AutoBe's Hardcore Function Calling Benchmark Pushes LLM Backend Generation to New Limits

The landscape of AI-assisted backend development just got a serious stress test with AutoBe, an open-source project that's pushing language models to their absolute limits through what its creators call "the most extreme function calling benchmark ever." This isn't your typical code generation tool—AutoBe leverages LLM function calling at every phase, including compiler Abstract Syntax Tree (AST) structures of infinite depths, to generate complete backend applications.

The Benchmark That Breaks Models

What makes AutoBe's approach so hardcore? The project doesn't just ask models to write code—it forces them to navigate and construct extremely complex type systems, including compiler AST structures, through function calling. This includes a validation feedback loop that detects detailed type errors and provides recovery guidance when models make mistakes.

When tested across different models on the same topics, the results were eye-opening. Anthropic's Claude Sonnet 4.5 generated 630 test functions, while OpenAI's GPT-5.1 went wild creating 2,000 test functions for identical scenarios. Meanwhile, Qwen3-Next-80B-A3B produced 360 functions. These aren't just numbers—they represent fundamentally different approaches to problem-solving and type handling.

100% Compilation Success, But Runtime Reality

The team recently achieved a milestone: 100% build success rate for small to medium-sized backend applications using Qwen3-Next-80B-A3B. However, they're quick to note this doesn't guarantee 100% runtime success. The distinction matters—compilation ensures syntactic correctness, but runtime success depends on logic, external dependencies, and real-world conditions.

This achievement came after months of optimization, including RAG (Retrieval-Augmented Generation) improvements that now enable large-scale backend generation on local LLMs. The project has expanded support beyond commercial models like GPT and Sonnet to include various local models such as Qwen3, DeepSeek, and Kimi.

The Real Challenge: Validation Feedback

What sets AutoBe apart is its validation feedback mechanism. When a model creates arguments of the wrong type, the system detects these errors in detail and provides specific feedback for recovery. This isn't just about getting the right answer—it's about teaching models to correct themselves through iterative refinement.

The current benchmark, while groundbreaking, has limitations. It's uncontrolled and primarily indicates whether models can construct extremely complex types through function calling, rather than providing a comprehensive evaluation of function calling capabilities. The team acknowledges this and promises improvements in future iterations.

Why This Matters for the AI Ecosystem

AutoBe's approach reveals critical insights about current LLM capabilities. The vast differences in function generation counts between models suggest varying strategies for handling complexity. Some models appear to brute-force solutions with massive function counts, while others take more conservative approaches.

For developers and researchers, this benchmark provides a reference point for anyone planning function calling with extremely complex types. It demonstrates both the potential and the limitations of current models when pushed beyond typical use cases.

The Road Ahead

The AutoBe team, consisting of just two developers, remains committed to the project's development. Their goals include perfecting prompting and RAG optimization to handle the issue of models generating excessive test functions, as well as consistently releasing benchmark data for various local LLMs beyond just Qwen3-Next-80B-A3B.

Their ultimate vision is creating an environment where developers can freely generate backend applications on local devices without cost burden—a significant step toward democratizing AI-assisted development.

Getting Involved

AutoBe is open-source and available on GitHub, with benchmark results and examples also publicly accessible. The project represents a fascinating intersection of compiler theory, function calling optimization, and practical backend development.

As the team continues to push boundaries, their work serves as both a technical achievement and a reminder of how much room remains for improvement in AI-assisted software development. The fact that two developers can create such a sophisticated benchmark speaks to the maturity of the tools available today, while the results highlight the ongoing challenges in making AI truly reliable for complex software engineering tasks.

For anyone interested in the cutting edge of AI-assisted development, AutoBe's hardcore benchmark offers both inspiration and a sobering look at current limitations. It's not just about generating code—it's about generating correct, complex, and maintainable code through the most challenging function calling scenarios imaginable.