Chinese vs global AI models
The gap is closing — but they're not on the same track
Chinese models now appear in the intelligence top ten and dominate the open-weights front ranks — but "close" isn't "equal": long-context stability, complex agent tasks and tooling ecosystems still lag. Assign models per task instead of picking one winner.
Last updated
Intelligence top 10: 1 Chinese model
Kimi K3.1 Pro (46.5)
Artificial AnalysisVersion 2026-11-07License Proprietary — limited factual citation (intelligence index totals + top-30 entries only) per AA Terms of Use; no full-table reproduction, no chart/copy reuse. Re-review quarterly; if AA requires licensing, buy Commercial access or drop this field.Artificial Analysis
Compared by task
- Hard reasoning / mathLean global
Global flagships still lead; the strongest Chinese models now compete in the same heat — a competent stand-in when budget-bound.
- CodingNear parity
Open-weights Chinese models (DeepSeek/GLM/Kimi) reach the SWE-bench top twenty — fine for daily work; complex multi-repo tasks still favor global flagships.
- Chinese writingLean Chinese
Chinese models win on voice and local knowledge; prefer them for official and marketing copy. Pure literary writing depends on voice-fit.
- Long-horizon agent tasksLean global
Global flagships keep an edge in long-horizon stability; Chinese labs are closing fast — re-check quarterly.
- Data compliance / on-premChinese, hands down
Chinese open weights + local deployment is the only friction-free compliant path, at controllable cost.
LMArenaVersion 2026-11-06License CC-BY-4.0LMArenaMay require overseas network
OpenCompass 司南Version 2026-11-07License 引用式（文字结论 + 外链，未整表复制）OpenCompass 司南
SuperCLUEVersion 2026-11-07License 引用式（月度报告结论，标注出处）SuperCLUE
CLiB / ReLEVersion 2026-11-07License 引用式（GitHub 公开榜单）CLiB / ReLE
A few terms first
FAQ
How big is the gap between Chinese models and GPT/Claude now?
Depends on the task: on the intelligence index the flagship-vs-flagship gap has narrowed to single-digit percentage points, though global labs still hold most top-ten seats. In coding, open-weights Chinese models sit at the edge of the first tier. The clearest remaining gaps are long-horizon agents and very long context. Follow this column's changelog monthly rather than memorizing conclusions.
How do I choose between open and closed models?
Have deployment capacity, sensitive data, or enough volume that self-hosting amortizes → open weights (fine-tunable, controllable, you pay in hardware). Want the highest ceiling with zero ops → closed flagship APIs. The open frontier rises every year; in 2026 open-weights models crack top-ten spots on several public benchmarks.
Are Chinese models only cheaper?
No. Chinese voice and local knowledge (law, government, internet slang), domestic compliance and filing, CNY billing with local payment, and integration with WeChat/DingTalk/Feishu are real advantages. Price is just one of them.
Is there a simple rule for same-budget choices?
Yes: at a fixed budget, prefer Chinese flagships for Chinese tasks (stronger per yuan); for English/code tasks consult the specific board — at the same price point Chinese value tiers are already competitive on SWE-bench and the intelligence index. Pilot on your own workload before switching over.
About the data
This column cites rather than reproduces: we quote only key scores and findings, each labeled with its source, version date and a link to the original — never a full-table copy of anyone's leaderboard.
Automated sources (LMArena / SWE-bench / LiveBench) sync weekly; the Artificial Analysis intelligence index and the pricing table are manually verified, with check dates noted in the changelog.
Benchmarks are not your experience. Each board measures something different and scores drift between releases — test against your own task before committing.
Artificial AnalysisVersion 2026-11-07License Proprietary — limited factual citation (intelligence index totals + top-30 entries only) per AA Terms of Use; no full-table reproduction, no chart/copy reuse. Re-review quarterly; if AA requires licensing, buy Commercial access or drop this field.Artificial Analysis
LMArenaVersion 2026-11-06License CC-BY-4.0LMArenaMay require overseas network
OpenCompass 司南Version 2026-11-07License 引用式（文字结论 + 外链，未整表复制）OpenCompass 司南
SuperCLUEVersion 2026-11-07License 引用式（月度报告结论，标注出处）SuperCLUE
CLiB / ReLEVersion 2026-11-07License 引用式（GitHub 公开榜单）CLiB / ReLE
Written by the Flowerclaw editorial team · Reviewed by the Flowerclaw editorial team