GLM-5.2, DeepSeek V4, Qwen 3.6, MiniMax M2, Kimi K2, Llama 4, and Mistral — compared and ranked. Benchmarks, strengths, context lengths, and how to actually use them.
| Model | Provider | Best For |
|---|---|---|
| GLM-5.2 | Zhipu AI (z.ai) | General-purpose tasks, multilingual work, coding across languages |
| DeepSeek V4 | DeepSeek | Software development, debugging, algorithmic reasoning |
| Qwen 3.6 | Alibaba | Agentic workflows, tool use, browsing, function calling |
| MiniMax M2 | MiniMax | Research, analysis, long documents, creative work |
| Kimi K2 | Moonshot AI | Mathematics, logical reasoning, analysis |
| Llama 4 | Meta | Custom deployments, research, fine-tuning |
| Mistral Large | Mistral AI | European deployments, efficiency-focused use cases |
GLM-5.2 is Zhipu AI's flagship model — a Mixture-of-Experts architecture that delivers strong performance across coding, reasoning, and bilingual (English/Chinese) tasks. It is one of the most well-rounded open-source models available. It matches GPT-5 on several coding benchmarks while being available as open weights. If you work across English and Chinese content, GLM-5.2 is arguably the best model available — open or closed.
DeepSeek V4 comes in two variants: Pro for maximum quality and Flash for speed. It is arguably the strongest open-source model for code generation, consistently matching or beating GPT-5 on coding benchmarks. The Flash variant uses speculative decoding (dSpark) to achieve 85% faster inference without quality loss. If your primary use case is coding, DeepSeek V4 is hard to beat.
Qwen 3.6 is Alibaba's hybrid linear attention model with 256 experts. It uses a 35B MoE architecture with only 3B active parameters, making it incredibly fast to run. Qwen excels at tool calling, function use, and agentic workflows — it is the model of choice when you need an AI that takes actions, browses websites, and uses external tools. The 256K context window is the largest among open-source models.
MiniMax M2 is a massive 229B parameter MoE model with always-on reasoning capabilities. It handles the longest contexts of any model on this list — up to 1 million tokens. This makes it ideal for analyzing entire codebases, reading long research papers, or processing extensive documents. MiniMax also excels at creative writing and complex multi-step reasoning.
Kimi K2 is Moonshot AI's thinking model — designed for deep, step-by-step reasoning. It excels at mathematics, logic puzzles, and problems that require careful chain-of-thought analysis. If you need an AI that can work through complex problems methodically rather than jumping to quick answers, Kimi K2 is the strongest open-source option.
Meta's Llama 4 is the most widely deployed open-source model family. Its main advantage is ecosystem: the largest fine-tuning community, the most supported platforms, and extensive documentation. However, in raw benchmark performance, it trails the Chinese models (GLM, DeepSeek, Qwen) in 2026. Its license has some restrictions compared to truly open models like Qwen's Apache 2.0.
Mistral Large is Europe's flagship open-source model. It is known for efficiency — strong performance with fewer parameters. However, its research license restricts commercial use, limiting practical deployment. For European companies with data sovereignty requirements, it is the leading option. For general use, the Chinese models offer better performance and more permissive licensing.
The open-source landscape has shifted dramatically in 2026. Chinese models — particularly DeepSeek V4 and GLM-5.2 — now match or exceed GPT-5 and Claude on most benchmarks. This was unthinkable 18 months ago.
On coding benchmarks (HumanEval, MBPP, LiveCodeBench): DeepSeek V4 and GLM-5.2 consistently score in the top tier, trading blows with GPT-5. Qwen 3.6 is close behind and leads on agentic code tasks (where the AI must use tools and run code).
On reasoning benchmarks (MATH, GSM8K, GPQA): Kimi K2 and MiniMax M2 lead the open-source pack. Both use extended chain-of-thought reasoning, which trades latency for accuracy on hard problems.
On tool use and agentic tasks: Qwen 3.6 is the clear leader. Its hybrid linear attention architecture and strong function-calling make it the go-to model for browsing, automation, and multi-step agent workflows.
On multilingual tasks: GLM-5.2 dominates, particularly for English-Chinese bilingual work. Qwen 3.6 also has excellent multilingual support across 30+ languages.
On long-context tasks: MiniMax M2's 1M token context window is unmatched. For processing entire codebases, long legal documents, or extensive research papers, nothing else comes close.
There are several ways to access these models, each with different tradeoffs:
Desktop app with GLM, DeepSeek, Qwen, MiniMax, and Kimi built in. No API keys. Switch models per conversation. Also browses websites, runs commands, automates tasks.
Start free →Register with each provider, get API keys, pay per token. Most flexible but requires coding and managing multiple accounts and billing.
Run models locally on your machine. Free but requires significant GPU/RAM. You are responsible for setup, updates, and compatibility.
Download model weights and run inference yourself. Maximum control but requires deep ML knowledge and significant compute resources.
For most people, CopperRiver is the simplest option — all five top models in one app, with the ability to switch between them mid-conversation based on the task at hand.
The top open-source AI models in 2026 are GLM-5.2 (Zhipu AI), DeepSeek V4, Qwen 3.6 (Alibaba), MiniMax M2, Kimi K2 (Moonshot AI), Llama 4 (Meta), and Mistral Large. Each excels at different tasks — DeepSeek for coding, Qwen for tool calling, MiniMax for reasoning, GLM for bilingual tasks.
In many benchmarks, yes. DeepSeek V4 and GLM-5.2 match or exceed GPT-5 on coding tasks. Qwen 3.6 leads on tool use. The gap has closed significantly — open-source models now compete with proprietary models on most tasks while being free to use and run locally.
Yes. Most of these models are available for free through their providers' APIs, through Hugging Face, or through apps like CopperRiver that bundle multiple models. You can also run them locally with tools like Ollama or LM Studio.
DeepSeek V4 and GLM-5.2 are the strongest open-source models for coding. DeepSeek excels at code generation and debugging, while GLM-5.2 is strong across Python, JavaScript, Go, and more. Both match or exceed GPT-5 on code benchmarks.
CopperRiver bundles GLM-5.2, DeepSeek V4, Qwen 3.6, MiniMax M2, and Kimi K2 into one desktop app with no API keys. You can switch models mid-conversation. Plans start at $9/mo.