Cohere Rerank v4.0 vs GLM-5

Pick two tools from the same category to compare side-by-side.

	Cohere Rerank v4.0 Cohere's current-generation reranking models (Pro and Fast variants) for boosting retrieval relevance in RAG and agent search.	GLM-5 Zhipu AI's MIT-licensed 744B-parameter MoE model targeting complex systems engineering and agentic tasks.
Category	model	model
Vendor type	commercial	open source
Pricing	usage	free
Strengths	+ Purpose-built second-stage reranking improves precision of any retriever + Multilingual and handles semi-structured JSON documents + Pro/Fast split lets builders trade quality against latency and throughput	+ MIT-licensed open weights + Designed specifically for agentic and tool-use workloads + Sparse attention architecture reduces long-context serving cost + 40B active parameters keep inference cost moderate for its class
Tradeoffs	− API-only (no open weights) − Adds an extra network hop and per-query cost to the retrieval pipeline	− 744B total parameters still demand multi-GPU infrastructure to self-host − Smaller first-party API ecosystem than DeepSeek or Alibaba
Used in stacks	0	0