Best LLM for Tool Use & Function Calling (2026)
Tool use (function calling) is the foundation of agentic AI systems — models that can call APIs, query databases, run code, and orchestrate multi-step workflows. The best tool-use models reliably produce valid JSON, handle parallel tool calls, and maintain coherent state across many tool invocations.
Top picks for tool use
Top-tier JSON schema adherence and instruction following. Handles complex multi-step tool chains with minimal hallucination. Best for production agentic pipelines where reliability matters most.
Near-Opus reliability at 5× lower cost. Parallel tool calls, structured outputs, and strong context retention across long agentic workflows.
Mature function calling API with the largest ecosystem of integrations. Parallel tool calls, reliable JSON mode, and extensive documentation. Best for teams already on OpenAI.
Strong function calling with open weights — self-hostable for teams with data privacy requirements. Competitive with GPT-4o on structured output tasks.
Model comparison — tool use
0 models| # | Model | Provider | Tier | SWE-bench | AutoBench | Intelligence | Input/1M | Context | Confidence |
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Which model for which task?
Anthropic models lead on maintaining coherent state and goals across dozens of tool calls. Extended thinking mode (Sonnet 4.5) helps with complex planning.
Both have mature, well-documented function calling APIs. GPT-4o has the largest ecosystem; Claude Sonnet 4.5 has stronger JSON schema adherence.
For pipelines making thousands of tool calls per day, efficient-tier models reduce cost by 10–20× vs. frontier models with acceptable quality for well-defined schemas.
Open weights enable on-premise deployment for data privacy. Competitive function calling quality with no API dependency or data leaving your infrastructure.