Choosing the right software wrapper around an AI model can dramatically change both the cost and speed of completing a task — sometimes by a factor of three or more. New benchmark results from AI tooling company Composio, released in August 2025, put four leading agent frameworks — Claude Code, Codex, OpenCode, and Oh My Pi — head-to-head on 30 real-world tasks using DeepSeek V4 Flash, one of the most cost-effective large language models available. The findings reveal a fragmented landscape where no single framework dominates, but the trade-offs between price, speed, and reliability are stark enough to shape architectural decisions for developers building AI-driven automation.
Composio’s benchmark puts Claude Code, Codex, OpenCode, and Oh My Pi through 30 real-world trials
Composio, a company that provides integration tooling for AI agents, designed the test to reflect actual usage patterns rather than synthetic benchmarks. The 30 tasks involved live interactions with tools that developers and business users rely on daily: Gmail, GitHub, Slack, and Notion. Each framework was given the same set of instructions, the same underlying model (DeepSeek V4 Flash), and the same target environment. The goal was simple: measure how often each framework succeeded, how long it took, and how much it cost in terms of API calls to the underlying language model.
Oh My Pi emerged as the most reliable, completing 17 of the 30 tasks successfully. Claude Code and Codex each succeeded on 16 tasks, while OpenCode trailed slightly with 14 successes. On the surface, these numbers suggest a tight race — a difference of only three tasks between the best and worst performer. But the deeper story lies in the speed and cost metrics, where the variation is far more dramatic.
Claude Code is the fastest but costs nearly three times as much as the cheapest rival
Claude Code, built by Anthropic as a coding agent, completed each successful task in an average of 122 seconds. That made it the fastest framework in the test, more than twice as fast as Oh My Pi, which took 272 seconds per task. Codex and OpenCode fell in between, with average times of 152 seconds and 174 seconds respectively.
But speed comes at a price. Claude Code’s average cost per successful task was $0.195, making it the most expensive framework by a significant margin. OpenCode, by contrast, cost just $0.073 per successful task — roughly 2.7 times cheaper. Codex ($0.119) and Oh My Pi ($0.121) clustered in the middle, each about 60 percent cheaper than Claude Code.
What makes this cost disparity particularly notable is that Claude Code also used the fewest tool calls and generated the least output tokens among the four frameworks. In other words, it was the most efficient in terms of raw API usage, yet still the most expensive. This suggests that the pricing structure of the agent framework itself — not just the underlying model — plays a major role in the final bill. Claude Code may be invoking a more expensive internal orchestration layer, or it may be making calls that are more costly per token due to how it structures prompts and responses.
Oh My Pi leads in reliability but lags in speed
Oh My Pi, an open-source agent framework designed for general-purpose automation, posted the highest success rate at 17 out of 30 tasks. But it also took the longest: 272 seconds per successful task, more than double Claude Code’s time. For users who prioritize accuracy over speed, Oh My Pi’s higher success rate might be worth the wait. However, the cost per task ($0.121) was not the lowest, which means that slower execution does not necessarily translate to cheaper operation.
OpenCode, the cheapest framework at $0.073 per task, had the lowest success rate (14/30). That creates a classic engineering trade-off: you can save money, but you will have to retry failed tasks more often, which could erode the cost advantage in practice. For a developer running a high-volume pipeline, the real cost per successful task might be closer to OpenCode’s raw cost times a failure multiplier. If a task fails roughly 53 percent of the time (16 failures out of 30), the effective cost could be nearly double the headline figure.
Codex, developed by OpenAI, offered a middle ground: 16 successes, 152 seconds per task, and $0.119 per task. It did not lead in any category, but it also did not lag significantly in any dimension. For teams that value consistency across metrics, Codex may be the least risky choice.
Seven tasks were framework-dependent — success hinged on the wrapper
A particularly revealing finding from the Composio benchmark is that seven of the 30 tasks passed or failed solely based on which framework ran them. This means that the underlying model — DeepSeek V4 Flash — was capable of completing the task in principle, but the framework’s orchestration, tool integration, or error handling either enabled or blocked success. These framework-dependent tasks highlight the critical role of the agent layer in determining real-world performance.
For example, tasks that required multi-step sequences, account authentication, or parsing complex responses from external APIs could fail in one framework while succeeding in another, even though the same model was used. This suggests that developers should not assume that a model’s benchmark performance translates directly to agent-based outcomes. The framework’s ability to manage state, retry on failure, and format tool calls correctly can be the deciding factor.
What is an agent framework and why does it affect cost so much?
An agent framework is a software layer that sits between a large language model and the external tools or APIs it needs to interact with. When a user asks an AI agent to “send an email to the team with the latest sales figures,” the framework must break that instruction into a sequence of steps: open Gmail, compose a message, find the correct recipients, attach data, and send. Each step may require a separate API call to the language model, plus calls to the external service. The framework decides how to structure those calls, how to interpret the model’s responses, and how to handle errors.
Cost differences between frameworks arise from several factors. Some frameworks use more aggressive prompt engineering, sending longer context windows that increase token usage. Others may make multiple redundant calls to verify a result. The framework’s internal logic — how it parses the model’s output and decides the next action — can also lead to different numbers of API calls even for the same task. Claude Code, despite using fewer tool calls and generating fewer output tokens, still ended up more expensive, which points to a higher cost-per-call structure, possibly due to premium routing or additional safety checks.
For teams building production systems, understanding these hidden costs is essential. The cost of the underlying model is only part of the equation. The framework surcharge can easily double or triple the final bill, as this benchmark shows.
DeepSeek V4 Flash keeps costs low, but the framework still dominates the price gap
All four frameworks in the Composio test used DeepSeek V4 Flash, a model known for its low token pricing. DeepSeek’s pricing strategy — roughly 60 percent cheaper than comparable models from OpenAI — has made it a popular choice for cost-sensitive applications. But the benchmark demonstrates that even with the cheapest model, framework choices can introduce a nearly 3x price difference. That ratio is likely to persist or even widen when more expensive models like GPT-5 or Claude 4 are used, because the framework’s overhead multiplies the base model cost.
In practical terms, a team running 10,000 tasks per day would pay roughly $730 per day using OpenCode, versus $1,950 per day using Claude Code — a difference of over $1,200 daily. Over a year, that gap exceeds $430,000. For enterprise deployments, the framework choice is not just a technical preference; it is a financial decision with significant consequences.
Why speed and cost are inversely correlated in this benchmark
The Composio results show a clear inverse relationship between speed and cost: the fastest framework (Claude Code) was the most expensive, and the slowest (Oh My Pi) was not the cheapest. OpenCode, the cheapest, was also the second slowest. This pattern suggests that frameworks that optimize for speed may be using more expensive orchestration strategies — perhaps by making parallel calls or using higher-priority API tiers — while frameworks that prioritize cost efficiency may accept slower execution by using fewer, more carefully sequenced calls.
For developers, this means there is no free lunch. Choosing a framework requires a clear understanding of the workload’s sensitivity to latency versus budget. A customer-facing chatbot that needs sub-second responses may justify the higher cost of Claude Code. An internal data-processing pipeline that runs overnight can tolerate minutes of latency and benefit from the lower cost of OpenCode.
How to choose the right agent framework for your use case
Based on the Composio benchmark, the decision framework can be summarized in three scenarios:
- If success rate is paramount and latency is not a concern, Oh My Pi offers the highest reliability (17/30) at a moderate cost ($0.121 per task). Its slower speed (272 seconds) is acceptable for background tasks or batch processing.
- If speed is the top priority and budget is generous, Claude Code delivers the fastest results (122 seconds) but at the highest cost ($0.195 per task). It is best suited for real-time applications where a 2x speed advantage justifies a 2.7x price premium.
- If cost is the primary constraint and some failures are acceptable, OpenCode is the cheapest option ($0.073 per task) but comes with the lowest success rate (14/30). It works well for high-volume, low-stakes tasks where retries are cheap.
- If balance across all metrics is needed, Codex offers a middle ground: 16 successes, 152 seconds, and $0.119 per task. It is a safe default for teams that cannot afford to optimize for a single dimension.
It is also worth noting that no framework succeeded on all 30 tasks. Even the best performer, Oh My Pi, failed 13 times. This underscores the reality that agent-based automation is not yet a solved problem. Frameworks still struggle with tasks that require nuanced interpretation, multi-step authentication, or handling unexpected API responses. Developers should budget for failure handling and build retry logic into their systems regardless of the framework they choose.
The broader implications for AI agent adoption
The Composio benchmark arrives at a time when companies are rapidly adopting AI agents to automate customer support, code generation, data analysis, and workflow orchestration. The results serve as a reality check: the agent framework is not a commodity. It is a performance-critical component that can make or break both the economics and the reliability of an AI-powered system.
These findings also highlight the growing importance of independent, real-world benchmarking. As AI companies race to release new frameworks — often with bold claims about speed and accuracy — third-party evaluations like this one provide the data that developers need to make informed decisions. Composio’s test is particularly valuable because it uses actual tools rather than synthetic tasks, and because it isolates the framework variable by using the same model throughout.
Looking ahead, the gap between frameworks is likely to narrow as the market matures. Open-source frameworks like OpenCode and Oh My Pi will continue to improve, while commercial offerings like Claude Code and Codex will face pressure to justify their premium pricing with demonstrable advantages. The winners will be the users who can navigate this complexity with clear metrics and a willingness to test each framework against their own specific workloads.
For now, the data is clear: the cheapest framework costs about one-third of the most expensive one, and the difference is not just about model choice. The agent wrapper itself is a cost center that deserves as much scrutiny as the underlying LLM. Developers who treat it as an afterthought risk paying far more than they need to — or settling for far less performance than they could achieve.
