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, Qwen3.6 Plus leads this ranking with a score of 56.9, followed by Qwen3.5 397B (54) and Claude Opus 4.5 (53.7). There is meaningful separation between the top models, suggesting genuine performance differences.
The best open-weight option is Qwen3.5 397B (ranked #2 with a score of 54). 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.
Qwen3.6 Plus
Alibaba · 1M
Qwen3.5 397B
Alibaba · 128K
Claude Opus 4.5
Anthropic · 200K
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The top model on this sourced reporting-family slice is Qwen3.6 Plus by Alibaba with an average of 56.9.
The best open-weight model is Qwen3.5 397B at position #2.
6 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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