Head to head: Muse Spark 1.1 vs DeepSeek-V4-Pro

Muse Spark 1.1 vs DeepSeek-V4-Pro

This wasn’t a competitive split decision; it was a rout driven by instruction-following, robustness, and cleaner execution across a wide mix of real tasks. Muse Spark 1.1 consistently delivered answers that were tighter, more compliant, and less error-prone than DeepSeek-V4-Pro.

Muse Spark 1.1 wins this matchup outright. The aggregate score gap is massive — **110.8 to 84.3** — and the statistical verdict is as strong as it gets: **100% confidence, decisive**. Just as telling, the task count wasn’t close either: **11 wins for Muse Spark 1.1, 1 for DeepSeek-V4-Pro, no ties**. What stands out is *how* Muse Spark 1.1 won. It wasn’t just better at one niche; it was more dependable across localization, proofreading, JSON extraction, SQL, constrained writing, faithful rewriting, and code repair. In practical terms, Muse Spark 1.1 kept doing the unglamorous but essential things right: preserving exact wording when asked, returning raw JSON instead of fenced output, respecting tight formatting constraints, and avoiding brittle implementation mistakes in concurrency and cache logic. DeepSeek-V4-Pro’s problem here is not total incompetence; it’s sloppiness under pressure. Again and again, it lost on avoidable compliance failures: markdown fences where raw JSON was required, unnecessary wording changes in proofreading, extra reasoning where strict output format mattered, and less robust code paths in bug-fix tasks. Its lone win — unit-aware math — came on presentation, not on some deeper advantage in reasoning or correctness. There were a few order-swapped judge passes that flipped individual task rationales, which suggests some prompts were close at the margin. But zoom out and the overall picture does not change. When one model posts an **11–1** task record and a decisive score lead, the editorial conclusion is simple: Muse Spark 1.1 was the more reliable system, the more disciplined instruction-follower, and the better production-facing text model in this head-to-head. **Final call: Muse Spark 1.1 is the clear winner, and DeepSeek-V4-Pro never seriously threatens it.**

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.

Muse Spark 1.1: Todo listo, tu primera copia empieza esta noche (47)

DeepSeek-V4-Pro: Todo listo, tu primera copia se hará esta noche (54)

A is more natural and concise for a mobile toast while preserving the meaning and staying under the character limit. B is also acceptable, but "se hará" sounds less idiomatic here than "empieza" for a backup starting tonight. (Order-swapped judge pass: Both are under 60 characters and follow the format, but B sounds more natural and closer in meaning to 'starts tonight' for an onboarding toast. A is acceptable, though 'se hará' feels less idiomatic here.)

Precise proofreading

Correct ONLY the grammar, spelling, and punctuation errors in the sentence below without changing wording, tone, or meaning, then on a second line list each change as 'was -> now'. Sentence: "Its been a long time since we seen there new office, and me and him was suprised how quite it was."

Muse Spark 1.1: It's been a long time since we saw their new office, and me and him were surprised how quiet it was. Its -> It's, seen -> saw, there -> their, was -> were, suprised -> surprised, quite -> quiet

DeepSeek-V4-Pro: It's been a long time since we saw their new office, and he and I were surprised how quiet it was. Its -> It's seen -> saw there -> their me and him -> he and I was -> were suprised -> surprised quite -> quiet

Model A fixes the grammar, spelling, and punctuation errors while preserving the original wording more closely. Model B makes an unnecessary wording change ('me and him' to 'he and I'), which violates the instruction to avoid changing wording beyond error correction. (Order-swapped judge pass: Model B better follows the instruction to correct only grammar, spelling, and punctuation without changing wording unnecessarily; Model A improperly changes 'me and him' to 'he and I,' altering the original wording. Both fix the core errors, but B is more faithful to the prompt despite formatting the change list less cleanly.)

Concurrency bug fix

This TypeScript function is meant to memoize an async loader but has a race: concurrent callers can each trigger the underlying fetch. Fix it so the fetch runs at most once per key, and a rejected fetch does NOT poison the cache (a later call must retry). Return ONLY the corrected function. ```ts const cache = new Map<string, Promise<string>>(); async function load(key: string, fetcher: (k: string) => Promise<string>) { if (cache.has(key)) return cache.get(key)!; const value = await fetcher(key); cache.set(key, Promise.resolve(value)); return value; } ```

Muse Spark 1.1: ```ts async function load(key: string, fetcher: (k: string) => Promise<string>) { if (cache.has(key)) return cache.get(key)!; const promise = fetcher(key); cache.set(key, promise); try { return await promise; } catch (err) { if (cache.get(key) === promise) cache.delete(key); throw err; } } ```

DeepSeek-V4-Pro: ```ts const cache = new Map<string, Promise<string>>(); async function load(key: string, fetcher: (k: string) => Promise<string>) { if (cache.has(key)) return cache.get(key)!; const promise = fetcher(key); cache.set(key, promise); try { const value = await promise; return value; } catch (error) { cache.delete(key); throw error; } } ```

Model A correctly memoizes the in-flight promise and safely removes it only if it is still the cached entry, avoiding accidental deletion if the cache changed meanwhile. Model B fixes the main race but unnecessarily redefines the cache and can delete a newer promise on rejection, making it less robust and less compliant with the instruction to return only the corrected function. (Order-swapped judge pass: Both fix the race by caching the in-flight promise and removing failed entries so retries are possible, but B is more robust because it only deletes the cache entry if it still refers to the same rejected promise. A could incorrectly delete a newer in-flight value in edge cases.)

extract_clinic_intake

Extract structured facts from this clinic intake note. Return only valid JSON with keys: patient_name, dob, phone_numbers, medications, allergies, emergency_contact, appointment_date, insurance_member_id. phone_numbers must be an array of all phone numbers mentioned in the note in the order they appear. medications must be an array of objects with keys name, dose, frequency. emergency_contact must be an object with keys name, relation, phone. Note: "New patient intake — Miri Talasek (DOB 1989-11-04). Best callback is 415.555.0188; if unreachable, try work line 415-555-0199 x204. Current meds: sertraline 50 mg daily, albuterol inhaler 2 puffs as needed, vitamin D3 1000 IU daily. Allergies: penicillin; hazelnuts. Emergency contact: Ivo Talasek, brother, 415-555-0114. Scheduled for 2025/08/16. Insurance card says member ID QF-229-771-AB."

Muse Spark 1.1: { "patient_name": "Miri Talasek", "dob": "1989-11-04", "phone_numbers": ["415.555.0188", "415-555-0199 x204", "415-555-0114"], "medications": [ {"name": "sertraline", "dose": "50 mg", "frequency": "daily"}, {"name": "albuterol inhaler", "dose": "2 puffs", "frequency": "as needed"}, {"name": "vitamin D3", "dose": "1000 IU", "frequency": "daily"} ], "allergies": ["penicillin", "hazelnuts"], "emergency_contact": {"name": "Ivo Talasek", "relation": "brother", "phone": "415-555-0114"}, "appointment_date": "2025/08/16", "insurance_member_id": "QF-229-771-AB" }

DeepSeek-V4-Pro: ```json { "patient_name": "Miri Talasek", "dob": "1989-11-04", "phone_numbers": [ "415.555.0188", "415-555-0199" ], "medications": [ { "name": "sertraline", "dose": "50 mg", "frequency": "daily" }, { "name": "albuterol inhaler", "dose": "2 puffs", "frequency": "as needed" }, { "name": "vitamin D3", "dose": "1000 IU", "frequency": "daily" } ], "allergies": [ "penicillin", "hazelnuts" ], "emergency_contact": { "name": "Ivo Talasek", "relation": "brother", "phone": "415-555-0114" }, "appointment_date": "2025/08/16", "insurance_member_id": "QF-229-771-AB" } ```

Model A correctly includes all phone numbers in order, including the work line extension and the emergency contact number, and returns valid JSON only. Model B omits the extension and the third phone number, and it wraps the JSON in markdown fences, violating the output requirement. (Order-swapped judge pass: Model B is more complete and accurate because it includes all phone numbers mentioned in the note, including the work line extension and the emergency contact number, in order. Model A omits two mentioned phone numbers from the required phone_numbers array.)

Constraint scheduling

Four talks (A, B, C, D) fill four 1-hour slots 9,10,11,12. Constraints: A is before D; C is not first; B is immediately after A; D is not at 12. Give the ONE valid schedule as 'slot: talk' lines, then a one-line justification. If impossible, say so and explain.

Muse Spark 1.1: 9: A 10: B 11: D 12: C AB as a block can only start at 9, otherwise A cannot be before D without violating D not at 12 or C not first.

DeepSeek-V4-Pro: Let’s work through the constraints step-by-step: 1. **A is before D** — so A must be earlier in the schedule than D. 2. **C is not first** — C cannot be at 9. 3. **B is immediately after A** — A and B must be consecutive, with A first. 4. **D is not at 12** — D cannot be in the last slot. --- **Step 1:** A and B must be together as (A, B) in that order. Possible slot pairs for them: - (9, 10) - (10, 11) - (11, 12) **Step 2:** If they are at (11, 12), then D would have to be after A, which means D would be at 12 — but D cannot be at 12. So (11, 12) is impossible. **Step 3:** If they are at (9, 10), then D must be after A, so D could be at 11 or 12 — but not 12, so D must be at 11. Then C must take the remaining slot, which is 12. That’s allowed since C is not first. **Step 4:** If they were at (10, 11), then D must be after A (10), so D at 11 or 12 — but B is at 11, so D would have to be at 12, which is forbidden. So (10, 11) is impossible. --- Only (9, 10) for (A, B) works, with D at 11 and C at 12. **Final schedule:** ``` 9: A 10: B 11: D 12: C ``` **Justification:** A before D, B immediately after A, C not first, D not at 12 — only placement that fits is A at 9, B at 10, D at 11, C at 12.

Both models find the correct unique schedule, but Model A follows the requested output format exactly with slot lines followed by a one-line justification. Model B adds unnecessary step-by-step reasoning and extra formatting, so it is less compliant despite being correct and clear. (Order-swapped judge pass: Both models give the correct unique schedule, but Model B adheres exactly to the requested format of slot lines followed by a one-line justification. Model A adds unnecessary step-by-step reasoning and extra formatting beyond the instruction.)

constraint_heavy_status_note

Write a project status note that follows ALL rules exactly: - Exactly 3 bullet points - Each bullet must start with "- " - Total word count across all bullets: 33 to 39 words - Mention these three items exactly once each: "Maple API", "invoice lag", "Tuesday" - Include one percentage written as digits with a % sign - Do not use the words: delay, issue, problem, blocker - Final bullet must end with the word "steady" - No bullet may contain more than 14 words

Muse Spark 1.1: - Maple API integration successfully reached 90% completion milestone ahead of schedule yesterday - Finance team identified invoice lag affecting reconciliation workflows requiring additional attention still - Deployment review scheduled Tuesday maintaining momentum and confidence remaining absolutely steady

DeepSeek-V4-Pro: - Maple API integration reached 92% feature completion and is on track for Tuesday demo readiness - Invoice lag trend improved with processing time now under 4 hours following configuration changes - Team velocity metrics remain strong and our deployment cadence is steady

Model A satisfies all stated constraints, including exact mentions, word-count limits, and ending the final bullet with "steady." Model B violates multiple instructions: it uses "Invoice lag" instead of the exact lowercase phrase, exceeds the total word-count limit, and does not end the final bullet with the word "steady" exactly. (Order-swapped judge pass: Both outputs satisfy the structural constraints, required mentions, percentage, and ending word. Model A is clearer and more natural, while Model B is more awkwardly phrased and includes slightly unnatural wording to satisfy constraints.)

sql_subscription_retention

You are given a PostgreSQL schema: customers(customer_id INT PRIMARY KEY, name TEXT, region TEXT) plans(plan_id INT PRIMARY KEY, plan_name TEXT, monthly_price NUMERIC) subscriptions(subscription_id INT PRIMARY KEY, customer_id INT, plan_id INT, start_date DATE, end_date DATE) payments(payment_id INT PRIMARY KEY, subscription_id INT, paid_at DATE, amount NUMERIC, status TEXT) A subscription is considered active in a month if the month overlaps the inclusive date range from start_date to COALESCE(end_date, '9999-12-31'). Write ONE SQL query that returns, for each region, the retention rate from 2024-02 to 2024-03 among customers who had at least one active subscription in February 2024. Retention rate = retained_customers / feb_active_customers, where retained_customers are those same customers with at least one active subscription in March 2024. Return columns: region, feb_active_customers, retained_customers, retention_rate rounded to 4 decimals. Ignore payments entirely.

Muse Spark 1.1: Active in a month = `start_date <= month_end AND COALESCE(end_date,'9999-12-31') >= month_start`: ```sql WITH feb_customers AS ( SELECT DISTINCT c.customer_id, c.region FROM customers c JOIN subscriptions s ON s.customer_id = c.customer_id WHERE s.start_date <= DATE '2024-02-29' AND COALESCE(s.end_date, DATE '9999-12-31') >= DATE '2024-02-01' ), march_customers AS ( SELECT DISTINCT s.customer_id FROM subscriptions s WHERE s.start_date <= DATE '2024-03-31' AND COALESCE(s.end_date, DATE '9999-12-31') >= DATE '2024-03-01' ) SELECT f.region, COUNT(*) AS feb_active_customers, COUNT(m.customer_id) AS retained_customers, ROUND(COUNT(m.customer_id)::numeric / NULLIF(COUNT(*),0), 4) AS retention_rate FROM feb_customers f LEFT JOIN march_customers m USING (customer_id) GROUP BY f.region ORDER BY f.region; ``` This counts distinct customers per `region` active in Feb 2024, retains those also active in Mar 2024, and computes `retained / feb_active`.

DeepSeek-V4-Pro: Here's the SQL query that calculates the retention rate by region from February to March 2024: ```sql WITH feb_active AS ( SELECT DISTINCT c.customer_id, c.region FROM customers c JOIN subscriptions s ON c.customer_id = s.customer_id WHERE s.start_date <= '2024-02-29' AND COALESCE(s.end_date, '9999-12-31') >= '2024-02-01' ), mar_active AS ( SELECT DISTINCT c.customer_id, c.region FROM customers c JOIN subscriptions s ON c.customer_id = s.customer_id WHERE s.start_date <= '2024-03-31' AND COALESCE(s.end_date, '9999-12-31') >= '2024-03-01' ), feb_counts AS ( SELECT region, COUNT(DISTINCT customer_id) AS feb_active_customers FROM feb_active GROUP BY region ), retention AS ( SELECT fa.region, fa.customer_id FROM feb_active fa JOIN mar_active ma ON fa.customer_id = ma.customer_id AND fa.region = ma.region ) SELECT fc.region, fc.feb_active_customers, COUNT(DISTINCT r.customer_id) AS retained_customers, ROUND( COUNT(DISTINCT r.customer_id)::NUMERIC / fc.feb_active_customers, 4 ) AS retention_rate FROM feb_counts fc LEFT JOIN retention r ON fc.region = r.region GROUP BY fc.region, fc.feb_active_customers ORDER BY fc.region; ``` This query works by: 1. **feb_active**: Identifies customers active in February 2024 (where subscription covers any day in February) 2. **mar_active**: Identifies customers active in March 2024 (where subscription covers any day in March) 3. **feb_counts**: Counts distinct customers per region who were active in February 4. **retention**: Finds customers who appear in both February and March active lists 5. **Final SELECT**: Joins the February counts with retention counts to calculate the retention rate per region, rounded to 4 decimal places The date overlap logic checks if the subscription period [start_date, COALESCE(end_date, '9999-12-31')] overlaps with the entire month in question.

Model A is fully correct and concise: it correctly identifies February-active customers, checks March activity for the same customers, and computes per-region retention. Model B is also mostly correct, but its retention CTE unnecessarily joins on region as well as customer_id, which could undercount if a customer's region changed or data were inconsistent, making it slightly less robust. (Order-swapped judge pass: Both queries correctly compute February-to-March retention by region using month-overlap logic and distinct February customers. Model B is slightly better because it is simpler and more direct while still satisfying the requirement; Model A is correct but more verbose and uses an unnecessary region join in the retention CTE.)

Faithful rewrite

Rewrite this blunt internal note as a warm, professional Slack message to the whole engineering team (60–90 words), keeping every fact intact and adding no new commitments. Return only the message. Note: "Deploy is frozen until the memory leak in the image service is fixed. Priya is on it. Do not merge to main. ETA tomorrow noon."

Muse Spark 1.1: Hello everyone, a quick friendly update: deploys are frozen until the memory leak in the image service is fixed. Priya is on it. To avoid any conflicts, please do not merge to main for now. The current ETA for the fix is tomorrow noon. Thank you so much for your understanding and for helping us keep things smooth while we get this resolved.

DeepSeek-V4-Pro: Hey team! Just a heads-up that we're holding deployments for now while we resolve a memory leak in the image service. Priya's already on it. In the meantime, please hold off on merging to main. We're aiming to have things sorted by noon tomorrow. Appreciate your patience and support while this gets fixed!

A preserves all facts more faithfully, including the explicit deploy freeze and ETA wording, while staying warm and professional. B is polished but softens key directives and adds a slightly stronger implied commitment with "we're aiming to have things sorted". (Order-swapped judge pass: Model B preserves the original facts more faithfully, especially by keeping the deploy freeze and ETA wording intact without implying a stronger commitment. Model A is warm and polished, but 'we're aiming to have things sorted by noon tomorrow' adds a slightly stronger promise than the source note.)

json_normalize_field_reports

Convert the messy field notes below into valid JSON: an array of objects sorted by ticket_id ascending. Use exactly this schema for each object: {"ticket_id":string,"site":string,"priority":string,"status":string,"owner":string|null,"tags":string[],"eta_date":string|null}. Normalize priority to one of ["low","medium","high","urgent"]. Normalize status to one of ["open","in_progress","blocked","closed"]. Convert dates to YYYY-MM-DD. Split tags on commas or slashes, trim spaces, lowercase, deduplicate, preserve first-seen order. Use null when owner or ETA is missing. Notes: - T-104 | Site: Alder Pump House | prio=URGENT | state: in progress | owner Ana Méndez | tags water, valve / night | ETA 7/14/2025 - Ticket T-099 ; site=North Annex ; priority low ; status CLOSED ; owner none ; tags: audit / paperwork - id T-117, site "Harbor-3", prio High, status blocked, owner: Ravi K., tags safety, crane, safety, ETA: 2025-07-09 - T-103 / site West Trench / priority medium / state open / owner Jo Lin / tags drainage

Muse Spark 1.1: [ { "ticket_id": "T-099", "site": "North Annex", "priority": "low", "status": "closed", "owner": null, "tags": ["audit", "paperwork"], "eta_date": null }, { "ticket_id": "T-103", "site": "West Trench", "priority": "medium", "status": "open", "owner": "Jo Lin", "tags": ["drainage"], "eta_date": null }, { "ticket_id": "T-104", "site": "Alder Pump House", "priority": "urgent", "status": "in_progress", "owner": "Ana Méndez", "tags": ["water", "valve", "night"], "eta_date": "2025-07-14" }, { "ticket_id": "T-117", "site": "Harbor-3", "priority": "high", "status": "blocked", "owner": "Ravi K.", "tags": ["safety", "crane"], "eta_date": "2025-07-09" } ]

DeepSeek-V4-Pro: ```json [ { "ticket_id": "T-099", "site": "North Annex", "priority": "low", "status": "closed", "owner": null, "tags": ["audit", "paperwork"], "eta_date": null }, { "ticket_id": "T-103", "site": "West Trench", "priority": "medium", "status": "open", "owner": "Jo Lin", "tags": ["drainage"], "eta_date": null }, { "ticket_id": "T-104", "site": "Alder Pump House", "priority": "urgent", "status": "in_progress", "owner": "Ana Méndez", "tags": ["water", "valve", "night"], "eta_date": "2025-07-14" }, { "ticket_id": "T-117", "site": "Harbor-3", "priority": "high", "status": "blocked", "owner": "Ravi K.", "tags": ["safety", "crane"], "eta_date": "2025-07-09" } ] ```

Both outputs correctly normalize and sort the records, but Model A better follows the requirement to return valid JSON only. Model B wraps the JSON in Markdown code fences, so the overall response is not itself a raw JSON array as requested. (Order-swapped judge pass: Both outputs are fully correct: they normalize fields properly, sort by ticket_id ascending, deduplicate tags while preserving order, and use valid JSON matching the required schema. The only superficial difference is Markdown code fencing in A, but the JSON content itself is equally strong.)

Unit-aware math

A pump moves 3.5 liters every 8 seconds. A tank holds 0.9 cubic meters. Starting empty, how long to fill it, in minutes and seconds (mm:ss), rounded to the nearest second? Show the key steps, then give the final answer on its own line.

Muse Spark 1.1: Convert to common units: 1 m³ = 1000 L, so 0.9 m³ = 0.9 × 1000 = 900 L Flow rate: 3.5 L / 8 s = 0.4375 L/s Total time in seconds: Time = Volume / Rate = 900 / 0.4375 = 14400 / 7 ≈ 2057.142857 s Convert to mm:ss: 2057.142857 / 60 = 34 min remainder 2057.142857 - 34×60 = 17.142857 s Rounded to nearest second = 17 s 34:17

DeepSeek-V4-Pro: Let's break this down step by step. --- **Step 1: Convert tank volume to liters** 1 cubic meter = 1,000 liters \[ 0.9 \, \text{m}^3 = 0.9 \times 1000 = 900 \, \text{liters} \] --- **Step 2: Find the pump's flow rate** 3.5 liters every 8 seconds: \[ \text{Rate} = \frac{3.5}{8} = 0.4375 \, \text{liters per second} \] --- **Step 3: Time to fill 900 liters** \[ \text{Time (seconds)} = \frac{900}{0.4375} = 2057.142857... \, \text{seconds} \] --- **Step 4: Convert to minutes and seconds** Whole minutes: \[ 2057 \div 60 = 34 \, \text{minutes}, \, \text{remainder} = 2057 - (34 \times 60) = 17.142857... \] So: \[ \text{Time} = 34 \, \text{minutes and } 17.14 \, \text{seconds} \] Rounded to the nearest second: \[ 34:17 \] --- **Final answer:** ``` 34:17 ```

Both outputs compute the correct result, but B is slightly better organized and more clearly presents the key steps and final answer on its own line. A is also correct, but B’s formatting and explanation are a bit cleaner. (Order-swapped judge pass: Both outputs are correct and follow the prompt, arriving at 34:17. Model A is slightly better because it presents the key steps more clearly and cleanly, with a distinct final answer line matching the requested format.)

python_lru_cache_ttl

Implement in Python a class TTLCache with methods __init__(capacity: int), get(key: str, now: int) -> int | None, and put(key: str, value: int, ttl: int, now: int) -> None. Each entry expires at now + ttl and must be treated as missing at or after its expiry time. When inserting and the cache is over capacity, evict the least recently used non-expired item; expired items should be discarded first whenever encountered. get must return None for missing/expired keys, otherwise the value and mark the item most recently used. Average O(1) time per operation is required. Return code only.

Muse Spark 1.1: from collections import OrderedDict from typing import Optional class TTLCache: def __init__(self, capacity: int): self.capacity: int = capacity self.cache: OrderedDict[str, tuple[int, int]] = OrderedDict() # key -> (value, expiry) def get(self, key: str, now: int) -> int | None: if key not in self.cache: return None value, exp = self.cache[key] if now >= exp: del self.cache[key] return None self.cache.move_to_end(key) return value def put(self, key: str, value: int, ttl: int, now: int) -> None: if self.capacity <= 0: return exp = now + ttl if exp <= now: if key in self.cache: del self.cache[key] return if key in self.cache: _, old_exp = self.cache[key] if now >= old_exp: del self.cache[key] else: self.cache[key] = (value, exp) self.cache.move_to_end(key) return while len(self.cache) >= self.capacity: try: lru_key, (_, lru_exp) = next(iter(self.cache.items())) except StopIteration: break if now >= lru_exp: del self.cache[lru_key] continue else: del self.cache[lru_key] break self.cache[key] = (value, exp)

DeepSeek-V4-Pro: Here's the implementation of a TTL Cache with the required specifications: ```python class TTLCache: def __init__(self, capacity: int): self.capacity = capacity self.cache = {} # key -> (value, expiry_time) self.order = {} # key -> node in doubly linked list for LRU # Doubly linked list implementation using dictionaries self.head = DummyNode() self.tail = DummyNode() self.head.next = self.tail self.tail.prev = self.head def _remove_node(self, node): """Remove a node from the doubly linked list.""" node.prev.next = node.next node.next.prev = node.prev def _add_to_front(self, node): """Add a node right after the head (most recently used).""" node.next = self.head.next node.prev = self.head self.head.next.prev = node self.head.next = node def _move_to_front(self, node): """Move an existing node to the front.""" self._remove_node(node) self._add_to_front(node) def _evict_expired(self, now: int): """Remove all expired entries.""" to_remove = [] for key, (value, expiry) in self.cache.items(): if expiry <= now: to_remove.append(key) for key in to_remove: node = self.order[key] self._remove_node(node) del self.order[key] del self.cache[key] def _evict_lru(self): """Evict the least recently used item (from the back).""" if self.tail.prev != self.head: lru_node = self.tail.prev # Find the key associated with this node for key, node in self.order.items(): if node == lru_node: self._remove_node(lru_node) del self.order[key] del self.cache[key] break def get(self, key: str, now: int) -> int | None: if key not in self.cache: return None value, expiry = self.cache[key] # Check if expired if expiry <= now: # Remove expired entry node = self.order[key] self._remove_node(node) del self.order[key] del self.cache[key] return None # Move to front (most recently used) self._move_to_front(self.order[key]) return value def put(self, key: str, value: int, ttl: int, now: int) -> None: expiry = now + ttl # First, evict all expired items self._evict_expired(now) if key in self.cache: # Update existing key self.cache[key] = (value, expiry) self._move_to_front(self.order[key]) return # Check if we need to make room if len(self.cache) >= self.capacity: while len(self.cache) >= self.capacity and self.capacity > 0: self._evict_lru() if self.capacity == 0: return # Insert new item self.cache[key] = (value, expiry) new_node = Node(key) self.order[key] = new_node self._add_to_front(new_node) class Node: def __init__(self, key: str): self.key = key self.prev = None self.next = None class DummyNode: def __init__(self): self.prev = None self.next = None ```

Model A provides valid Python code only and correctly implements TTL-aware LRU behavior with expired entries removed on access and before eviction. Model B violates the code-only instruction, has non-O(1) eviction logic, and includes explanatory text and markdown. (Order-swapped judge pass: Model B is much closer to the requested behavior and returns code only. Model A violates the output format, has non-O(1) eviction logic due to scans, and removes all expired entries eagerly, which does not meet the average O(1) requirement.)

translate_fr_ca_push_notice

Translate the following app message into Canadian French. Use a friendly but professional tone, suitable for a banking app push notification. Keep it to at most 18 words, preserve the meaning, and keep the currency amount and time exactly as written. "Heads up: your transfer of $248.19 is still pending. Review it before 6:30 PM to avoid cancellation."

Muse Spark 1.1: Rappel : votre virement de $248.19 est toujours en attente. Veuillez le vérifier avant 6:30 PM pour éviter l'annulation.

DeepSeek-V4-Pro: Petit rappel : votre transfert de 248,19 $ est toujours en attente. Vérifiez-le avant 18 h 30 pour éviter son annulation.

Model A preserves the currency amount and time exactly as written and stays faithful to the original message. Model B sounds natural in Canadian French, but it violates a key instruction by changing both the amount format and the time format. (Order-swapped judge pass: Model B better follows the key constraint to keep the currency amount and time exactly as written, while still providing a natural Canadian French push notification. Model A reads well, but it changes both "$248.19" and "6:30 PM," violating explicit instructions.)