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An AI coding-agent score is meaningful only in the execution environment that produced it. The sandbox determines which files and tools the agent can reach, whether it can change hidden configuration, and what network or credentials it can access. To compare results fairly, document those boundaries and keep them consistent across runs.
What the sandbox controls
A sandbox is an isolated, Unix-like execution environment. Depending on its configuration, it can provide a filesystem, shell, installed packages, mounted data, exposed ports, snapshots, and controlled access to external services. Those capabilities define what the agent can do during an evaluation, so a task description alone is not a complete benchmark specification. OpenAI’s sandbox documentation describes these kinds of environment components.
As OpenAI’s sandbox security documentation puts it: “Agent-generated code can access the files, credentials, and network available to its environment.” That is both a security boundary and an evaluation condition: changing what is available can change the opportunities and risks presented to the agent.
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Record the environment alongside the task and result. A useful report lets another evaluator understand what was available, what was restricted, and how a run began and ended.
#1 Best Overall
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- Operating system and image: identify the OS or execution image and how it is initialized.
- Workspace and mounted data: describe the repository contents, mounted paths, and whether the agent can read or write them.
- Tools and dependencies: list installed packages, shells, compilers, test tools, and other available commands.
- Network policy: state whether outbound connections are blocked or allowed, and identify permitted endpoints or domains.
- Credentials: explain whether credentials are available, where they are stored, and whether access is brokered or separated from the execution environment.
- Ports and snapshots: note exposed ports and whether snapshots or other saved states affect a run.
- Reset procedure: explain how the environment is restored between tasks or repetitions, including what happens to modified files and generated state.
Make filesystem scope explicit
“The agent can edit the repository” is not a precise boundary. A writable workspace may include hidden files, configuration, build scripts, and Git hooks as well as the source files targeted by the task. Docker’s documentation on sandbox security warns that its default mounted workspace can remain writable. For an evaluation, state which paths are readable and writable and whether files outside the intended edit area can be changed.
That distinction matters when interpreting a successful result: the evaluation should make clear whether the agent solved the task through the intended source change or had access to other writable project machinery. The sandbox description should cover the actual mounted workspace, not only the repository’s visible application code.
Rank #2
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- 90W Pass-Through Charging: Achieve optimal charging with 90W pass-through power to your laptop, supported by a total input of 100W, with the hub reserving 10W for operational efficiency. (Note: Wall charger not included.)
- Quick Data Transfers: Accelerate your productivity with rapid data transfers using a high-speed 5Gbps USB 3.0 port and two 480Mbps USB 2.0 ports.
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Separate network restrictions from credential controls
Network access and credentials are related but distinct controls. An agent might have no network access but still be able to read a credential mounted in its environment; conversely, network access may be limited to approved endpoints without exposing application secrets. Report both conditions rather than treating one as a substitute for the other.
OpenAI recommends restricting outbound access to approved endpoints, using isolated compute, and separating credentials from the execution environment. Its guidance specifically advises against placing an application API key inside that environment. For a benchmark, state the egress policy and credential placement so the configuration is both safer and interpretable.
Rank #3
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- Flawless 4K@60Hz Video with HDMI: Delivers exceptional clarity and smoothness with its 4K@60Hz HDMI port, making it ideal for high-definition presentations and entertainment. (Note: Only the HDMI port supports video projection; the USB-C port is for data transfer only.)
- Double Up on Efficiency: The two USB-A 3.0 ports and a USB-C port support a fast 5Gbps data rate, significantly boosting your transfer speeds and improving productivity.
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Describe the isolation model without overstating assurance
“Sandboxed” can refer to different technical boundaries. Docker describes its local sandboxes as microVMs with separate Linux kernels, and lists the hypervisor, network, Docker Engine, workspace, and credential proxy among the isolation layers in its security overview. Anthropic describes Claude Code sandboxing as a combination of filesystem and network controls, including configurable allowed paths and domains, in its sandboxing documentation.
These are vendor descriptions of their designs, not independent comparative security certifications. When describing an evaluation, name the isolation model and its relevant controls rather than implying that the word “sandbox” alone establishes a particular level of security.
Rank #4
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- Next-Level 10Gbps & 3A Charging: No more slow 480Mbps, this usb to usb c adapter has a transfer speed of up to 10Gbps, allowing you to do more transferring in less time. This usb adapter fits both USB A and USB C charger, supporting up to 3A fast charging
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Compare environments on the dimensions that affect runs
If an evaluation uses more than one environment, compare the configuration rather than assigning a universal ranking to the options. The dimensions below are useful for understanding what differs and what needs to stay fixed:
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| Dimension | What to report |
|---|---|
| Isolation boundary | Execution boundary and kernel model, including whether the design uses a separate kernel. |
| Filesystem scope | Workspace mounts, allowed paths, read/write permissions, and access to hidden files or project configuration. |
| Network egress | Whether outbound traffic is blocked, broadly allowed, or restricted to named endpoints or domains. |
| Credential handling | Whether credentials are present, where they reside, and whether a proxy or separate broker mediates access. |
| Packages and tools | Installed dependencies and commands available to the agent. |
| Reset and reproducibility | How runs are initialized, whether snapshots are used, and how state is cleared or restored. |
| Operational friction | What setup, access, or workflow constraints affect running the evaluation. |
OpenAI, Docker, and Anthropic documentation describe controls relevant to these dimensions, but the available documentation does not establish a universal ranking among providers. A comparison should therefore state the concrete configurations and trade-offs rather than declare one option categorically best.
Best Value
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Keep performance claims within the evidence
A sandbox can shape what an agent is able to do, but the cited sources do not provide a controlled estimate of how a particular sandbox choice changes coding-agent benchmark scores. Do not attribute a score difference to sandboxing unless the evaluation design isolates that variable. For comparisons, keep the environment fixed; if it changes, disclose the change and avoid claiming that it caused a performance difference without supporting evidence.
OpenAI announced SWE-bench Verified as a human-validated subset intended to make evaluation of real-world software issue solving more reliable. Its announcement reported leaderboard scores as of August 5, 2024; those figures are historical context, not current standings, and the announcement does not isolate sandbox configuration as an experimental variable. Read the SWE-bench Verified announcement for its stated scope and date.
Quick Recap
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