Real-SWE attempts to measure how well AI models work with real enterprise codebases, rather than merely solving tasks in public benchmarks. Its results therefore reflect real-world work more closely, such as understanding legacy systems, fixing bugs, and modifying code with complex context.
However, the scores must still be interpreted carefully. Code is private data, making verification and reproduction difficult. In addition, each organization uses different languages, tools, and coding conventions. Results from one test set therefore do not mean that a model will perform well on every real-world system.
Real-SWE attempts to measure how well AI models work with real enterprise codebases, rather than merely solving tasks in public benchmarks. Its results therefore reflect real-world work more closely, such as understanding legacy systems, fixing bugs, and modifying code with complex context.
However, the scores must still be interpreted carefully. Code is private data, making verification and reproduction difficult. In addition, each organization uses different languages, tools, and coding conventions. Results from one test set therefore do not mean that a model will perform well on every real-world system.
Real-SWE Measures What Earlier Benchmarks Could Not See
Real-SWE is a benchmark that tests models on real enterprise codebases containing legacy systems, internal rules, and context that may not be fully documented in a single file. Its goal is not merely to see whether a model can write code that passes, but to assess how well it understands work within a real system.
Using real codebases reveals tasks that public datasets often hide, such as tracing root causes across multiple areas, reading code written by others, and fixing problems without breaking other components. When evaluated only on public data, a model may score well by relying on familiar patterns while still struggling with unfamiliar systems and organization-specific constraints.
Real-SWE Measures What Earlier Benchmarks Could Not See
Real-SWE is a benchmark that tests models on real enterprise codebases containing legacy systems, internal rules, and context that may not be fully documented in a single file. Its goal is not merely to see whether a model can write code that passes, but to assess how well it understands work within a real system.
Using real codebases reveals tasks that public datasets often hide, such as tracing root causes across multiple areas, reading code written by others, and fixing problems without breaking other components. When evaluated only on public data, a model may score well by relying on familiar patterns while still struggling with unfamiliar systems and organization-specific constraints.
From an Issue to the Bigger Picture of an Enterprise Codebase
Real-SWE starts with an issue that occurred in a real system and asks the model to read a private codebase to identify relevant areas, analyze the root cause, and propose a fix. This reflects developers’ work more closely than solving a single-file task, because every change must avoid affecting other parts of the system.
After the code is modified, the system runs tests to verify that the problem has actually been fixed and that existing functionality still works. This diagram therefore helps show the path from the task to a verifiable result.
From an Issue to the Bigger Picture of an Enterprise Codebase
Real-SWE starts with an issue that occurred in a real system and asks the model to read a private codebase to identify relevant areas, analyze the root cause, and propose a fix. This reflects developers’ work more closely than solving a single-file task, because every change must avoid affecting other parts of the system.
After the code is modified, the system runs tests to verify that the problem has actually been fixed and that existing functionality still works. This diagram therefore helps show the path from the task to a verifiable result.
The Day AI Writes a Passing Patch but the Team Still Does Not Dare to Merge It
A model may score well on a benchmark and write a patch that looks correct in the target file. But when it encounters legacy code and multiple dependency layers, it may fail to see hidden effects in the real system.
Teams therefore do not dare to merge immediately, because internal code often contains exceptions, approval rules, and security constraints that are absent from benchmark tasks. A patch passing tests does not mean it meets the organization’s standards; a person must always review the context and risks first.
The Day AI Writes a Passing Patch but the Team Still Does Not Dare to Merge It
A model may score well on a benchmark and write a patch that looks correct in the target file. But when it encounters legacy code and multiple dependency layers, it may fail to see hidden effects in the real system.
Teams therefore do not dare to merge immediately, because internal code often contains exceptions, approval rules, and security constraints that are absent from benchmark tasks. A patch passing tests does not mean it meets the organization’s standards; a person must always review the context and risks first.
Where Real-SWE Fits on the Software Engineering Benchmark Map
If general model benchmarks measure answering questions or writing code from prepared tasks, and open-source benchmarks measure issue resolution in repositories available for inspection, Real-SWE moves toward evaluating work in private enterprise codebases.
The key point is that the model must understand existing structures, internal rules, dependencies, and enterprise system context while solving problems without affecting other components. This reflects the work of real development teams more closely, although the evaluation also faces limitations around confidentiality and data disclosure.
Where Real-SWE Fits on the Software Engineering Benchmark Map
If general model benchmarks measure answering questions or writing code from prepared tasks, and open-source benchmarks measure issue resolution in repositories available for inspection, Real-SWE moves toward evaluating work in private enterprise codebases.
The key point is that the model must understand existing structures, internal rules, dependencies, and enterprise system context while solving problems without affecting other components. This reflects the work of real development teams more closely, although the evaluation also faces limitations around confidentiality and data disclosure.
From SWE-bench to Real-SWE: What Changed
| Factor | SWE-bench | Real-SWE |
|---|---|---|
| Code source | Public repository | Private enterprise codebase |
| Privacy | Can be disclosed | Access must be controlled |
| Task type | Fix a specified issue | Solve problems in a real system |
| Risk of data leakage | Lower | Requires greater caution |
| Risk of training-data contamination | May be detectable in training data | Reduced by using undisclosed data |
| Similarity to enterprise work | Partially similar | Reflects organizational context more closely |
SWE-bench is suitable for measuring capabilities on tasks that can be independently reproduced. Real-SWE adds challenges involving access permissions, internal rules, dependencies, and effects on existing systems, making it closer to enterprise work. However, evaluation is more difficult because confidential data must be protected at the same time.
From SWE-bench to Real-SWE: What Changed
| Factor | SWE-bench | Real-SWE |
|---|---|---|
| Code source | Public repository | Private enterprise codebase |
| Privacy | Can be disclosed | Access must be controlled |
| Task type | Fix a specified issue | Solve problems in a real system |
| Risk of data leakage | Lower | Requires greater caution |
| Risk of training-data contamination | May be detectable in training data | Reduced by using undisclosed data |
| Similarity to enterprise work | Partially similar | Reflects organizational context more closely |
SWE-bench is suitable for measuring capabilities on tasks that can be independently reproduced. Real-SWE adds challenges involving access permissions, internal rules, dependencies, and effects on existing systems, making it closer to enterprise work. However, evaluation is more difficult because confidential data must be protected at the same time.
When the Same Task Has to Face the Real World of Systems
Real-SWE is suitable for evaluating models before deploying them to internal repositories because the task does not end with modifying a single file. The model must also handle legacy code, dependencies, and organizational access rules.
When a bug affects multiple files or services, this evaluation helps determine how well the model can trace the effects of its changes. Teams can also use it to compare models without exposing the actual source code outside the system, giving a better view of suitability for enterprise work than looking at scores alone.
When the Same Task Has to Face the Real World of Systems
Real-SWE is suitable for evaluating models before deploying them to internal repositories because the task does not end with modifying a single file. The model must also handle legacy code, dependencies, and organizational access rules.
When a bug affects multiple files or services, this evaluation helps determine how well the model can trace the effects of its changes. Teams can also use it to compare models without exposing the actual source code outside the system, giving a better view of suitability for enterprise work than looking at scores alone.
Real-SWE Compared with Competitors Trying to Make Benchmarks More Realistic
| Factor | Real-SWE | SWE-bench Verified / SWE-Bench Pro |
|---|---|---|
| Privacy | Runs on an organization’s private codebase | Relies more on publicly available tasks and repositories |
| Task difficulty | Bugs spanning files, services, and access rules | Focuses on issues and task-specific code fixes |
| Codebase size and type | Real enterprise systems with dependencies and internal context | A defined set of benchmark repositories |
| Evaluation method | Examines effects on the system and organizational conditions | Uses tests and benchmark criteria |
Real-SWE’s strength is measuring whether a model can work under real constraints, rather than merely fixing tests to make them pass. It is suitable for organizations that want to select models that fit their internal systems and security policies.
Real-SWE Compared with Competitors Trying to Make Benchmarks More Realistic
| Factor | Real-SWE | SWE-bench Verified / SWE-Bench Pro |
|---|---|---|
| Privacy | Runs on an organization’s private codebase | Relies more on publicly available tasks and repositories |
| Task difficulty | Bugs spanning files, services, and access rules | Focuses on issues and task-specific code fixes |
| Codebase size and type | Real enterprise systems with dependencies and internal context | A defined set of benchmark repositories |
| Evaluation method | Examines effects on the system and organizational conditions | Uses tests and benchmark criteria |
Real-SWE’s strength is measuring whether a model can work under real constraints, rather than merely fixing tests to make them pass. It is suitable for organizations that want to select models that fit their internal systems and security policies.
Strengths That Make the Results More Credible—and Weaknesses That Still Require Caution
Real-SWE is closer to real software development work, helping reduce the risk of measuring models with tasks they may have seen before and revealing capabilities that public benchmarks do not fully assess.
Its limitation is that reproduction can be difficult because each organization’s codebase and conditions are different. The dataset is also limited in scope, so results may vary depending on the system, tools, and internal policies.
Pros
- +More closely reflects work with real codebases
- +Reveals model limitations in an organizational context
Cons
- −Difficult to reproduce and compare results
- −Results depend on each organization’s specific characteristics
Strengths That Make the Results More Credible—and Weaknesses That Still Require Caution
Real-SWE is closer to real software development work, helping reduce the risk of measuring models with tasks they may have seen before and revealing capabilities that public benchmarks do not fully assess.
Its limitation is that reproduction can be difficult because each organization’s codebase and conditions are different. The dataset is also limited in scope, so results may vary depending on the system, tools, and internal policies.
Pros
- +More closely reflects work with real codebases
- +Reveals model limitations in an organizational context
Cons
- −Difficult to reproduce and compare results
- −Results depend on each organization’s specific characteristics
The Price That Does Not Appear in the Benchmark Score
Private benchmarking carries hidden costs, starting with data screening, obtaining permission to use code, and redacting sensitive information before creating an environment that resembles real work. The more complex the system, the higher the cost of running models and the engineering time required for human review of patches.
The score therefore does not reflect the full cost of maintenance. Teams must also allow time to update data, fix the environment, and continuously review access permissions.
Pros
- +Shows testing costs in a real-world context
- +Helps assess readiness before organizational deployment
Cons
- −Requires significant engineering time and resources
- −Data redaction may reduce the realism of the test set
The Price That Does Not Appear in the Benchmark Score
Private benchmarking carries hidden costs, starting with data screening, obtaining permission to use code, and redacting sensitive information before creating an environment that resembles real work. The more complex the system, the higher the cost of running models and the engineering time required for human review of patches.
The score therefore does not reflect the full cost of maintenance. Teams must also allow time to update data, fix the environment, and continuously review access permissions.
Pros
- +Shows testing costs in a real-world context
- +Helps assess readiness before organizational deployment
Cons
- −Requires significant engineering time and resources
- −Data redaction may reduce the realism of the test set
Before Trusting the Score, Ask How Well This Benchmark Represents Our Work
Real-SWE scores should be treated as signals, not final answers, because each organization uses different programming languages, architectures, and testing systems.
If your codebase has stricter security constraints, access permissions, or review procedures than the test set, real-world results may differ substantially. You should also examine whether the benchmark measures the workflow your team actually uses, such as fixing problems in legacy systems, running tests, and submitting work through approval procedures.
Therefore, use Real-SWE to compare model trends, then retest with your own organization’s tasks and policies before making a decision.
Before Trusting the Score, Ask How Well This Benchmark Represents Our Work
Real-SWE scores should be treated as signals, not final answers, because each organization uses different programming languages, architectures, and testing systems.
If your codebase has stricter security constraints, access permissions, or review procedures than the test set, real-world results may differ substantially. You should also examine whether the benchmark measures the workflow your team actually uses, such as fixing problems in legacy systems, running tests, and submitting work through approval procedures.
Therefore, use Real-SWE to compare model trends, then retest with your own organization’s tasks and policies before making a decision.
From Scores on Paper to Decision Criteria for Engineering Teams
A good benchmark should not merely answer which model is the best. It should help teams determine which model is the best fit for their codebase, risk level, and organizational workflow.
Real-SWE is useful because it evaluates work from real enterprise codebases, making the gap between public scores and real-world use clearer. However, its results must still be considered alongside limitations involving confidentiality, reproducibility, and the diversity of the test set. Ultimately, a benchmark should be used as the starting point for a decision, not the final answer.
From Scores on Paper to Decision Criteria for Engineering Teams
A good benchmark should not merely answer which model is the best. It should help teams determine which model is the best fit for their codebase, risk level, and organizational workflow.
Real-SWE is useful because it evaluates work from real enterprise codebases, making the gap between public scores and real-world use clearer. However, its results must still be considered alongside limitations involving confidentiality, reproducibility, and the diversity of the test set. Ultimately, a benchmark should be used as the starting point for a decision, not the final answer.
Real-SWE Measures What Earlier Benchmarks Could Not See
Real-SWE is a benchmark that uses tasks from private codebases and real enterprise systems to test AI models. It examines how well models understand code structures, solve problems, and work under real-world constraints.
The key reason is that enterprise codebases often contain legacy code, multiple dependency layers, and internal rules that are not explained in a short task description. Public datasets alone may therefore make a model appear more capable than it is in real use, because the context is well prepared and does not reflect tasks requiring problems to be traced across multiple parts of a system.
Real-SWE Measures What Earlier Benchmarks Could Not See
Real-SWE is a benchmark that uses tasks from private codebases and real enterprise systems to test AI models. It examines how well models understand code structures, solve problems, and work under real-world constraints.
The key reason is that enterprise codebases often contain legacy code, multiple dependency layers, and internal rules that are not explained in a short task description. Public datasets alone may therefore make a model appear more capable than it is in real use, because the context is well prepared and does not reflect tasks requiring problems to be traced across multiple parts of a system.
From an Issue to the Bigger Picture of an Enterprise Codebase
Real-SWE starts with an issue that occurred in a real system and asks the model to trace the problem through a private codebase containing a mixture of legacy code, dependencies, and internal rules. The model must then propose a patch or modify the code to fit the system’s context.
The result is not judged only by whether the code looks correct. It must also pass tests without breaking other parts of the system. This brings the benchmark closer to the work of real development teams.
From an Issue to the Bigger Picture of an Enterprise Codebase
Real-SWE starts with an issue that occurred in a real system and asks the model to trace the problem through a private codebase containing a mixture of legacy code, dependencies, and internal rules. The model must then propose a patch or modify the code to fit the system’s context.
The result is not judged only by whether the code looks correct. It must also pass tests without breaking other parts of the system. This brings the benchmark closer to the work of real development teams.
The Day AI Writes a Passing Patch but the Team Still Does Not Dare to Merge It
A model may score well on a benchmark and write a patch that looks correct. But when it encounters internal code, it may not understand the context of legacy systems, tightly coupled dependency layers, or the rationale behind certain parts of the code.
Teams therefore still do not dare to merge immediately. Someone must review the effects on the system and check the organization’s rules, because a patch that passes a test in one area may conflict with the team’s security standards or deployment process.
The Day AI Writes a Passing Patch but the Team Still Does Not Dare to Merge It
A model may score well on a benchmark and write a patch that looks correct. But when it encounters internal code, it may not understand the context of legacy systems, tightly coupled dependency layers, or the rationale behind certain parts of the code.
Teams therefore still do not dare to merge immediately. Someone must review the effects on the system and check the organization’s rules, because a patch that passes a test in one area may conflict with the team’s security standards or deployment process.
Where Real-SWE Fits on the Software Engineering Benchmark Map
Real-SWE measures AI capabilities in enterprise work that requires understanding private codebases, system context, and organizational constraints. It does not merely assess whether a model can write code according to a task description.
If general benchmarks focus on foundational capabilities and open-source issue resolution focuses on patches that can be inspected through public code, Real-SWE emphasizes undisclosed data and more complex dependencies. It therefore reflects real-world use by software teams more closely.
Where Real-SWE Fits on the Software Engineering Benchmark Map
Real-SWE measures AI capabilities in enterprise work that requires understanding private codebases, system context, and organizational constraints. It does not merely assess whether a model can write code according to a task description.
If general benchmarks focus on foundational capabilities and open-source issue resolution focuses on patches that can be inspected through public code, Real-SWE emphasizes undisclosed data and more complex dependencies. It therefore reflects real-world use by software teams more closely.
From SWE-bench to Real-SWE: What Changed
SWE-bench uses issues and code from inspectable open-source projects, while Real-SWE uses real private codebases with more complex dependencies. It therefore requires consideration of both security and organizational constraints.
| Factor | SWE-bench | Real-SWE |
|---|---|---|
| Code source | Open source | Private enterprise codebase |
| Privacy | Publicly disclosed | Access restricted |
| Task type | Fix issues according to a task description | Solve problems in real systems with complex dependencies |
| Risk of data leakage | Lower | Higher |
| Risk of training-data contamination | Easier to investigate | Harder to investigate |
| Similarity to enterprise work | Partially similar | Closer to real-world use |
From SWE-bench to Real-SWE: What Changed
SWE-bench uses issues and code from inspectable open-source projects, while Real-SWE uses real private codebases with more complex dependencies. It therefore requires consideration of both security and organizational constraints.
| Factor | SWE-bench | Real-SWE |
|---|---|---|
| Code source | Open source | Private enterprise codebase |
| Privacy | Publicly disclosed | Access restricted |
| Task type | Fix issues according to a task description | Solve problems in real systems with complex dependencies |
| Risk of data leakage | Lower | Higher |
| Risk of training-data contamination | Easier to investigate | Harder to investigate |
| Similarity to enterprise work | Partially similar | Closer to real-world use |
When the Same Task Has to Face the Real World of Systems
Real-SWE is suitable for evaluating models before deploying them to internal repositories because its tasks use real code and more private data than general benchmarks. Teams can therefore gain a clearer view of readiness before real-world deployment.
When a bug occurs in legacy code with complex dependencies, or when its effects span multiple files and services, this type of evaluation measures understanding of the system as a whole rather than merely filling in code until tests pass.
For teams that must protect source-code confidentiality, Real-SWE can also compare models in a context close to real work without relying solely on public code samples.
When the Same Task Has to Face the Real World of Systems
Real-SWE is suitable for evaluating models before deploying them to internal repositories because its tasks use real code and more private data than general benchmarks. Teams can therefore gain a clearer view of readiness before real-world deployment.
When a bug occurs in legacy code with complex dependencies, or when its effects span multiple files and services, this type of evaluation measures understanding of the system as a whole rather than merely filling in code until tests pass.
For teams that must protect source-code confidentiality, Real-SWE can also compare models in a context close to real work without relying solely on public code samples.
Real-SWE Compared with Competitors Trying to Make Benchmarks More Realistic
| Factor | Real-SWE | SWE-bench Verified / SWE-Bench Pro |
|---|---|---|
| Privacy | Supports private codebases | Relies on public code |
| Task difficulty | Bugs and effects spanning systems | Issue-based tasks from a benchmark set |
| Codebase size and type | Enterprise codebases spanning multiple services | Open-source projects |
| Evaluation method | Tests against real systems and conditions | Evaluates patches and test suites |
| Suitability for enterprise | High | Suitable for baseline comparison |
Real-SWE’s strength is that it measures both problem-solving and the ability to preserve organizational context, making it more representative of real work when systems contain dependencies and confidentiality constraints. SWE-bench is better suited to standardized model comparisons, making results easier to view and reproduce.
Real-SWE Compared with Competitors Trying to Make Benchmarks More Realistic
| Factor | Real-SWE | SWE-bench Verified / SWE-Bench Pro |
|---|---|---|
| Privacy | Supports private codebases | Relies on public code |
| Task difficulty | Bugs and effects spanning systems | Issue-based tasks from a benchmark set |
| Codebase size and type | Enterprise codebases spanning multiple services | Open-source projects |
| Evaluation method | Tests against real systems and conditions | Evaluates patches and test suites |
| Suitability for enterprise | High | Suitable for baseline comparison |
Real-SWE’s strength is that it measures both problem-solving and the ability to preserve organizational context, making it more representative of real work when systems contain dependencies and confidentiality constraints. SWE-bench is better suited to standardized model comparisons, making results easier to view and reproduce.
Strengths That Make the Results More Credible—and Weaknesses That Still Require Caution
Real-SWE uses codebases and tasks from real work, making model capabilities in an organizational context clearer, including their handling of dependencies, confidentiality constraints, and work patterns that public benchmarks may not cover.
Pros
- +Reflects real organizational use
- +Reduces the risk of relying on data the model has seen before
- +Reveals capabilities that public benchmarks may not fully measure
Cons
- −Difficult to reproduce because the data is confidential
- −The dataset is limited in scope
- −Results may depend on each organization’s specific characteristics
Strengths That Make the Results More Credible—and Weaknesses That Still Require Caution
Real-SWE uses codebases and tasks from real work, making model capabilities in an organizational context clearer, including their handling of dependencies, confidentiality constraints, and work patterns that public benchmarks may not cover.
Pros
- +Reflects real organizational use
- +Reduces the risk of relying on data the model has seen before
- +Reveals capabilities that public benchmarks may not fully measure
Cons
- −Difficult to reproduce because the data is confidential
- −The dataset is limited in scope
- −Results may depend on each organization’s specific characteristics
The Price That Does Not Appear in the Benchmark Score
Benchmark scores do not include the time required to screen data, obtain permission to use code, and redact sensitive information before real testing. Costs also come from building the environment, running models, and having engineers manually review patches—work that must be repeated whenever the test set changes.
Pros
- +Produces test results close to an organization’s real work
- +Helps control data and usage-permission risks
Cons
- −Requires significant engineering time and resources
- −Model-running costs may increase with the number of tests
- −Data redaction may remove important code context
The Price That Does Not Appear in the Benchmark Score
Benchmark scores do not include the time required to screen data, obtain permission to use code, and redact sensitive information before real testing. Costs also come from building the environment, running models, and having engineers manually review patches—work that must be repeated whenever the test set changes.
Pros
- +Produces test results close to an organization’s real work
- +Helps control data and usage-permission risks
Cons
- −Requires significant engineering time and resources
- −Model-running costs may increase with the number of tests
- −Data redaction may remove important code context
Before Trusting the Score, Ask How Well This Benchmark Represents Our Work
Real-SWE scores should be treated as signals, not fixed answers, because programming languages, architectures, and codebase structures differ between organizations. A model that performs well on one system may not stand out when faced with a different testing system or set of constraints.
You should also examine how closely the benchmark simulates the real workflow, from data access and code review to deployment, as well as each team’s security policies and permissions. If these steps do not match your own, the score can be used to compare trends but should not be directly used to predict real-world outcomes.
Before Trusting the Score, Ask How Well This Benchmark Represents Our Work
Real-SWE scores should be treated as signals, not fixed answers, because programming languages, architectures, and codebase structures differ between organizations. A model that performs well on one system may not stand out when faced with a different testing system or set of constraints.
You should also examine how closely the benchmark simulates the real workflow, from data access and code review to deployment, as well as each team’s security policies and permissions. If these steps do not match your own, the score can be used to compare trends but should not be directly used to predict real-world outcomes.
From Scores on Paper to Decision Criteria for Engineering Teams
A good benchmark should not merely answer which model is the best. It should help teams determine which model is the best fit for their codebase, risk level, and organizational workflow.
Decision criteria should therefore combine correctness, problem-solving ability, and consistency in real-world work, while also considering security, data-access permissions, and the burden of reviewing code. Scores should serve as the starting point for selection and further experimentation, not as the final answer for every team.
From Scores on Paper to Decision Criteria for Engineering Teams
A good benchmark should not merely answer which model is the best. It should help teams determine which model is the best fit for their codebase, risk level, and organizational workflow.
Decision criteria should therefore combine correctness, problem-solving ability, and consistency in real-world work, while also considering security, data-access permissions, and the burden of reviewing code. Scores should serve as the starting point for selection and further experimentation, not as the final answer for every team. Real-SWE attempts to measure how well AI models work with real enterprise codebases, rather than merely solving tasks in public benchmarks. Its results therefore reflect real-world work more closely, such as understanding legacy systems, fixing bugs, and modifying code with complex context.
However, the scores must still be interpreted carefully. Code is private data, making verification and reproduction difficult. In addition, each organization uses different languages, tools, and coding conventions. Results from one test set therefore do not mean that a model will perform well on every real-world system.
Real-SWE attempts to measure how well AI models work with real enterprise codebases, rather than merely solving tasks in public benchmarks. Its results therefore reflect real-world work more closely, such as understanding legacy systems, fixing bugs, and modifying code with complex context.
However, the scores must still be interpreted carefully. Code is private data, making verification and reproduction difficult. In addition, each organization uses different languages, tools, and coding conventions. Results from one test set therefore do not mean that a model will perform well on every real-world system.
Real-SWE Measures What Earlier Benchmarks Could Not See
Real-SWE is a benchmark that tests models on real enterprise codebases containing legacy systems, internal rules, and context that may not be fully documented in a single file. Its goal is not merely to see whether a model can write code that passes, but to assess how well it understands work within a real system.
Using real codebases reveals tasks that public datasets often hide, such as tracing root causes across multiple areas, reading code written by others, and fixing problems without breaking other components. When evaluated only on public data, a model may score well by relying on familiar patterns while still struggling with unfamiliar systems and organization-specific constraints.
Real-SWE Measures What Earlier Benchmarks Could Not See
Real-SWE is a benchmark that tests models on real enterprise codebases containing legacy systems, internal rules, and context that may not be fully documented in a single file. Its goal is not merely to see whether a model can write code that passes, but to assess how well it understands work within a real system.
Using real codebases reveals tasks that public datasets often hide, such as tracing root causes across multiple areas, reading code written by others, and fixing problems without breaking other components. When evaluated only on public data, a model may score well by relying on familiar patterns while still struggling with unfamiliar systems and organization-specific constraints.
From an Issue to the Bigger Picture of an Enterprise Codebase
Real-SWE starts with an issue that occurred in a real system and asks the model to read a private codebase to identify relevant areas, analyze the root cause, and propose a fix. This reflects developers’ work more closely than solving a single-file task, because every change must avoid affecting other parts of the system.
After the code is modified, the system runs tests to verify that the problem has actually been fixed and that existing functionality still works. This diagram therefore helps show the path from the task to a verifiable result.
From an Issue to the Bigger Picture of an Enterprise Codebase
Real-SWE starts with an issue that occurred in a real system and asks the model to read a private codebase to identify relevant areas, analyze the root cause, and propose a fix. This reflects developers’ work more closely than solving a single-file task, because every change must avoid affecting other parts of the system.
After the code is modified, the system runs tests to verify that the problem has actually been fixed and that existing functionality still works. This diagram therefore helps show the path from the task to a verifiable result.
The Day AI Writes a Passing Patch but the Team Still Does Not Dare to Merge It
A model may score well on a benchmark and write a patch that looks correct in the target file. But when it encounters legacy code and multiple dependency layers, it may fail to see hidden effects in the real system.
Teams therefore do not dare to merge immediately, because internal code often contains exceptions, approval rules, and security constraints that are absent from benchmark tasks. A patch passing tests does not mean it meets the organization’s standards; a person must always review the context and risks first.
The Day AI Writes a Passing Patch but the Team Still Does Not Dare to Merge It
A model may score well on a benchmark and write a patch that looks correct in the target file. But when it encounters legacy code and multiple dependency layers, it may fail to see hidden effects in the real system.
Teams therefore do not dare to merge immediately, because internal code often contains exceptions, approval rules, and security constraints that are absent from benchmark tasks. A patch passing tests does not mean it meets the organization’s standards; a person must always review the context and risks first.
Where Real-SWE Fits on the Software Engineering Benchmark Map
If general model benchmarks measure answering questions or writing code from prepared tasks, and open-source benchmarks measure issue resolution in repositories available for inspection, Real-SWE moves toward evaluating work in private enterprise codebases.
The key point is that the model must understand existing structures, internal rules, dependencies, and enterprise system context while solving problems without affecting other components. This reflects the work of real development teams more closely, although the evaluation also faces limitations around confidentiality and data disclosure.
Where Real-SWE Fits on the Software Engineering Benchmark Map
If general model benchmarks measure answering questions or writing code from prepared tasks, and open-source benchmarks measure issue resolution in repositories available for inspection, Real-SWE moves toward evaluating work in private enterprise codebases.
The key point is that the model must understand existing structures, internal rules, dependencies, and enterprise system context while solving problems without affecting other components. This reflects the work of real development teams more closely, although the evaluation also faces limitations around confidentiality and data disclosure.
From SWE-bench to Real-SWE: What Changed
| Factor | SWE-bench | Real-SWE |
|---|---|---|
| Code source | Public repository | Private enterprise codebase |
| Privacy | Can be disclosed | Access must be controlled |
| Task type | Fix a specified issue | Solve problems in a real system |
| Risk of data leakage | Lower | Requires greater caution |
| Risk of training-data contamination | May be detectable in training data | Reduced by using undisclosed data |
| Similarity to enterprise work | Partially similar | Reflects organizational context more closely |
SWE-bench is suitable for measuring capabilities on tasks that can be independently reproduced. Real-SWE adds challenges involving access permissions, internal rules, dependencies, and effects on existing systems, making it closer to enterprise work. However, evaluation is more difficult because confidential data must be protected at the same time.
From SWE-bench to Real-SWE: What Changed
| Factor | SWE-bench | Real-SWE |
|---|---|---|
| Code source | Public repository | Private enterprise codebase |
| Privacy | Can be disclosed | Access must be controlled |
| Task type | Fix a specified issue | Solve problems in a real system |
| Risk of data leakage | Lower | Requires greater caution |
| Risk of training-data contamination | May be detectable in training data | Reduced by using undisclosed data |
| Similarity to enterprise work | Partially similar | Reflects organizational context more closely |
SWE-bench is suitable for measuring capabilities on tasks that can be independently reproduced. Real-SWE adds challenges involving access permissions, internal rules, dependencies, and effects on existing systems, making it closer to enterprise work. However, evaluation is more difficult because confidential data must be protected at the same time.
When the Same Task Has to Face the Real World of Systems
Real-SWE is suitable for evaluating models before deploying them to internal repositories because the task does not end with modifying a single file. The model must also handle legacy code, dependencies, and organizational access rules.
When a bug affects multiple files or services, this evaluation helps determine how well the model can trace the effects of its changes. Teams can also use it to compare models without exposing the actual source code outside the system, giving a better view of suitability for enterprise work than looking at scores alone.
When the Same Task Has to Face the Real World of Systems
Real-SWE is suitable for evaluating models before deploying them to internal repositories because the task does not end with modifying a single file. The model must also handle legacy code, dependencies, and organizational access rules.
When a bug affects multiple files or services, this evaluation helps determine how well the model can trace the effects of its changes. Teams can also use it to compare models without exposing the actual source code outside the system, giving a better view of suitability for enterprise work than looking at scores alone.
Real-SWE Compared with Competitors Trying to Make Benchmarks More Realistic
| Factor | Real-SWE | SWE-bench Verified / SWE-Bench Pro |
|---|---|---|
| Privacy | Runs on an organization’s private codebase | Relies more on publicly available tasks and repositories |
| Task difficulty | Bugs spanning files, services, and access rules | Focuses on issues and task-specific code fixes |
| Codebase size and type | Real enterprise systems with dependencies and internal context | A defined set of benchmark repositories |
| Evaluation method | Examines effects on the system and organizational conditions | Uses tests and benchmark criteria |
Real-SWE’s strength is measuring whether a model can work under real constraints, rather than merely fixing tests to make them pass. It is suitable for organizations that want to select models that fit their internal systems and security policies.
Real-SWE Compared with Competitors Trying to Make Benchmarks More Realistic
| Factor | Real-SWE | SWE-bench Verified / SWE-Bench Pro |
|---|---|---|
| Privacy | Runs on an organization’s private codebase | Relies more on publicly available tasks and repositories |
| Task difficulty | Bugs spanning files, services, and access rules | Focuses on issues and task-specific code fixes |
| Codebase size and type | Real enterprise systems with dependencies and internal context | A defined set of benchmark repositories |
| Evaluation method | Examines effects on the system and organizational conditions | Uses tests and benchmark criteria |
Real-SWE’s strength is measuring whether a model can work under real constraints, rather than merely fixing tests to make them pass. It is suitable for organizations that want to select models that fit their internal systems and security policies.
Strengths That Make the Results More Credible—and Weaknesses That Still Require Caution
Real-SWE is closer to real software development work, helping reduce the risk of measuring models with tasks they may have seen before and revealing capabilities that public benchmarks do not fully assess.
Its limitation is that reproduction can be difficult because each organization’s codebase and conditions are different. The dataset is also limited in scope, so results may vary depending on the system, tools, and internal policies.
Pros
- +More closely reflects work with real codebases
- +Reveals model limitations in an organizational context
Cons
- −Difficult to reproduce and compare results
- −Results depend on each organization’s specific characteristics
Strengths That Make the Results More Credible—and Weaknesses That Still Require Caution
Real-SWE is closer to real software development work, helping reduce the risk of measuring models with tasks they may have seen before and revealing capabilities that public benchmarks do not fully assess.
Its limitation is that reproduction can be difficult because each organization’s codebase and conditions are different. The dataset is also limited in scope, so results may vary depending on the system, tools, and internal policies.
Pros
- +More closely reflects work with real codebases
- +Reveals model limitations in an organizational context
Cons
- −Difficult to reproduce and compare results
- −Results depend on each organization’s specific characteristics
The Price That Does Not Appear in the Benchmark Score
Private benchmarking carries hidden costs, starting with data screening, obtaining permission to use code, and redacting sensitive information before creating an environment that resembles real work. The more complex the system, the higher the cost of running models and the engineering time required for human review of patches.
The score therefore does not reflect the full cost of maintenance. Teams must also allow time to update data, fix the environment, and continuously review access permissions.
Pros
- +Shows testing costs in a real-world context
- +Helps assess readiness before organizational deployment
Cons
- −Requires significant engineering time and resources
- −Data redaction may reduce the realism of the test set
The Price That Does Not Appear in the Benchmark Score
Private benchmarking carries hidden costs, starting with data screening, obtaining permission to use code, and redacting sensitive information before creating an environment that resembles real work. The more complex the system, the higher the cost of running models and the engineering time required for human review of patches.
The score therefore does not reflect the full cost of maintenance. Teams must also allow time to update data, fix the environment, and continuously review access permissions.
Pros
- +Shows testing costs in a real-world context
- +Helps assess readiness before organizational deployment
Cons
- −Requires significant engineering time and resources
- −Data redaction may reduce the realism of the test set
Before Trusting the Score, Ask How Well This Benchmark Represents Our Work
Real-SWE scores should be treated as signals, not final answers, because each organization uses different programming languages, architectures, and testing systems.
If your codebase has stricter security constraints, access permissions, or review procedures than the test set, real-world results may differ substantially. You should also examine whether the benchmark measures the workflow your team actually uses, such as fixing problems in legacy systems, running tests, and submitting work through approval procedures.
Therefore, use Real-SWE to compare model trends, then retest with your own organization’s tasks and policies before making a decision.
Before Trusting the Score, Ask How Well This Benchmark Represents Our Work
Real-SWE scores should be treated as signals, not final answers, because each organization uses different programming languages, architectures, and testing systems.
If your codebase has stricter security constraints, access permissions, or review procedures than the test set, real-world results may differ substantially. You should also examine whether the benchmark measures the workflow your team actually uses, such as fixing problems in legacy systems, running tests, and submitting work through approval procedures.
Therefore, use Real-SWE to compare model trends, then retest with your own organization’s tasks and policies before making a decision.
From Scores on Paper to Decision Criteria for Engineering Teams
A good benchmark should not merely answer which model is the best. It should help teams determine which model is the best fit for their codebase, risk level, and organizational workflow.
Real-SWE is useful because it evaluates work from real enterprise codebases, making the gap between public scores and real-world use clearer. However, its results must still be considered alongside limitations involving confidentiality, reproducibility, and the diversity of the test set. Ultimately, a benchmark should be used as the starting point for a decision, not the final answer.
From Scores on Paper to Decision Criteria for Engineering Teams
A good benchmark should not merely answer which model is the best. It should help teams determine which model is the best fit for their codebase, risk level, and organizational workflow.
Real-SWE is useful because it evaluates work from real enterprise codebases, making the gap between public scores and real-world use clearer. However, its results must still be considered alongside limitations involving confidentiality, reproducibility, and the diversity of the test set. Ultimately, a benchmark should be used as the starting point for a decision, not the final answer.
Real-SWE Measures What Earlier Benchmarks Could Not See
Real-SWE is a benchmark that uses tasks from private codebases and real enterprise systems to test AI models. It examines how well models understand code structures, solve problems, and work under real-world constraints.
The key reason is that enterprise codebases often contain legacy code, multiple dependency layers, and internal rules that are not explained in a short task description. Public datasets alone may therefore make a model appear more capable than it is in real use, because the context is well prepared and does not reflect tasks requiring problems to be traced across multiple parts of a system.
Real-SWE Measures What Earlier Benchmarks Could Not See
Real-SWE is a benchmark that uses tasks from private codebases and real enterprise systems to test AI models. It examines how well models understand code structures, solve problems, and work under real-world constraints.
The key reason is that enterprise codebases often contain legacy code, multiple dependency layers, and internal rules that are not explained in a short task description. Public datasets alone may therefore make a model appear more capable than it is in real use, because the context is well prepared and does not reflect tasks requiring problems to be traced across multiple parts of a system.
From an Issue to the Bigger Picture of an Enterprise Codebase
Real-SWE starts with an issue that occurred in a real system and asks the model to trace the problem through a private codebase containing a mixture of legacy code, dependencies, and internal rules. The model must then propose a patch or modify the code to fit the system’s context.
The result is not judged only by whether the code looks correct. It must also pass tests without breaking other parts of the system. This brings the benchmark closer to the work of real development teams.
From an Issue to the Bigger Picture of an Enterprise Codebase
Real-SWE starts with an issue that occurred in a real system and asks the model to trace the problem through a private codebase containing a mixture of legacy code, dependencies, and internal rules. The model must then propose a patch or modify the code to fit the system’s context.
The result is not judged only by whether the code looks correct. It must also pass tests without breaking other parts of the system. This brings the benchmark closer to the work of real development teams.
The Day AI Writes a Passing Patch but the Team Still Does Not Dare to Merge It
A model may score well on a benchmark and write a patch that looks correct. But when it encounters internal code, it may not understand the context of legacy systems, tightly coupled dependency layers, or the rationale behind certain parts of the code.
Teams therefore still do not dare to merge immediately. Someone must review the effects on the system and check the organization’s rules, because a patch that passes a test in one area may conflict with the team’s security standards or deployment process.
The Day AI Writes a Passing Patch but the Team Still Does Not Dare to Merge It
A model may score well on a benchmark and write a patch that looks correct. But when it encounters internal code, it may not understand the context of legacy systems, tightly coupled dependency layers, or the rationale behind certain parts of the code.
Teams therefore still do not dare to merge immediately. Someone must review the effects on the system and check the organization’s rules, because a patch that passes a test in one area may conflict with the team’s security standards or deployment process.
Where Real-SWE Fits on the Software Engineering Benchmark Map
Real-SWE measures AI capabilities in enterprise work that requires understanding private codebases, system context, and organizational constraints. It does not merely assess whether a model can write code according to a task description.
If general benchmarks focus on foundational capabilities and open-source issue resolution focuses on patches that can be inspected through public code, Real-SWE emphasizes undisclosed data and more complex dependencies. It therefore reflects real-world use by software teams more closely.
Where Real-SWE Fits on the Software Engineering Benchmark Map
Real-SWE measures AI capabilities in enterprise work that requires understanding private codebases, system context, and organizational constraints. It does not merely assess whether a model can write code according to a task description.
If general benchmarks focus on foundational capabilities and open-source issue resolution focuses on patches that can be inspected through public code, Real-SWE emphasizes undisclosed data and more complex dependencies. It therefore reflects real-world use by software teams more closely.
From SWE-bench to Real-SWE: What Changed
SWE-bench uses issues and code from inspectable open-source projects, while Real-SWE uses real private codebases with more complex dependencies. It therefore requires consideration of both security and organizational constraints.
| Factor | SWE-bench | Real-SWE |
|---|---|---|
| Code source | Open source | Private enterprise codebase |
| Privacy | Publicly disclosed | Access restricted |
| Task type | Fix issues according to a task description | Solve problems in real systems with complex dependencies |
| Risk of data leakage | Lower | Higher |
| Risk of training-data contamination | Easier to investigate | Harder to investigate |
| Similarity to enterprise work | Partially similar | Closer to real-world use |
From SWE-bench to Real-SWE: What Changed
SWE-bench uses issues and code from inspectable open-source projects, while Real-SWE uses real private codebases with more complex dependencies. It therefore requires consideration of both security and organizational constraints.
| Factor | SWE-bench | Real-SWE |
|---|---|---|
| Code source | Open source | Private enterprise codebase |
| Privacy | Publicly disclosed | Access restricted |
| Task type | Fix issues according to a task description | Solve problems in real systems with complex dependencies |
| Risk of data leakage | Lower | Higher |
| Risk of training-data contamination | Easier to investigate | Harder to investigate |
| Similarity to enterprise work | Partially similar | Closer to real-world use |
When the Same Task Has to Face the Real World of Systems
Real-SWE is suitable for evaluating models before deploying them to internal repositories because its tasks use real code and more private data than general benchmarks. Teams can therefore gain a clearer view of readiness before real-world deployment.
When a bug occurs in legacy code with complex dependencies, or when its effects span multiple files and services, this type of evaluation measures understanding of the system as a whole rather than merely filling in code until tests pass.
For teams that must protect source-code confidentiality, Real-SWE can also compare models in a context close to real work without relying solely on public code samples.
When the Same Task Has to Face the Real World of Systems
Real-SWE is suitable for evaluating models before deploying them to internal repositories because its tasks use real code and more private data than general benchmarks. Teams can therefore gain a clearer view of readiness before real-world deployment.
When a bug occurs in legacy code with complex dependencies, or when its effects span multiple files and services, this type of evaluation measures understanding of the system as a whole rather than merely filling in code until tests pass.
For teams that must protect source-code confidentiality, Real-SWE can also compare models in a context close to real work without relying solely on public code samples.
Real-SWE Compared with Competitors Trying to Make Benchmarks More Realistic
| Factor | Real-SWE | SWE-bench Verified / SWE-Bench Pro |
|---|---|---|
| Privacy | Supports private codebases | Relies on public code |
| Task difficulty | Bugs and effects spanning systems | Issue-based tasks from a benchmark set |
| Codebase size and type | Enterprise codebases spanning multiple services | Open-source projects |
| Evaluation method | Tests against real systems and conditions | Evaluates patches and test suites |
| Suitability for enterprise | High | Suitable for baseline comparison |
Real-SWE’s strength is that it measures both problem-solving and the ability to preserve organizational context, making it more representative of real work when systems contain dependencies and confidentiality constraints. SWE-bench is better suited to standardized model comparisons, making results easier to view and reproduce.
Real-SWE Compared with Competitors Trying to Make Benchmarks More Realistic
| Factor | Real-SWE | SWE-bench Verified / SWE-Bench Pro |
|---|---|---|
| Privacy | Supports private codebases | Relies on public code |
| Task difficulty | Bugs and effects spanning systems | Issue-based tasks from a benchmark set |
| Codebase size and type | Enterprise codebases spanning multiple services | Open-source projects |
| Evaluation method | Tests against real systems and conditions | Evaluates patches and test suites |
| Suitability for enterprise | High | Suitable for baseline comparison |
Real-SWE’s strength is that it measures both problem-solving and the ability to preserve organizational context, making it more representative of real work when systems contain dependencies and confidentiality constraints. SWE-bench is better suited to standardized model comparisons, making results easier to view and reproduce.
Strengths That Make the Results More Credible—and Weaknesses That Still Require Caution
Real-SWE uses codebases and tasks from real work, making model capabilities in an organizational context clearer, including their handling of dependencies, confidentiality constraints, and work patterns that public benchmarks may not cover.
Pros
- +Reflects real organizational use
- +Reduces the risk of relying on data the model has seen before
- +Reveals capabilities that public benchmarks may not fully measure
Cons
- −Difficult to reproduce because the data is confidential
- −The dataset is limited in scope
- −Results may depend on each organization’s specific characteristics
Strengths That Make the Results More Credible—and Weaknesses That Still Require Caution
Real-SWE uses codebases and tasks from real work, making model capabilities in an organizational context clearer, including their handling of dependencies, confidentiality constraints, and work patterns that public benchmarks may not cover.
Pros
- +Reflects real organizational use
- +Reduces the risk of relying on data the model has seen before
- +Reveals capabilities that public benchmarks may not fully measure
Cons
- −Difficult to reproduce because the data is confidential
- −The dataset is limited in scope
- −Results may depend on each organization’s specific characteristics
The Price That Does Not Appear in the Benchmark Score
Benchmark scores do not include the time required to screen data, obtain permission to use code, and redact sensitive information before real testing. Costs also come from building the environment, running models, and having engineers manually review patches—work that must be repeated whenever the test set changes.
Pros
- +Produces test results close to an organization’s real work
- +Helps control data and usage-permission risks
Cons
- −Requires significant engineering time and resources
- −Model-running costs may increase with the number of tests
- −Data redaction may remove important code context
The Price That Does Not Appear in the Benchmark Score
Benchmark scores do not include the time required to screen data, obtain permission to use code, and redact sensitive information before real testing. Costs also come from building the environment, running models, and having engineers manually review patches—work that must be repeated whenever the test set changes.
Pros
- +Produces test results close to an organization’s real work
- +Helps control data and usage-permission risks
Cons
- −Requires significant engineering time and resources
- −Model-running costs may increase with the number of tests
- −Data redaction may remove important code context
Before Trusting the Score, Ask How Well This Benchmark Represents Our Work
Real-SWE scores should be treated as signals, not fixed answers, because programming languages, architectures, and codebase structures differ between organizations. A model that performs well on one system may not stand out when faced with a different testing system or set of constraints.
You should also examine how closely the benchmark simulates the real workflow, from data access and code review to deployment, as well as each team’s security policies and permissions. If these steps do not match your own, the score can be used to compare trends but should not be directly used to predict real-world outcomes.
Before Trusting the Score, Ask How Well This Benchmark Represents Our Work
Real-SWE scores should be treated as signals, not fixed answers, because programming languages, architectures, and codebase structures differ between organizations. A model that performs well on one system may not stand out when faced with a different testing system or set of constraints.
You should also examine how closely the benchmark simulates the real workflow, from data access and code review to deployment, as well as each team’s security policies and permissions. If these steps do not match your own, the score can be used to compare trends but should not be directly used to predict real-world outcomes.
From Scores on Paper to Decision Criteria for Engineering Teams
A good benchmark should not merely answer which model is the best. It should help teams determine which model is the best fit for their codebase, risk level, and organizational workflow.
Decision criteria should therefore combine correctness, problem-solving ability, and consistency in real-world work, while also considering security, data-access permissions, and the burden of reviewing code. Scores should serve as the starting point for selection and further experimentation, not as the final answer for every team.
From Scores on Paper to Decision Criteria for Engineering Teams
A good benchmark should not merely answer which model is the best. It should help teams determine which model is the best fit for their codebase, risk level, and organizational workflow.
Decision criteria should therefore combine correctness, problem-solving ability, and consistency in real-world work, while also considering security, data-access permissions, and the burden of reviewing code. Scores should serve as the starting point for selection and further experimentation, not as the final answer for every team.