Claude Code is the fastest agent framework but costs nearly three times more than the cheapest rival

Composio benchmarked four agent frameworks using Deepseek V4 Flash across 30 real-world tasks. The results expose a critical trade-off between speed and cost that will shape how teams choose their AI infrastructure.

Claude Code emerged as the fastest framework but carries a steep price tag. Each task costs $0.195, nearly three times the $0.073 per-task price of OpenCode, the cheapest option tested. This gap persists despite Claude Code using fewer tool calls and generating less output tokens than competitors. The performance advantage comes from architectural efficiency rather than raw resource consumption.

Success rates across frameworks proved remarkably similar, which shifts the decision criteria away from capability and toward economics. For organizations running hundreds or thousands of agent tasks monthly, cost differences compound quickly. A company executing 10,000 tasks annually would spend $1,950 with Claude Code versus $730 with OpenCode, a $1,220 annual gap that grows with scale.

Speed matters for specific use cases. Applications requiring real-time responses or handling latency-sensitive workloads benefit from Claude Code's faster execution. Background processing, batch operations, or non-time-critical automation favor the cheaper alternatives.

The benchmark reveals that agent framework selection is no longer primarily about accuracy. Success rates clustered together across all tested platforms, meaning the technology has matured to a point where capability differences shrink beneath cost and performance metrics. Teams now choose frameworks based on their budget constraints and latency requirements rather than whether the model can complete the task.

This creates a dilemma for developers and enterprises. Claude Code delivers speed advantages that justify its premium for latency-sensitive applications. For everything else, cheaper frameworks deliver identical results at a fraction of the cost. The real challenge lies in assessing which tasks truly require speed and which simply benefit from it, then matching framework selection accordingly.