Best Tool Use & Function Calling Models in 2026
As of July 7, 2026, the top model in best tool use & function calling models on the BenchLM leaderboard is Qwen3.7 Plus with a score of 72.
Last verified: July 7, 2026
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.
Verdict
Qwen3.7 Plus leads for most workloads by a narrow margin.
Based on BenchLM composite scores, July 2026.
According to BenchLM.ai, Qwen3.7 Plus leads this ranking with a score of 72, followed by Claude Opus 4.8 (70.6) and GLM-5.1 (70.1). The top three are separated by just a few points — any of them would perform well for this use case.
The best open-weight option is GLM-5.1 (ranked #3 with a score of 70.1). 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.
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Key Takeaways
The top model on this sourced reporting-family slice is Qwen3.7 Plus by Alibaba with an average of 72.
The best open-weight model is GLM-5.1 at position #3.
18 models are listed with sourced benchmark coverage in this reporting family.
Score in Context
What these scores mean
This ranking averages sourced tool-use benchmarks. It is narrower than the agentic category and focuses specifically on function calling and structured tool execution.
Known limitations
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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