How we built the fastest GLM-5 on Artificial Analysis

Baseten reports that its inference stack achieved more than 200 tokens per second and roughly 200 ms time to first token for Z.ai’s open-weight GLM-5 model in Artificial Analysis benchmarks. GLM-5 is a 744-billion-parameter mixture-of-experts reasoning model with 40 billion active parameters per pass, a 200K-token context window, MIT licensing, and strong reported coding, mathematics, and agentic-task benchmark results. Baseten attributes its performance to architecture-specific MoE routing and dispatch kernels for GLM-5’s deeper, narrower design, custom support for DeepSeek Sparse Attention to reduce long-context KV-cache costs, and low-overhead speculative decoding using the model’s native Multi-Token Prediction heads rather than a separate draft model. The company argues that throughput is especially important for reasoning workloads, where hidden thinking sequences may be substantially longer than final answers, and positions the optimized deployment for coding agents, tool-use pipelines, and other long-running production applications.

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