<cite index="43-2">On the DeepSWE v1.1 coding benchmark, Claude Fable 5 scores 69.7% pass@1 at $21.63 per task, while GLM-5.3 scores 69.0% at $3.99 per task—a cost difference of 5.4x.</cite> <cite index="43-2">Per $100 spent, GLM-5.3 solves 17 tasks while Fable solves 3, making GLM the more cost-efficient path for budget-constrained agentic coding.</cite> DeepSWE tests long-horizon software engineering across 113 original tasks in 5 languages, <cite index="43-3">building contamination-free benchmarks that reflect how frontier coding agents actually perform in real software engineering work.</cite>
<cite index="43-2">The models separate once retries are allowed: GLM-5.3 leads pass@2 at 81.1% versus Fable 5's 77.1%, and extends that lead at pass@4 (87.6% vs. 84.1%).</cite> This pattern favors teams using routing or cascading strategies: run GLM-5.3 first at low cost, escalate to Fable 5's superior single-shot accuracy only when GLM fails. <cite index="43-2">Per-task correlation between the two is 0.65, the highest agreement in the comparison set, so they solve largely the same problems through different paths.</cite>
For production deployments, the trade-off is clear: GLM-5.3 is the cost optimization play, delivering 69% accuracy at a fraction of Fable's price; Fable 5 is the quality ceiling, with marginally higher single-shot performance and stronger reasoning on complex systems work. At 10,000-task scale, the cost gap ($39,900 vs. $216,300) justifies mixed routing logic. Neither model is universally superior—the choice depends whether your workload can absorb retries or demands first-attempt precision.