AIOAI task ranking · 2026-08-21

Best Asian AI Models for Coding in 2026

This ranking focuses on Asian AI models that can help generate, explain and debug code. It also considers whether developers can use the model through chat, APIs or open weights.

Ranked for coding

Coding scores emphasize code generation, debugging and reasoning fit, then practical developer access. They are editorial discovery scores rather than controlled software-engineering benchmarks.

01

DeepSeek

Reasoning and coding models with a strong free experience and developer access.

DeepSeek · China96Estimate
02

Qwen

A broad model family for reasoning, coding, multimodal work and multilingual tasks.

Alibaba Cloud · China94Estimate
03

GLM

A multilingual model family spanning chat, reasoning, coding and visual understanding.

Z.ai · Zhipu AI · China93Estimate
04

A.X K2

An open-weight Korean foundation model focused on reasoning, coding, multilingual work and agentic use.

SK Telecom · South Korea92Estimate
05

Kimi

A research and long-document assistant with reasoning and coding capabilities.

Moonshot AI · China90Estimate
06

ERNIE

Baidu's multimodal foundation-model family for text, reasoning, vision, audio and enterprise applications.

Baidu · China89Estimate
07

Hunyuan

Tencent's model family for text, code, images, video and enterprise applications.

Tencent · China88Estimate
08

Step

A multimodal model family covering reasoning, coding, image, video and speech generation.

StepFun · China88Estimate
09

EXAONE

LG's bilingual and multimodal model family for professional and research use.

LG AI Research · South Korea88Estimate
10

Kanana

Kakao's Korean-first language-model family with open releases for chat, reasoning and developer use.

Kakao · South Korea88Estimate
11

Solar

A compact language model family built for efficient enterprise and document workflows.

Upstage · South Korea87Estimate
12

Yi

A bilingual model family with long-context, reasoning and open-weight releases for developers.

01.AI · China86Estimate
Scores are editorial discovery estimates, not lab benchmarks, prices or user-review aggregates. See the complete methodology.

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