GLM-5.3 Dominates DeepSWE Benchmark with Cost-Efficient Coding

A recent benchmark test by DeepSWE has pitted two AI models against each other, with GLM-5.3 emerging as the clear winner in terms of cost efficiency and task coverage.

According to the results, GLM-5.3 outperformed Claude Fable 5 on a wide range of tasks, including concurrency and durability, program analysis, and more. While Fable excelled in areas such as Rust programming and data serialization, GLM-5.3's broader coverage and lower costs make it the more practical choice for most teams.

One of the key advantages of GLM-5.3 is its ability to deliver 17 solved tasks per $100, compared to Fable's 3. This translates to a significant cost savings, with GLM-5.3 costing just $3.99 per rollout versus Fable's $21.63.

DeepSWE's structured tasks test AI models on original software engineering tasks, evaluating their ability to solve long-horizon problems. The benchmark aims to measure AI coding agents under real-world conditions, making the results particularly relevant for developers and organizations evaluating AI models for software engineering use cases.