DeepSeek vs GLM vs Kimi vs ERNIE: 2026 Developer Comparison

Chinese AI models are evolving fast. In 2026, you have four major contenders: DeepSeek V4, GLM-4 (Zhipu), Kimi VL (Moonshot), and ERNIE 4.5 (Baidu). Each claims to match or beat GPT-4-class models — but which one actually delivers for real-world development?

We ran them through coding challenges, reasoning puzzles, and multilingual tests. Here's what we found.

TL;DR — The Quick Verdict

Model Overview

Pricing Comparison (per 1M tokens, USD equivalent)

Note: GLM-4-Flash is free on AIWave with reasonable rate limits. Yes, really.

Coding Benchmarks

We tested each model with a standard coding benchmark: implement a concurrent rate limiter in Python with Redis backend, write a React component with virtual scrolling, and fix a subtle SQL injection vulnerability in Node.js.

DeepSeek wins coding hands down. Its SQL injection fix even suggested parameterized queries without being asked. GLM-4 is solid but sometimes over-engineers solutions. Kimi and ERNIE are adequate but not exceptional for hardcore engineering work.

Reasoning & Logic

We used the GPQA (graduate-level) and MATH benchmarks plus a custom multi-step reasoning puzzle ("If Alice is 3 years older than Bob, and in 5 years... but with three more constraints").

DeepSeek V4 and GLM-4 tied for first place on reasoning. Both handled multi-step deduction without hallucinating intermediate steps. Kimi VL was close behind. ERNIE 4.5 struggled with puzzles requiring counterfactual reasoning.

Multilingual Performance

Tested in English, Chinese, Japanese, Korean, and Arabic:

• GLM-4 — strongest multilingual model. Natural in all 5 languages. Handles code-switching flawlessly.
• ERNIE 4.5 — excellent Chinese, good English, mediocre others.
• DeepSeek V4 — excellent English and Chinese, acceptable others.
• Kimi VL — strong English/Chinese, weak in Arabic.

Vision & Document Analysis

Tested on chart reading, handwritten text OCR, and multi-page PDF summarization:

• Kimi VL — dominates this category. 128K context handles 200+ page PDFs. Table extraction is near-perfect.
• GLM-4V — second place. Good chart interpretation, occasional OCR errors with handwriting.
• DeepSeek V4 — vision is not its primary strength. Adequate for basic images.
• ERNIE 4.5 — limited vision capabilities compared to multimodal-native models.

Which Model Should You Use?

🖥️ For Coding & Development

DeepSeek V4. The most cost-effective coding assistant. Pair it with Cursor or Continue.dev for a GPT-4-level experience at 1/20th the cost.

🌐 For Multilingual Apps

GLM-4. If your app serves users in 5+ languages, GLM-4's multilingual quality is unmatched among Chinese models.

👁️ For Document & Image Processing

Kimi VL. Legal contracts, financial reports, medical records — Kimi VL's 128K context + vision makes it the go-to for document-heavy workflows.

✍️ For Chinese Content Creation

ERNIE 4.5. Baidu's training data gives it deep knowledge of Chinese culture, idioms, and writing styles. Marketing copy in Chinese? ERNIE.

The AIWave Advantage

All four models are available through a single API key at aiwave.live. You pay in USD or crypto — no Chinese phone number, no ID verification, no WeChat Pay required.

from openai import OpenAI

client = OpenAI(
    api_key="sk-your-key",
    base_url="https://aiwave.live/v1"
)

# Swap models with one line
response = client.chat.completions.create(
    model="deepseek-chat",  # or glm-4, kimi-vl, ernie-4.5
    messages=[{"role": "user", "content": "Write a Python rate limiter"}]
)