GLM-5.1 vs Mixtral 8x22B v0.1

On shared benchmark coverage, Google-Proof Q&A has GLM-5.1 at 86.8 and Mixtral 8x22B v0.1 at 60.1, with GLM-5.1 ahead by 26.7 points. The largest visible gap is 26.7 points on Google-Proof Q&A, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.

The capability footprint differs most on reasoning mode: GLM-5.1, function calling: GLM-5.1, tool use: GLM-5.1, structured outputs: GLM-5.1, and code execution: GLM-5.1. Both models share the core language-model surface, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.

For cost, GLM-5.1 lists $0.95/1M input and $3.15/1M output tokens, while Mixtral 8x22B v0.1 lists $0.3/1M input and $0.9/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Mixtral 8x22B v0.1 lower by about $1.13 per million blended tokens. Availability is 2 providers versus 8, so concentration risk also matters.

Choose GLM-5.1 when coding workflow support and larger context windows are central to the workload. Choose Mixtral 8x22B v0.1 when provider fit, lower input-token cost, and broader provider choice are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.