This reporting page focuses on structured output, tool routing, function calling, and MCP-style task completion. It is narrower than the general agentic leaderboard and is better aligned to developers choosing models for tool-heavy applications.
This page ranks models using only sourced tool-use benchmarks in the reporting family.
Bottom line: Tool-use quality determines whether your AI agent can actually call APIs, use MCP servers, and route structured requests. Not all "agentic" models are equally good at it.
According to BenchLM.ai, GLM-5.1 leads this ranking with a score of 77.5, followed by Qwen3.7 Max (74.4) and GPT-5.4 (69.3). There is a significant gap between the leading models and the rest of the field.
The best open-weight option is GLM-5.1 (ranked #1 with a score of 77.5). Open-weight models are highly competitive in this category — self-hosting is a viable alternative to proprietary APIs.
This ranking is based on provisional overall weighted scores across BenchLM.ai's scoring formula tracked by BenchLM.ai. For detailed model profiles, click any model name below. To compare two specific models head-to-head, use the "vs #" links.
GLM-5.1
Z.AI · 203K
Qwen3.7 Max
Alibaba · 1M
GPT-5.4
OpenAI · 1.05M
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The top model on this sourced reporting-family slice is GLM-5.1 by Z.AI with an average of 77.5.
The best open-weight model is GLM-5.1 at position #1.
11 models are listed with sourced benchmark coverage in this reporting family.
This ranking averages sourced tool-use benchmarks. It is narrower than the agentic category and focuses specifically on function calling and structured tool execution.
Tool-use benchmarks test specific function-calling patterns. Real-world tool use also depends on prompt engineering, retry logic, and error handling that benchmarks cannot capture.
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