Qwen vs Kimi vs DeepSeek vs GLM

Quick Answer

Four large Mixture-of-Experts model families from Chinese AI labs are competing hard for the same audience right now: developers who want frontier-adjacent capability without being locked into a closed provider. Qwen3.8-Max (Alibaba) is the largest by stated capability ambition but is still API-only with an undisclosed active-parameter count. Kimi K3 (Moonshot AI) is the largest by total parameters and already open-weight. DeepSeek V4-Flash (DeepSeek) is dramatically cheaper and built for high-volume execution rather than maximum capability. GLM 5.2 (Z.ai) is the most fully documented of the four, with disclosed specs and available open weights today.

There’s no single winner across every category. This page breaks the decision down by what you’re actually optimizing for.

At a Glance

Which Wins Where

Only a few of these categories have clear, evidence-backed answers. The rest genuinely depend on your setup, and this page says so rather than forcing a pick.

Best overall capability: Unresolved. Kimi K3 has the largest total parameter count and the most aggressive vendor capability claims (behind only Claude Fable 5 and GPT-5.6 on Moonshot’s own numbers), but none of the four have been benchmarked against each other by an independent evaluator. Treat any single vendor’s “best overall” claim as marketing until third-party verification exists.

Best for coding: Depends on the task. GLM 5.2 and Kimi K3 currently have the strongest documented coding benchmark claims. Qwen3.8-Max reports a weaker SWE-bench Pro score specifically, alongside stronger results elsewhere. DeepSeek V4-Flash is built for high-volume, routine coding execution rather than the hardest problems.

Best for agents: Depends on budget and task complexity. All four target agentic workflows. DeepSeek V4-Flash is the practical choice for high-volume, cheap agent loops; Kimi K3 and Qwen3.8-Max lean toward harder agent reasoning per their own reported benchmarks.

Best for long context: Tie. All four offer a 1M-token context window.

Best for high-volume API work: DeepSeek V4-Flash, clearly. Its pricing is roughly a tenth to a twentieth of Qwen3.8-Max’s, the largest, most verifiable gap in this entire comparison.

Best for low cost: DeepSeek V4-Flash, for the same reason.

Easiest to self-host: DeepSeek V4-Flash has the smallest active-parameter footprint (about 13 billion) among the three models with released open weights, making it the most realistic to actually run yourself, though it still requires real GPU infrastructure, not a laptop.

Easiest to access without setup: Kimi K3 (via Kimi.com) and DeepSeek (via chat.deepseek.com) both offer simple consumer chat interfaces with no API key required to start.

Best for privacy: This depends on deployment, not vendor choice. All four companies are based in China, so if data residency or privacy is the priority, the meaningful lever is self-hosting one of the three open-weight models (Kimi K3, DeepSeek V4-Flash, or GLM 5.2) rather than assuming any one vendor’s hosted API is more private than another’s.

Best license for commercial use: GLM 5.2 and DeepSeek V4-Flash are both confirmed MIT-licensed. Kimi K3’s exact license terms should be verified directly before commercial deployment. Qwen3.8-Max’s license isn’t published yet.

Best for beginners: None of these four are true beginner tools, but Kimi K3 and DeepSeek’s consumer chat interfaces are the more approachable entry points if you just want to try one without an API key.

Best for advanced teams: Depends on the specific need: GLM 5.2 for the most complete documentation and available-today self-hosting, DeepSeek V4-Flash for cheap high-volume execution, Kimi K3 for the largest open-weight model available right now, Qwen3.8-Max for Alibaba’s broader multimodal input range once its open-weight release lands.

The Honest Reality

All four are moving fast, and vendor benchmark claims across this entire category should be treated the same way: a starting point, not proof. None of these four models have been independently benchmarked head-to-head against each other as of this writing. If a decision genuinely matters, run your own evaluation on your actual tasks rather than trusting any single comparison table, including this one.

Explore the Pairs

• Qwen3.8-Max vs Kimi K3, the two largest, newest entrants head to head
• Qwen3.8-Max vs DeepSeek V4-Flash, maximum capability vs efficient execution
• Qwen3.8-Max vs GLM-5.2, scale vs documentation and available-today open weights

Related Tools and Guides

• Tools: Qwen3.8-Max, Kimi K3, DeepSeek, GLM 5.2
• Guides: Frontier Open Models Explained, Open Models vs Closed Models, How to Make Cheaper AI Models Work Smarter
• Glossary: Mixture of Experts, Model Parameters, Model Routing, Output Token Efficiency