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Head to head: Sakana: Fugu Ultra vs GLM 5.2 - RuntimeWire

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The topline is decisive: Fugu Ultra wins 107.9 to 96.9, with 91% confidence . That matches the task count too — 6 wins to 2, with 4 ties — and the pattern matters more than the raw margin. Fugu Ultra didn’t just edge ahead on style points; it repeatedly produced answers that were more robust, more faithful to the prompt, and less likely to break in real use.

The clearest separation showed up in code and technical reasoning. Fugu Ultra was better on the Python cache bug, the JavaScript group-by-day task, and the dense-passage summary because it handled edge cases and underlying mechanics more cleanly. It also took meeting-notes summarization and precise proofreading by being more complete and more exact. That’s the profile of a model that tends to do the unglamorous but important work correctly: sorting logic, UTC handling, faithful compression, and format-sensitive edits.

GLM 5.2 did earn real wins, but they were narrower. It was better on the localization task, where its European Spanish toast copy sounded more natural and mobile-friendly, and on the constraint-scheduling puzzle, where it explained the uniqueness of the solution more convincingly. Those are legitimate strengths — fluency and tidy justification — but they weren’t enough to offset Fugu Ultra’s broader advantage on higher-stakes execution.

The ties don’t rescue GLM. On LRU cache and warehouse reasoning, the judging notes themselves show some inconsistency across passes, which is exactly why they ended as ties rather than meaningful points for either side. On contradiction-finding and SQL, both models were simply good enough. Strip those out, and the remaining signal still points one way: Fugu Ultra was the stronger, more dependable model across this test set.

Final call: Sakana: Fugu Ultra is the better text model here, and this is a clear win — not a squeaker. GLM 5.2 has nicer touch in a couple of language-heavy moments, but Fugu Ultra is the one I’d trust more across mixed real-world workloads.

How they were tested

We ran 12 fresh text tasks, generated on the fly for this matchup so neither model could prepare in advance, and had OpenAI: GPT-5.6 Sol score each one. To cancel position bias, every task was judged twice — once in each presentation order — and every number reported here, including the headline totals, is the average of both passes. Sakana: Fugu Ultra scored 107.9 to GLM 5.2's 96.9.

1. Localization with tone

Translate this app onboarding line into natural, friendly European Spanish suitable for a mobile toast (keep it under 60 characters, no exclamation marks): "You're all set — your first backup starts tonight." Return only the translation, then the character count in parentheses.

Winner: GLM 5.2 — Model B sounds more natural and friendly for a mobile toast in European Spanish while staying concise and within the limit. Model A is accurate, but "copia de seguridad" feels heavier and less toast-like than Model B’s smoother phrasing. (Second judge pass, order swapped — scores are the average of both: Model B is more natural and toast-friendly in European Spanish, with a concise, friendly tone and an accurate character count under the limit. Model A is also correct and within length, but "Ya está" sounds slightly less idiomatic here and "copia de seguridad" is bulkier for a mobile toast.)

2. python-cache-bug

Debugging & code review (Python): The function should return the top k product IDs by total quantity sold, sorted by quantity descending then product ID ascending. It sometimes returns duplicates or misses products. Find the bug, fix it, and return the corrected code only. python from collections import defaultdict def top_k_products(lines, k): totals = defaultdict(int) for product_id, qty in lines: totals[product_id] += qty ranked = [] for product_id, total in totals.items(): ranked.append((total, product_id)) ranked.sort(reverse=True) if len(ranked) >= k: ranked = ranked[:k-1] return [product_id for total, product_id in ranked] # Example: # lines = [("P7", 2), ("P2", 5), ("P7", 4), ("P3", 5), ("P2", 1)] # expected top_k_products(lines, 2) == ["P2", "P7"]

Winner: Sakana: Fugu Ultra — Model A fully fixes the bug and returns the correct ordering by total descending then product ID ascending, while also handling nonpositive k safely. Model B fixes the truncation bug but keeps an incremental sort approach and does not handle kPractical coding (JavaScript): Write a function groupErrorsByDay(events) that takes an array of objects like { ts: "2025-03-14T23:58:02Z", service: "billing", level: "error" } and returns an...