DeepSeek vs Qwen vs Kimi vs GLM: A Cloud Architect's Take

When it comes to choosing a large language model (LLM) for production workloads, engineers must weigh several factors beyond just benchmark scores. These include p99 latency under load, cost per million tokens, uptime and SLA guarantees, and the ability to deploy across regions without latency budget issues. The four model families examined in this comparison all expose OpenAI-compatible APIs, ensuring a clean migration story.

DeepSeek's V4 Flash model stands out as the price-to-performance champion. Priced at $0.25 per million output tokens, it offers GPT-4o quality at roughly 1/40th the cost. During peak traffic, DeepSeek consistently pushes 60 tokens per second. However, its vision capabilities are limited, making it less suitable for image understanding tasks.

Qwen's catalog is extensive, featuring models ranging from 8B to 397B parameters. With output prices starting at $0.01 per million tokens, Qwen provides a broad range of options for various workloads. The 8B model is ideal for simple classification and extraction tasks, while the 32B model serves as a general-purpose workhorse. Qwen also leads in multimodal capabilities, offering native support for image inputs and text, image, and audio/video outputs.

Kimi's K2.5 model is the most expensive at $3.00 per million tokens. Positioned as a reasoning-focused model, it justifies its price with superior chain-of-thought performance. However, its high cost may not be easily justified without clear business value.

GLM, developed by Zhipu AI, offers models tuned for Chinese-language workloads. With output prices starting at $0.01 per million tokens, GLM delivers impressive performance in Chinese tasks. However, its English capabilities are slightly lower compared to the other models.