi-step reasoning over the source, since on simple schemas its reasoning overhead is cost without benefit. GPT-4o works but prices itself out of high-volume extraction against these Chinese options.

FAQ

Which Chinese LLM is best for JSON extraction? On our 2026-07-10 run, Qwen3-235B-A22B was best for high-volume extraction: valid exact-key JSON in 2.2 seconds for $0.000018, the cheapest of six and fastest of the Chinese models. DeepSeek V4-Flash was the close-second value pick at $0.0000366 per call.

Do Chinese LLMs support a strict JSON mode? Yes. Alibaba Cloud documents structured JSON output for Qwen, DeepSeek documents a JSON output mode, and OpenAI documents JSON mode plus structured outputs for the GPT-4o baseline. Every model we tested returned parseable JSON, though enforcement mechanics differ per vendor and should be checked in their docs.

Did any model get the JSON schema wrong? No. All six models returned valid JSON with exactly the three requested keys on our prompt. That said, we tested key-exactness on one simple flat object at temperature 0, a single-run snapshot, not deeply nested schemas or adversarial inputs, so treat it as a strong signal rather than a guarantee.

Why was GLM-5 so much slower and pricier for JSON? GLM-5 is a reasoning model. It spent 890 reasoning tokens working through a trivial three-field extraction before answering, billing $0.00071275 in 7.5 seconds versus Qwen's $0.000018 in 2.2 seconds. It was correct, just built for harder multi-step problems than flat extraction.

Were these costs measured on native vendor endpoints? No. All six calls, including DeepSeek, were routed through OpenRouter this run because api.deepseek.com was unreachable, so every cost is a routed figure needing native-key reverification. DeepSeek's prior native rate of $0.14 input and $0.28 output per million tokens is cited separately as earlier first-hand data.

This is part of the best Chinese LLM by use case hub, which compares DeepSeek, Qwen, GLM, Kimi, and MiniMax across real tasks. For full rate cards, see the Qwen API pricing hub and the GLM 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.