Qwen 3 Max vs Kimi K2 vs GLM-4.5 vs DeepSeek v3.1: Review & Comparison

The article compares Qwen 3 Max, GLM-4.5, Kimi K2, and DeepSeek v3.1 across benchmarks, licensing, pricing, context windows, and deployment. Its main claim is that benchmark leadership does not equal open source accessibility, and that GLM-4.5 offers the strongest mix of license, performance, and usability.

Reading notes

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• Qwen 3 Max ranks high on LMArena and benchmark tests, but it is API-only, cannot be downloaded or run locally, and is expensive to use.
• The article treats Qwen 3 Max as a marketing problem for open source claims because access depends on keys, billing, and cloud service use.
• GLM-4.5 is presented as the actual open source winner because it uses Apache 2.0, can be downloaded and modified, and runs with strong throughput and a 128,000-token context window.
• GLM-4.5 includes a thinking and non-thinking mode, which the article describes as a practical design for both fast chat and deeper reasoning.
• Kimi K2 is framed as a coding specialist, with strong SWE-Bench Verified performance and a mixture-of-experts design that activates only part of the model per token.
• Kimi K2 is slower in token generation than some competitors, but the article says it often produces code that works sooner and can handle large codebases.
• DeepSeek v3.1 Thinking is described as a hybrid model that adjusts reasoning depth dynamically instead of using separate modes.
• The article notes that DeepSeek v3.1 scores well on coding benchmarks, but recent evaluations show some regression and uneven behavior in production-like use.
• The article explains that SWE-Bench measures real repository issues, but benchmark results still may not match a specific codebase or domain.
• It argues that very large context windows are not necessary for most tasks and that 128,000 to 130,000 tokens cover most real uses.
• Running these models locally requires serious hardware, including roughly 64GB of VRAM for the larger active models.
• The article says API access can make sense for burst workloads and prototypes, but sustained use becomes expensive.
• It highlights open source models as useful for compliance, data sovereignty, and fine-tuning on proprietary data.
• Documentation quality and bilingual support are presented as practical advantages, with GLM-4.5 singled out for clearer documentation and stronger English-language support.
• The conclusion recommends GLM-4.5 for most organizations, Kimi K2 for coding-heavy products, and Qwen 3 Max only when maximum performance justifies the cost and vendor dependence.