Claude Code: Fastest Agent Framework, but Nearly 3× the Cost of Rivals
Agentic AIClaude Code finishes agent tasks in 122 seconds. OpenCode finishes them for $0.073. Both can’t be optimal — and the gap between them is wider than most teams realize.
The Number
Composio tested four agent frameworks — Claude Code, Codex, OpenCode, and Oh My Pi — across 30 real-world tasks integrating tools like Gmail, GitHub, Slack, and Notion. The underlying model was held constant (DeepSeek V4 Flash) across all four runs, isolating the framework’s contribution to performance and cost.
Claude Code came in at $0.195 per successful task. OpenCode, the cheapest framework tested, ran at $0.073 — a 2.7× cost premium for the same model doing the same work.
How the Math Works
The cost difference is not driven by model spend alone. Claude Code achieved its 122-second speed by using the fewest tool calls and generating the least output tokens — which should theoretically reduce cost. That it still cost the most suggests a framework-level overhead: the orchestration, scaffolding, and management layer that Claude Code wraps around the model runs at a higher operational cost than leaner frameworks.
Oh My Pi ran slowest at 272 seconds per task — a 2.2× speed gap between the extremes — but posted the highest success rate at 17 out of 30 tasks. OpenCode, the cheapest, had the lowest task success at 14 of 30. Speed, cost, and success rate did not correlate in any simple direction across the four frameworks tested.
What It Signals
Framework choice now functions as a cost multiplier on top of model spend. As teams scale agentic workflows — running not 30 tasks in a benchmark but thousands of tasks per day in production — the 2.7× cost spread between Claude Code and OpenCode becomes a meaningful budget variable that sits entirely outside the model selection decision. Earlier coverage of agentic AI’s infrastructure economics focused on model pricing; this benchmark surfaces the framework layer as a second, underexamined cost driver.
The data also complicates the assumption that spending more on infrastructure produces proportionally better outcomes. Claude Code is the fastest and most expensive but does not lead on task success rate. That tradeoff deserves explicit evaluation rather than default selection based on brand familiarity.
The So What
Teams running agentic workflows at scale should run their own framework benchmarks on representative tasks before locking into Claude Code as a default. The Composio study used a single model across all frameworks — the right methodology for isolating framework cost — but your task mix, success rate requirements, and latency constraints may produce a different optimal point. Speed and brand do not substitute for knowing your own cost-per-outcome number.
Source: The Decoder
Content created with AI assistance and reviewed for accuracy.
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