grok-4.3 vs DeepSeek-V4-Pro: Precision Beats Padding

grok-4.3 vs DeepSeek-V4-Pro

grok-4.3 wins this head-to-head 37.0 to 30.0 by being the more obedient, production-ready text model. Across four tasks, it was consistently tighter on instructions and cleaner on edge cases, while DeepSeek-V4-Pro kept drifting into unnecessary constraints or formatting mistakes.

grok-4.3 takes this matchup because it does the boring but essential thing better: it follows the prompt exactly. The aggregate score says it clearly enough — **37.0 to 30.0** — but the task-level results make the gap more concrete. This wasn’t a flashy win built on one standout answer; it was a steady demonstration that A is more reliable where real workflows break. The cleanest example is **python-log-redactor**. grok-4.3 matched the requested email and IPv4 patterns while preserving surrounding punctuation, which is exactly what a redaction utility needs to do. DeepSeek-V4-Pro made an avoidable mistake by imposing a stricter email rule than the prompt asked for, requiring a dot in the domain and therefore missing valid `local@domain` cases. That’s not a stylistic difference; that’s a functional miss caused by over-interpreting the spec. On **vendor-delay-update** and **meeting-summary-actions**, grok-4.3 was simply the sharper editor. Its Slack draft stayed within the 90–120 word limit, kept the tone calm and blame-free, and included the required facts without wandering into meta framing. DeepSeek-V4-Pro was serviceable, but too long and too padded. In the meeting summary task, A again showed better discipline: it captured the cause, mitigation, and measured improvement in two sentences, while B introduced a questionable “decision” and ended up slightly less faithful to the notes. The **messy-orders-to-json** result is the kind of detail that separates usable output from annoying cleanup. Both models parsed and sorted the orders correctly, and both likely stumbled on the same SKU ambiguity in `K9-AX2x3`. But only grok-4.3 respected the instruction to return **valid JSON only**. DeepSeek-V4-Pro wrapped its answer in Markdown fences, which is a small failure until you’re piping the result into something that expects raw JSON and breaks. **Final call: grok-4.3 is the better text model here because it is more exact, more compliant, and more immediately usable. DeepSeek-V4-Pro isn’t bad; it’s just looser, and in these tasks that looseness repeatedly turned into errors.**

python-log-redactor

Language: Python 3.11. Return code only. Write a function `redact_log(line: str) -> str` for an API gateway log pipeline. Replace any IPv4 address with `[IP]` and any email address with `[EMAIL]`, but do not alter surrounding punctuation. Matching should be case-insensitive for email. Assume valid emails are of the form local@domain with letters, digits, dots, underscores, plus, and hyphens in the local part, and letters, digits, dots, and hyphens in the domain. IPv4 means four 1-3 digit octets separated by dots; you do not need to validate 0-255 ranges. Examples: - `"Failed login from 10.44.3.9 for Mira.J+ops@Northpass.io"` -> `"Failed login from [IP] for [EMAIL]"` - `"notify<sam_lee@acorn-labs.dev>, src=172.16.0.12"` -> `"notify<[EMAIL]>, src=[IP]"` Use only the standard library.

grok-4.3: ```python import re def redact_log(line: str) -> str: line = re.sub(r'\b[A-Za-z0-9._+\-]+@[A-Za-z0-9.\-]+\b', '[EMAIL]', line, flags=re.IGNORECASE) line = re.sub(r'\b(?:\d{1,3}\.){3}\d{1,3}\b', '[IP]', line) return line ```

DeepSeek-V4-Pro: ```python import re def redact_log(line: str) -> str: ip_pattern = r'\b(?:\d{1,3}\.){3}\d{1,3}\b' email_pattern = r'\b[a-zA-Z0-9._+\-]+@[a-zA-Z0-9.\-]+\.[a-zA-Z0-9.\-]+\b' line = re.sub(ip_pattern, '[IP]', line) line = re.sub(email_pattern, '[EMAIL]', line, flags=re.IGNORECASE) return line ```

A cleanly matches the stated email form and IPv4 pattern while preserving surrounding punctuation. B is more restrictive than requested because it requires at least one dot in the domain, so it can miss valid local@domain emails allowed by the prompt.

vendor-delay-update

Draft a workplace status update for the operations leadership Slack channel. Scenario: The replacement badge printers for the Tulsa and Reno offices were due today, but vendor Heliotrope Devices pushed delivery to next Wednesday because the shipment was misrouted in Phoenix. Current workaround: security is issuing handwritten visitor passes and reusing temporary contractor badges after manual sign-out. Risks: slower lobby lines at 8-9am, but no compliance gap because ID checks continue. Ask site managers to stagger large visitor groups if possible. Audience: directors and site managers. Tone: calm, competent, no blame. Length: 90-120 words.

grok-4.3: Team, quick update on the replacement badge printers for Tulsa and Reno. Delivery from Heliotrope Devices has shifted to next Wednesday after the shipment was misrouted in Phoenix. Security is handling visitor access with handwritten passes and reused temporary contractor badges after manual sign-out. ID checks continue without interruption, so there is no compliance exposure. We expect slower lobby processing during the 8-9am peak, but the impact should stay limited. Site managers, if you can stagger large visitor groups where possible, that would help keep lines manageable. Thanks for your support.

DeepSeek-V4-Pro: Here’s a draft for the Slack update: --- **Operations Update: Badge Printer Delivery Delay** The replacement badge printers for Tulsa and Reno, originally scheduled for delivery today, have been pushed to next Wednesday. Vendor Heliotrope Devices confirmed the shipment was misrouted in Phoenix and is back on track. **Current workaround in place:** Security at both sites is issuing handwritten visitor passes and reusing temporary contractor badges with manual sign-out. ID verification continues as standard, so we remain fully compliant—no gaps on that front. **What to expect:** Lobby throughput may slow slightly during the 8–9am peak. We recommend staggering larger visitor groups where feasible to ease morning congestion. No further action is needed from your teams right now. We’ll confirm delivery and installation timing once the shipment is in hand. Happy to answer any questions in the thread.

A is concise, directly usable in Slack, and stays within the 90–120 word limit while covering all required facts in a calm, no-blame tone. B is clear and well-structured, but it exceeds the length limit and adds framing/meta text and extra details not needed for the requested draft.

meeting-summary-actions

Read the meeting notes below. Then provide: 1) a 2-sentence summary 2) a JSON object with keys `decisions`, `action_items`, and `risks` Meeting notes: "Tuesday 14:00 checkout reliability sync. Priya said the spike in duplicate charges came from the retry worker replaying 502 responses from Northlake Pay when the gateway had already captured the payment. Mateo shipped a temporary guard at 13:20 that suppresses retries if a capture id is present; early numbers show duplicate charges fell from 31 yesterday to 3 today. Team agreed not to enable the queued receipt emails until finance signs off on the refund wording. Lian will draft the finance note by Thursday 11:00. Devon will add a dashboard tile for duplicate-charge rate and chargeback count before Friday standup. Open risk: some bank debit transactions still arrive without capture ids, so the guard may miss a small subset. Customer support has 18 affected tickets waiting on refund ETA."

grok-4.3: 1) During Tuesday's checkout reliability sync, the team traced a spike in duplicate charges to the retry worker replaying 502 responses from Northlake Pay after the gateway had already captured payment, and Mateo deployed a temporary guard at 13:20 that suppresses retries when a capture ID is present, cutting duplicates from 31 to 3. 2) The group decided to keep queued receipt emails disabled until finance approves the refund wording, assigned Lian to draft a finance note by Thursday 11:00 and Devon to add duplicate-charge and chargeback dashboard tiles before Friday standup, while noting the remaining risk that some bank-debit transactions lack capture IDs and 18 support tickets still await refunds. ```json { "decisions": [ "Do not enable queued receipt emails until finance signs off on the refund wording." ], "action_items": [ "Lian will draft the finance note by Thursday 11:00.", "Devon will add a dashboard tile for duplicate-charge rate and chargeback count before Friday standup." ], "risks": [ "Some bank debit transactions still arrive without capture IDs, so the guard may miss a small subset.", "Customer support has 18 affected tickets waiting on refund ETA." ] } ```

DeepSeek-V4-Pro: 1) The team identified the root cause of duplicate charges as a retry worker issue with Northlake Pay and deployed a temporary fix that significantly reduced occurrences. Finance must approve refund wording before queued receipt emails are enabled, and there is an open risk that bank debit transactions without capture IDs could still bypass the guard. 2) ```json { "decisions": [ "Temporary guard deployed to suppress retries when a capture ID is present", "Queued receipt emails will not be enabled until finance approves refund wording" ], "action_items": [ { "assignee": "Lian", "task": "Draft finance note for refund wording", "due": "Thursday 11:00" }, { "assignee": "Devon", "task": "Add dashboard tile for duplicate-charge rate and chargeback count", "due": "Friday standup" } ], "risks": [ "Bank debit transactions arriving without capture IDs may bypass the retry guard", "Customer support has 18 affected tickets waiting on refund ETA" ] } ```

A is more faithful to the notes and fully captures the key specifics in the 2-sentence summary, including the cause, mitigation, and measured improvement. B is solid, but it adds a debatable 'decision' that was more an already-shipped action than an agreed decision, and its summary is slightly less complete.

messy-orders-to-json

Convert the messy order lines below into valid JSON only. Output schema: an array of objects sorted by `order_id` ascending. Each object must have exactly these keys: - `order_id` (string) - `customer` (string, title case) - `items` (array of objects with keys `sku` string and `qty` integer) - `rush` (boolean) - `ship_by` (string in YYYY-MM-DD) Rules: - Split items on `|` - Each item is `SKUxQTY` - Normalize customer spacing - `rush:Y` => true, `rush:N` => false - Dates are all in 2026 and appear as M/D Data: ORD-104 ; customer= nora velasquez ; items=K9-AX2x3|M-44x1 ; rush=Y ; ship_by=7/9 ORD-102;customer=eli brooks;items=Q2x12;rush=N;ship_by=7/11 ORD-107 ; customer= MARCO iBARRA; items=TT-9x2|Q2x1|L-EXx4 ; rush=Y ; ship_by=7/8 ORD-103; customer=ava ng ; items= M-44x2 | B7x5 ; rush=N ; ship_by=7/10

grok-4.3: [ { "order_id": "ORD-102", "customer": "Eli Brooks", "items": [ {"sku": "Q2", "qty": 12} ], "rush": false, "ship_by": "2026-07-11" }, { "order_id": "ORD-103", "customer": "Ava Ng", "items": [ {"sku": "M-44", "qty": 2}, {"sku": "B7", "qty": 5} ], "rush": false, "ship_by": "2026-07-10" }, { "order_id": "ORD-104", "customer": "Nora Velasquez", "items": [ {"sku": "K9-AX2", "qty": 3}, {"sku": "M-44", "qty": 1} ], "rush": true, "ship_by": "2026-07-09" }, { "order_id": "ORD-107", "customer": "Marco Ibarra", "items": [ {"sku": "TT-9", "qty": 2}, {"sku": "Q2", "qty": 1}, {"sku": "L-EX", "qty": 4} ], "rush": true, "ship_by": "2026-07-08" } ]

DeepSeek-V4-Pro: ```json [ { "order_id": "ORD-102", "customer": "Eli Brooks", "items": [ { "sku": "Q2", "qty": 12 } ], "rush": false, "ship_by": "2026-07-11" }, { "order_id": "ORD-103", "customer": "Ava Ng", "items": [ { "sku": "M-44", "qty": 2 }, { "sku": "B7", "qty": 5 } ], "rush": false, "ship_by": "2026-07-10" }, { "order_id": "ORD-104", "customer": "Nora Velasquez", "items": [ { "sku": "K9-AX2", "qty": 3 }, { "sku": "M-44", "qty": 1 } ], "rush": true, "ship_by": "2026-07-09" }, { "order_id": "ORD-107", "customer": "Marco Ibarra", "items": [ { "sku": "TT-9", "qty": 2 }, { "sku": "Q2", "qty": 1 }, { "sku": "L-EX", "qty": 4 } ], "rush": true, "ship_by": "2026-07-08" } ] ```

Both outputs parse the orders correctly and are sorted properly, but Model A follows the instruction to return valid JSON only, while Model B wraps the JSON in Markdown code fences. Both also share the same likely SKU parsing issue on "K9-AX2x3," so A is better on adherence.