Rankings of the best AI models for tool and function calling. Compare models by tool use accuracy and API integration capabilities.
| Model | Best for | Top strength | Watch out | Cost · Context |
|---|---|---|---|---|
| DeepSeek-V4.1-Flash DeepSeek | Best open-weight quality-to-price in the market | Frontier-adjacent quality at ~10× cheaper than US frontier | Routing through PRC providers may be a data-residency concern | $0.22 / $0.66 1.0M ctx |
| GLM-5.3 Zhipu AI | Zhipu AI's flagship — competitive Chinese open-weight | Strong multilingual | Smaller third-party ecosystem in Western markets | $1.20 / $4.00 1.0M ctx |
| GLM-5.3-Flash Zhipu AI | Zhipu AI's flagship — competitive Chinese open-weight | Strong multilingual | Smaller third-party ecosystem in Western markets | $0.15 / $0.50 1.0M ctx |
| Qwen3.8 Max Alibaba Cloud / Qwen Team | Solid open-weight base for the Qwen ecosystem | Broad sizing options | Newer Qwen 3.5/3.6/3.7 ahead on most tasks | $1.65 / $4.95 1.0M ctx |
Best open-weight quality-to-price in the market
Zhipu AI's flagship — competitive Chinese open-weight
Zhipu AI's flagship — competitive Chinese open-weight
Solid open-weight base for the Qwen ecosystem
Best open-weight quality-to-price in the market
When to useCost-sensitive workloads at scale; on-prem requirements.
Zhipu AI's flagship — competitive Chinese open-weight
When to useBilingual workloads; open-weight experimentation.
Zhipu AI's flagship — competitive Chinese open-weight
When to useBilingual workloads; open-weight experimentation.
Solid open-weight base for the Qwen ecosystem
When to useFine-tuning base; specific multilingual needs.
As of September 2026, GPT-6 Astra leads tool calling benchmarks with a score of 36.2, followed by DeepSeek-V4.1-Flash (35.1) and Muse Spark 1.3 (34.7). Rankings test function selection accuracy, parameter extraction, and multi-step tool chain orchestration from natural language instructions.
Ranked by 50 benchmarks including Berkeley Function Calling Leaderboard (BFCL) and real-world API integration tests, evaluating schema adherence, parameter accuracy, and error recovery.
Function calling lets AI models interact with external tools and APIs by outputting structured calls with correct parameters. For example, extracting a city name from 'What's the weather in Tokyo?' and calling a weather API with the right parameters. Top models score above 90% on schema adherence.
Models scoring highest on multi-step orchestration benchmarks, where one tool's output feeds another's input. The gap between models is largest on complex chains — most models handle single-function calls well, but only the top 3-5 reliably orchestrate multi-step workflows.
Yes. When given a function schema (describing available tools and their parameters), top models can select the right function, extract parameters from natural language, and output valid structured calls. This is the foundation for AI agents, chatbots with tools, and automated workflows.
An AI agent is a model that can plan and execute multi-step tasks by calling tools, reading results, and deciding what to do next. Tool calling accuracy is the core capability that determines agent reliability. The leaderboard above measures exactly this capability.
Most frontier models support function calling, but quality varies significantly. Models fine-tuned specifically for tool use outperform general-purpose models, especially on complex orchestration. Check the scores above — single-function accuracy and multi-step orchestration are different skills.
