.png)

# Kimi K2 vs Qwen 3 vs GLM 4.5 – The Ultimate 2025 Model Comparison for AI Builders

## Quick Summary: What separates Kimi K2, Qwen 3, and GLM 4.5 in 2025?

**Answer:** These three Chinese‑built large language models all leverage **Mixture‑of‑Experts architectures**, but they target different strengths. **Kimi K2** focuses on **coding excellence** and _agentic reasoning_ with a 1‑trillion parameter architecture (32 B active) and a **130 K token context window**, offering **64–65 % scores on SWE‑bench** while balancing cost. **Qwen 3 Coder** is the most **polyglot**; it scales to **480 B parameters** (35 B active), uses **dual thinking modes** and extends its context window to **256 K–1 M tokens** for repository‑scale tasks. **GLM 4.5** prioritises **tool‑calling and efficiency**, achieving **90.6 % tool‑calling success** with only **355 B parameters** and requiring just **eight H20 chips** for self‑hosting. The models’ pricing differs: Kimi K2 charges about **$0.15 per million input tokens**, Qwen 3 about **$0.35–0.60**, and GLM 4.5 around **$0.11**. Choosing the right model depends on your workload: coding accuracy and agentic autonomy, extended context for refactoring, or tool integration and low hardware footprint.

## Quick Digest – Key Specs & Use‑Case Summary

| **Model** | **Key Specs (summary)** | **Ideal Use Cases** |
| --- | --- | --- |
| **Kimi K2** | _1 T total parameters / 32 B active_; **130 K context**; **SWE‑bench 65 %**; **$0.15 input / $2.50 output per million tokens**; modified MIT license | **Coding assistants**, agentic tasks requiring multi‑step tool use; internal codebase fine‑tuning; autonomy with transparent reasoning |
| **Qwen 3 Coder** | _480 B total / 35 B active parameters_; **256 K–1 M context**; **SWE‑bench 67 %**; pricing ~$0.35 input / $1.50 output (varies); Apache 2.0 license | **Large‑codebase refactoring**, multilingual or niche languages, research requiring long memory, cost‑sensitive tasks |
| **GLM 4.5** | _355 B total / 32 B active_; **128 K context**; **SWE‑bench 64 %**; **90.6 % tool‑calling success**; cost **$0.11 input / $0.28 output**; MIT license | **Agentic workflows**, **debugging**, **tool integration**, and **hardware‑constrained deployments**; cross‑domain agents |

### Why the Eastern AI Revolution matters for developers

Chinese AI companies are no longer chasing the West; they’re **redefining the state of the art**. In 2025, Chinese open‑source models such as Kimi K2, Qwen 3, and GLM 4.5 achieved **SWE‑bench scores within a few points of the best Western models** while costing **10–100× less**. This disruptive price‑performance ratio is not a fluke – it’s rooted in strategic choices: **optimized coding performance**, **agentic tool integration**, and a focus on **open licensing**.

### A new benchmark of excellence

The **SWE‑bench** benchmark, released by researchers at Princeton, tests whether language models can resolve real GitHub issues across multiple files. Early versions of GPT‑4 barely solved 2 % of tasks; yet by 2025 these Chinese models were solving **64–67 %**. Importantly, their **context windows** and **tool‑calling abilities** enable them to handle entire codebases rather than toy problems.

### Creative example: The 10x cost disruption

Imagine a **startup** building an AI coding assistant. It needs to process **1 B tokens** per month. Using a Western model might cost **$2,500–$15,000** monthly. By adopting **GLM 4.5** or **Kimi K2**, the same workload could cost **$110–$150**, allowing the company to reinvest savings into product development and hardware. This **economic leverage** is why developers worldwide are paying attention.

### Meet the models: Overview of Kimi K2, Qwen 3 Coder and GLM 4.5

#### Overview of Kimi K2

**Kimi K2** is **Moonshot AI’s** flagship model. It employs a **Mixture‑of‑Experts (MoE)** architecture with **1 trillion total parameters**, but only **32 B activate per token**. This sparse design means you get the power of a huge model without massive compute requirements. The context window tops out at **130 K tokens**, enabling it to ingest entire microservice codebases. **SWE‑bench Verified** scores place it at **around 65 %**, competitive with Western proprietary models. The model is priced at **$0.15 per million input tokens** and **$2.50 per million output tokens**, making it suitable for high‑volume deployments.

Kimi K2 shines in **agentic coding**. Its architecture supports multi‑step tool integration, so it can not only generate code but also execute functions, call APIs, and run tests autonomously. A mixture of **eight active experts** handle each token, allowing domain‑specific expertise to emerge. The modified MIT license permits commercial use with minor attribution requirements.

#### Overview of Qwen 3 Coder

**Qwen 3 Coder**—often referred to as **Qwen 3.25**—balances **power and flexibility**. With **480 B total parameters** and **35 B active**, it offers robust performance on **coding benchmarks** and **reasoning tasks**. Its hallmark is the **256 K token native context window**, which can be expanded to **1 M tokens** using context extension techniques. This makes Qwen particularly suited to **repository‑scale refactoring** and cross‑file understanding.

A unique feature is the **dual thinking modes**: _Rapid mode_ for instantaneous completions and _Deep thinking mode_ for complex reasoning. Dual modes let developers choose between speed and depth. Pricing varies by provider but tends to be in the **$0.35–0.60 range per million input tokens**, with output costs around **$1.50–2.20**. Qwen is released under **Apache 2.0**, allowing wide commercial use.

#### Overview of GLM 4.5

**GLM 4.5**, created by **Z.AI**, emphasises **efficiency and agentic performance**. Its **355 B total parameters** with **32 B active** deliver performance comparable to larger models while requiring **eight Nvidia H20 chips**. A lighter **Air variant** uses **106 B total / 12 B active** and runs on **32–64 GB VRAM**, making self‑hosting more accessible. The context window sits at **128 K tokens**, which covers 99 % of real use cases.

GLM 4.5’s standout feature is its **agent‑native design**: it incorporates planning and tool execution into its core. Evaluations show a **90.6 % tool‑calling success rate**, the highest among open models. It supports a **Thinking Mode** and a **Non‑Thinking Mode**; developers can toggle deep reasoning on or off. The model is priced around **$0.11 per million input tokens** and **$0.28 per million output tokens**. Its MIT license allows commercial deployment without restrictions.