The Tool Desk
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Qwopus 3.6 27B was the stronger choice for the more demanding feature in this test, while both models handled a small regression fix successfully. On an AMD Radeon RX 7900 XT with 20 GB of VRAM, both ran fully locally and passed the same 87-test suite after the fix. When asked to add single-player mode to a JavaScript PWA, Qwopus produced a stronger strategy and more tests; Muse Glimmer 30B was more minimalist and finished that implementation faster in this run. These are results from one run per model per task—not a general ranking.
What did this local LLM coding test compare?
The test compared Qwopus 3.6 27B with Meta’s Muse Glimmer 30B on two real development tasks in Jeu de Cochons, a vanilla JavaScript progressive web app built with Vite and Vitest. Both models ran locally on an AMD Radeon RX 7900 XT with 20 GB of VRAM. The setup used GGUF model files, a llama.cpp multi-model router, an OpenAI-compatible endpoint and the pi CLI with high thinking. A third model, Codex, reviewed the resulting branches.
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The tasks were deliberately different in scope. The first was a bounded repair: fix a service-worker regression caused by a GitHub Pages base path. The second was a larger feature: add solo play against an AI opponent, including its menu option, bot identity, strategy, automatic turns, tests and a passing build. That contrast matters because success on a narrow patch does not establish how well a model handles design choices across a feature.
The author describes the result as “a data point, not a ranking of these models in general,” and notes that there was a single run per model per task rather than a statistical benchmark. Treat the findings as evidence about these models in this project and setup, not as a universal leaderboard.
#1 Best Overall
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How did the models perform on the service-worker fix?
They tied on the reported correctness checks and produced the same small code change, but Qwopus completed the task in less time in this run. The apparent tie also came with an important deployment caveat identified during review.
| Measure | Qwopus 3.6 27B | Muse Glimmer 30B |
|---|---|---|
| PWA tests | 11/11 passed | 11/11 passed |
| Full test suite | 87/87 passed | 87/87 passed |
| Files changed | 2 | 2 |
| Diff | Byte-identical +4/−4 change | Byte-identical +4/−4 change |
| Elapsed time | About 8.5 minutes | About 21 minutes |
Those elapsed times are the benchmark author’s measurements for this run, not a stable speed rating. They do not show that Qwopus will always finish faster: generation speed and total completion time can depend on the prompt, model configuration, hardware, and work required. The useful takeaway from this round is narrower: both models arrived at the same four-line net change and passed the same reported checks, while Qwopus took less time on this particular task.
Why was a passing test suite not the whole story?
The reviewer found a latent deployment conflict: reverting the manifest to a root path would break production when the app is deployed under /jeu-de-cochons/. That observation is a reminder that a green test suite only validates what the tests exercise. In this case, the fix needed to be judged against the app’s actual GitHub Pages base path as well as the local test results.
Because the two branches had a byte-identical diff, this round did not meaningfully distinguish the models on code style or implementation strategy. It did demonstrate that a small patch can look equally successful by automated checks while still requiring a reviewer to consider deployment context. The review finding should not be mistaken for evidence that either model independently made a different change: the reported diffs were identical.
What changed when the task required a full solo-play feature?
The feature task separated the implementations more clearly. Both models produced a passing build, but Qwopus’s version received stronger marks for strategy, architecture, robustness and tests, while Muse’s was judged more minimalist. The test totals also favored Qwopus in this run.
Rank #2
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| Measure | Qwopus 3.6 27B | Muse Glimmer 30B |
|---|---|---|
| New tests reported | 18 | 7 |
| Total tests reported | 102/105 passing | 91/94 passing |
| Build | Passed | Passed |
| Reviewer’s comparative assessment | Higher for strategy, architecture, robustness and tests | Higher for minimalism |
| Feature implementation time | Muse was faster in this run; exact elapsed times not stated | Faster in this run; exact elapsed times not stated |
The denominators in the reported totals differ, so read them as the outcomes of their respective branches, not as two runs against an explicitly identical final test count. Both builds passed, but neither reported total was fully green: Qwopus had 102 of 105 tests passing and Muse had 91 of 94 passing. The supplied results do not identify which tests accounted for the remaining failures, so they cannot support a claim that the failing cases were equivalent or equally serious.
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Qwopus used an adaptive threshold strategy and represented bot identity with an explicit isBot flag. Muse used a fixed threshold and inferred bot status from the localized display name. In the reviewer’s example, that could misclassify a human player named “Ordinateur,” because the display text is being used as a proxy for the underlying role.
The difference is a useful design distinction: identity data is more reliable when it is represented directly rather than inferred from text shown to a player. A localized name is presentation; it can change with language or collide with a human-chosen name. That does not make every explicit flag automatically correct, but it gives the program a clearer place to store the fact that a participant is a bot.
The review also noted that Muse’s deferred callback did not revalidate enough state before acting. A delayed action can become stale if the application state changes before the callback runs. The implementation observation points to a practical review question for turn-based or asynchronous features: does the action confirm that it is still valid when it executes, rather than only when it is scheduled? This is a finding from this branch, not a broad claim that Muse generally mishandles asynchronous code.
Which model is better for real coding work?
For this workload, Qwopus is the more convincing choice if the task involves feature design, explicit state modeling and test coverage. Muse remains competitive for bounded fixes and was more minimalist; the benchmark also reports that Muse completed the feature implementation faster in this run, without giving exact feature-task times.
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There is no single winner across every dimension in the two tasks. On the small regression fix, both passed all reported tests and made the same change. On the larger feature, the reviewer preferred Qwopus’s strategy and architecture, and Qwopus generated more tests, but neither branch ended with every reported test passing. If you care most about a compact implementation, Muse’s minimalism may be attractive; if you care more about the choices in this specific solo-play design, the review favors Qwopus.
The benchmark author’s observation that “On short tasks, these models are interchangeable” fits the regression round, but should be read narrowly. The fact that they produced the same result on one small repair does not prove they are interchangeable on other short tasks, and the feature round shows why task complexity can reveal differences that a small patch does not.
Do benchmark wins hold up on real repositories?
They are more informative when the test uses an actual repository, real project commands and a review of the resulting code, but one repository and two tasks still cannot establish how a model performs across software projects. A benchmark can demonstrate concrete behavior under its conditions; it cannot guarantee that the same model will make the same trade-offs in a different codebase.
Here, the app’s deployment base path mattered to the regression, and choices about bot identity, strategy and delayed turns mattered to the feature. Those are repository-level concerns that a score detached from the code might miss. Conversely, the results do not establish performance on other languages, frameworks, repository sizes or task types.
To judge a model for your own work, look beyond whether it can produce a compiling patch. Check whether it understands project-specific constraints, whether its tests exercise the behavior requested, whether the change fits the project’s existing architecture and whether delayed or edge-case behavior remains safe. A reviewer should also check what the passing tests leave untested, as the GitHub Pages path issue illustrates.
Rank #4
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Can a 20 GB GPU run both models locally?
In this reported setup, yes: both models were run fully locally on an AMD Radeon RX 7900 XT with 20 GB of VRAM. That is evidence for this particular combination of hardware, model files and configuration—not a guarantee that every 20 GB GPU can load every build of either model under every inference setting.
The test used GGUF files and a llama.cpp multi-model router. It does not state the exact quantization, context length, llama.cpp build, system RAM, or whether any layers were offloaded to system memory. Those details affect whether another machine can reproduce the same setup and performance, so the reported VRAM figure should not be treated as a complete compatibility specification.
Large GGUF files and 20 GB-class GPU memory were identified as the main physical constraints. If you are planning a local workstation, check the actual file size and memory behavior of the model file you intend to run, rather than relying on the nominal parameter count alone. The benchmark does not supply model-file sizes or exact memory usage for either model.
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What did the loading workaround change?
The author reports that --load-mode dio worked around a ROCm loading issue and loaded models 3–8× faster in that setup. This is a setup-specific observation, not a universal promise of faster loading or a guarantee that the option is appropriate for every ROCm system. The available data does not specify a baseline machine configuration or give a broader controlled comparison, so the reported multiplier should be kept attached to the author’s setup.
How can you reproduce a comparison without favoring one model?
A fair local comparison needs the models to face the same specification and the same project conditions, with verification that does not depend only on the model’s own description of its work. The documented reproduction path used isolated copies of the repository, separate branches, a shared task description, an objective verification script and cross-review.
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- Create two isolated git clones. Start from the same repository state so one model’s edits cannot affect the other model’s starting point.
- Give both models the identical markdown specification. Keep the requested behavior and acceptance conditions fixed; otherwise the prompts themselves become a variable in the comparison.
- Use separate branches. This makes it possible to inspect each patch independently and compare it with the same baseline.
- Run the same objective verification script on each branch. Record test results and build results separately; passing one does not imply the other passed.
- Review both branches across the same quality questions. Cross-review helps catch issues that automated tests may miss, including assumptions about deployment paths, state modeling and callbacks.
For a more informative local rerun, also record the exact model files and inference configuration, the repository commit, the prompt, the commands used for tests and builds, elapsed time, and the reviewer’s findings. The benchmark reports the 20 GB GPU and the broad software setup, but does not provide all of those configuration details, so an exact reproduction cannot be inferred from the reported results alone.
What should you take away from this comparison?
On a narrow service-worker regression, Qwopus and Muse Glimmer tied on the reported tests and produced identical diffs, with Qwopus finishing sooner in that run. On the larger solo-play feature, Qwopus made the stronger impression on strategy, architecture, robustness and test coverage, while Muse was more minimalist and faster to implement the feature in this run. Both builds passed, but the reported test totals show unresolved failures on each branch.
The practical conclusion is to use these results as a reason to test both models against representative work from your own repository, not as a universal model ranking. The benchmark’s own phrase, “The harness matters as much as the models,” is borne out by the value of isolated clones, identical instructions, objective verification and code review. A model’s apparent win is only as meaningful as the task and evaluation used to measure it.
Frequently Asked Questions
Does a 30B model automatically need more than 20 GB of VRAM?
Parameter count alone is not enough to determine a model’s runtime memory requirement. The model-file quantization and inference settings matter, and the reported comparison does not give enough configuration detail to calculate a universal VRAM requirement for either model.
Why can a code change pass tests but still be wrong in production?
A test suite checks the cases it contains, not every deployment environment or path. The service-worker review finding is an example of why production assumptions should be checked separately from a green test result.
Can I compare these results with benchmark scores from another project?
Only cautiously: different task definitions, repository conventions, inference settings and evaluation methods can change what a score means. This comparison is useful as a case study, but its one-run results are not standardized measurements that can be directly converted into another benchmark’s score.
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No. The tested work was in a vanilla JavaScript PWA, and the results do not establish performance in other languages or frameworks. Use representative tasks from the language and repository you actually work in to make that decision.
Quick Recap
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