Open-source agents with frontier advisors: matching frontier performance through training and harness engineering

In a study examining the performance of legal AI models using Harvey's Legal Agent Benchmark (LAB), the integration of open-source models with frontier tools and Fireworks-native post-training techniques significantly improved performance and cost efficiency. The hybrid system, featuring an open-source GLM 5.1 worker and Claude Opus 4.7 as an advisor, achieved an 18/100 all-pass rate at a reduced cost of $368, outperforming Opus alone, which had a 14/100 rate at $954. Post-training on the Fireworks platform, utilizing supervised and reinforcement fine-tuning on models like Kimi K2.6, further enhanced performance, demonstrating the potential of open-source models to approach frontier-level quality while maintaining cost-effectiveness. This approach allowed for a seamless transition from research to production, with no discrepancies between training and serving models, emphasizing the competitive edge of open-source solutions in legal AI tasks.

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