Not all code generation benchmarks test the same things. We built a taxonomy of task types and mapped coverage across six major benchmarks to show where the gaps are.
Before building our own code evaluation task pool, we spent time understanding what existing benchmarks actually test. The answer, when you map it out systematically, is narrower than the benchmarks imply. Most published code generation benchmarks cluster heavily around two task types: algorithm implementation and data structure manipulation. The coverage gets thinner quickly as you move toward tasks that represent common real-world software development work.
This post describes the taxonomy we built and what the coverage map looked like. It explains the design decisions we made in our task pool based on that analysis, and it is honest about the task types that are hard to benchmark well regardless of effort.
Building the taxonomy
We started by collecting 480 coding tasks from real software development contexts: pull request descriptions from public open source repositories, Stack Overflow questions with accepted answers, internal engineering ticket samples, and documentation for common developer tools. We chose real tasks rather than constructed examples because we wanted the taxonomy to be grounded in what engineers actually do rather than what benchmark designers typically test.
From those 480 tasks, two of us independently assigned each task to categories we generated bottom-up from the data. We reconciled independently generated categories and arrived at a 14-category taxonomy. The categories with the most tasks from the real-world collection were: algorithm implementation (74 tasks), data structure manipulation (61 tasks), string and text processing (54 tasks), debugging and error diagnosis (52 tasks), API integration and client code (48 tasks), database query and schema operations (42 tasks), and configuration and infrastructure code (38 tasks). The remaining categories each had fewer than 30 tasks but represent distinct problem types that appear regularly in real codebases.
Mapping coverage across six benchmarks
We mapped each of the six benchmarks we analyzed against our 14-category taxonomy, classifying every task in each benchmark using the same categories. The coverage map was skewed in ways we expected to find but not to this degree.
Four of the six benchmarks had more than 70 percent of their tasks in two categories: algorithm implementation and data structure manipulation. One had more than 85 percent in those two categories. The remaining twelve categories of our taxonomy were covered at less than 10 percent of tasks in most benchmarks.
Debugging and error diagnosis was the most striking gap. In our real-world task sample, debugging tasks represented 10.8 percent of all tasks (52 out of 480). Across the six benchmarks we mapped, debugging tasks represented less than 2 percent of all tasks. If you select a model based on its score on these benchmarks and then deploy it for debugging assistance in a production codebase, you are extrapolating from a benchmark that barely tested the thing you care about.
API integration and client code had a similar gap. Modern software development involves substantial amounts of code that calls external APIs: authentication flows, request construction, response parsing, error handling for network failures. This is not algorithmically interesting work, which is probably why it is underrepresented in benchmarks. But it is a large fraction of what developers write and where models are increasingly used. Coverage of this category in the benchmarks we analyzed ranged from 0 to 4 percent.
Database query and schema operations had inconsistent coverage: one benchmark covered it at 12 percent, the others at less than 3 percent. The one exception was a benchmark specifically designed for SQL generation, which obviously overrepresented this category. The point is that benchmark coverage shapes what models optimize for, and if you care about SQL generation, the benchmark you use to evaluate models matters.
What we built in response
We designed our task pool to match the category distribution of our real-world task sample rather than the distribution in existing benchmarks. This means our task pool is less concentrated in algorithm and data structure tasks and more distributed across the full 14-category taxonomy. We still have substantial algorithm and data structure coverage because these categories genuinely are important; we just do not let them crowd out the rest.
The specific category changes relative to typical benchmarks: we increased debugging task coverage to 9 percent of our pool (roughly 4-5x the typical benchmark rate), increased API integration tasks to 8 percent, and increased database operations to 7 percent. We reduced algorithm implementation from its typical benchmark concentration of 40-50 percent to approximately 25 percent in our pool.
This changes the scores. A model that has been specifically optimized for algorithm benchmarks will score differently on our task pool than on a standard algorithm-heavy benchmark. Whether that difference is a model improvement or a benchmark difference depends on which task distribution better represents the work you are trying to do. We think our distribution is more representative of practical software development. We cannot claim it is universally better, because "better" depends on use case.
Three categories that are hard to benchmark automatically
Several categories in our taxonomy are genuinely hard to evaluate without human review, and we want to be transparent about why we have not included them.
Code refactoring and architecture improvement is the most obvious gap. Refactoring tasks require evaluating whether the transformed code is better in ways that are hard to operationalize: more readable, better structured, more maintainable. Pass-rate-style evaluation does not work because correctly refactored code and incorrectly refactored code can both pass the same test suite. Our quality dimension work (described in a separate article) is a partial answer, but we have not built reliable automated scoring for refactoring tasks yet.
Security code review requires evaluating code for vulnerabilities that are not present in obvious test cases. You can have a vulnerability scanner as part of the scoring pipeline, but you then measure whether the model avoids patterns that static analysis catches, not whether the model actually produces secure code. Models can learn to avoid static analysis flags while still producing code with logical vulnerabilities that are harder to detect. We flag common anti-patterns in our quality dimension, but this is not a security code review benchmark and we do not claim it is.
Technical documentation writing is in our real-world task taxonomy (developers write docstrings, READMEs, and API references constantly) but is not in our benchmark because automated quality evaluation for technical prose is an unsolved problem at the level of precision we want. We could use a language model to evaluate model-generated documentation, but that introduces a model-judging-model circularity that we are not comfortable with for a leaderboard that claims objectivity.
Using the coverage map for benchmark selection
The practical application of this analysis is benchmark selection. If you are evaluating models for a specific use case, you should understand which task types your use case involves and check whether the benchmark you are using covers them. A high pass rate on an algorithm-heavy benchmark does not predict performance on debugging tasks. Our coverage map is available on request for teams doing this analysis. We also provide per-category subscores through the API for all models we evaluate, which lets you see model performance specifically in the categories relevant to your work rather than only the aggregate score.
