Head to head: grok-4.3 vs Llama-3.3-70B-Instruct

grok-4.3 vs Llama-3.3-70B-Instruct

This matchup wasn’t close: grok-4.3 won on both instruction discipline and editorial reliability. Across code, JSON formatting, and business writing, it consistently did the exact job asked, while Llama-3.3-70B-Instruct kept slipping on output constraints and precision.

grok-4.3 takes this comfortably, 38.0 to 26.0, because it does the unglamorous thing that actually matters in production settings: it follows directions exactly. That was the pattern across the set. Where the prompt asked for code only or JSON only, grok-4.3 delivered the required format without decoration; Llama-3.3-70B-Instruct repeatedly added Markdown fences or extra material and turned otherwise workable answers into instruction misses. The clearest example is `python-log-redactor`. grok-4.3 produced clean code only, included the necessary import, and correctly redacted values up to a space, comma, semicolon, or end of line while preserving delimiters. Llama-3.3-70B-Instruct had broadly similar redaction logic, but it wrapped the answer in Markdown fences and added example usage and printing. That is not a minor stylistic quirk; it is a direct failure on the prompt’s core constraint. The same problem showed up again in `messy-orders-to-json`. Both models basically parsed the orders correctly and sorted them into the right schema, but only grok-4.3 returned valid JSON only. Llama-3.3-70B-Instruct once again fenced the output, which is exactly the kind of formatting error that breaks downstream use. In structured-output tasks, “almost right” is just wrong. On writing tasks, grok-4.3 was also the steadier editor. In `status-update-delay`, it was clearer and more professional, and it included the concrete hotfix timing of **15:10 UTC** instead of vaguely circling the plan. In `meeting-notes-summary`, it stayed closer to the source notes and kept the JSON cleaner, especially by not inflating status updates and action items into formal decisions. Llama-3.3-70B-Instruct was serviceable, but grok-4.3 was sharper, calmer, and more faithful. **Final call: grok-4.3 is the better model here, decisively. It wins not with flash, but with the far more valuable habit of being precise, compliant, and trustworthy under instruction.**

python-log-redactor

Practical coding — Python. Return code only. Write a function `redact_log(line: str) -> str` that masks sensitive values in application log lines before they are shipped to a vendor. Replace the value after `email=` with `[REDACTED_EMAIL]`, after `token=` with `[REDACTED_TOKEN]`, and after `ip=` with `[REDACTED_IP]`. Values end at the next space, comma, semicolon, or end of line. Preserve everything else exactly. Example input: `ts=2026-03-14T09:22:11Z level=INFO email=mira.chen@northbay.io action=login ip=203.44.18.9 token=sk_live_7Hk92aX note=ok` should become `ts=2026-03-14T09:22:11Z level=INFO email=[REDACTED_EMAIL] action=login ip=[REDACTED_IP] token=[REDACTED_TOKEN] note=ok`. Include any imports needed, but no explanation.

grok-4.3: import re def redact_log(line: str) -> str: line = re.sub(r'email=([^ ,;]*)', r'email=[REDACTED_EMAIL]', line) line = re.sub(r'token=([^ ,;]*)', r'token=[REDACTED_TOKEN]', line) line = re.sub(r'ip=([^ ,;]*)', r'ip=[REDACTED_IP]', line) return line

Llama-3.3-70B-Instruct: ```python import re def redact_log(line: str) -> str: return re.sub(r'email=([^,\s;]+)', 'email=[REDACTED_EMAIL]', re.sub(r'token=([^,\s;]+)', 'token=[REDACTED_TOKEN]', re.sub(r'ip=([^,\s;]+)', 'ip=[REDACTED_IP]', line))) # Example usage: log_line = "ts=2026-03-14T09:22:11Z level=INFO email=mira.chen@northbay.io action=login ip=203.44.18.9 token=sk_live_7Hk92aX note=ok" print(redact_log(log_line)) ```

A cleanly satisfies the prompt with code only, includes the needed import, and correctly redacts values up to space, comma, semicolon, or end of line while preserving delimiters. B violates the 'code only' constraint by wrapping in Markdown fences and adding example usage/printing, so it does not adhere to the instructions despite having broadly similar redaction logic.

status-update-delay

Professional writing — Write a Slack status update to your product and support teams. Audience: internal coworkers. Situation: the "Mercury Lane" billing export scheduled for 14:00 UTC is delayed because a schema change in the `invoice_adjustments` table broke the job at 13:52 UTC. Impact: CSV exports for 18 enterprise customers are delayed; no data loss; API and dashboard are unaffected. Current plan: hotfix by 15:10 UTC, rerun exports by 15:25 UTC, post next update at 15:00 UTC. Tone: calm, accountable, concise. Length: 90–130 words.

grok-4.3: **Mercury Lane Billing Export Update** Hi team, The Mercury Lane billing export scheduled for 14:00 UTC is delayed. A schema change in the `invoice_adjustments` table caused the job to fail at 13:52 UTC. This affects CSV exports for 18 enterprise customers. No data has been lost, and the API and dashboard continue to operate normally. We’re deploying a hotfix by 15:10 UTC and will rerun the affected exports by 15:25 UTC. I’ll post the next update at 15:00 UTC. Thanks for your patience while we resolve this.

Llama-3.3-70B-Instruct: "Hey product and support teams, just a heads up that the 14:00 UTC Mercury Lane billing export is delayed due to a schema change in the `invoice_adjustments` table that broke the job at 13:52 UTC. This affects CSV exports for 18 enterprise customers, but there's no data loss and API/dashboard functionality remains unaffected. We're working on a hotfix, aiming to rerun exports by 15:25 UTC. I'll post another update at 15:00 UTC with a status update. Thanks for your understanding and please reach out if you have any questions."

A is clearer, more professional, and fully includes the requested plan details, including the hotfix timing of 15:10 UTC. B is acceptable but slightly less concise/calm in tone and omits the explicit hotfix-by-15:10 UTC commitment.

meeting-notes-summary

Summarization & extraction — Read these meeting notes and then provide: (1) a 2-sentence summary, and (2) a JSON object with keys `launch_date`, `owner`, `blocked_by`, `budget_change_usd`, and `decisions` (array of short strings). Notes: - NimbusOak mobile app launch review, Tue 7 Jan. - Priya said App Store screenshots are done, but Android screenshots still need legal review. - Marco moved the target launch from Feb 3 to Feb 10 after QA found a crash on password reset for Android 12 only. - Fix is assigned to Lena; patch build expected by Friday morning. - Finance approved an extra $6,500 for paid acquisition testing in week one. - Team agreed not to change onboarding copy before launch. - Biggest blocker: waiting on legal sign-off for the Android screenshots. - Next checkpoint Thursday 16:30.

grok-4.3: The NimbusOak mobile app launch review on January 7 moved the target date from February 3 to February 10 after QA identified a password-reset crash on Android 12; Lena was assigned the fix with a patch build expected Friday morning. Android screenshots remain blocked pending legal review, an extra $6,500 was approved for week-one paid acquisition testing, the team decided against any onboarding-copy changes, and the next checkpoint was set for Thursday at 16:30. ```json { "launch_date": "Feb 10", "owner": "Marco", "blocked_by": "legal sign-off for Android screenshots", "budget_change_usd": 6500, "decisions": [ "Do not change onboarding copy before launch" ] } ```

Llama-3.3-70B-Instruct: Here is a 2-sentence summary of the meeting notes: The NimbusOak mobile app launch review discussed the current status of the app's launch preparations, including the completion of App Store screenshots and the pending legal review of Android screenshots. The team made several decisions, including pushing back the launch date to February 10, assigning a fix for an Android 12 issue, and approving extra funding for paid acquisition testing. And here is a JSON object with the extracted information: ```json { "launch_date": "Feb 10", "owner": "Marco", "blocked_by": "legal sign-off for Android screenshots", "budget_change_usd": 6500, "decisions": [ "Launch date changed to Feb 10", "Fix assigned to Lena", "Extra $6,500 for paid acquisition testing", "No change to onboarding copy before launch" ] } ```

Model A is more faithful to the notes and cleaner: its summary captures the key facts accurately without adding framing text, and its JSON avoids treating non-decision items as decisions. Model B is still mostly correct, but its `decisions` array includes status/actions like assigning a fix and approving budget, which are less clearly decisions than the explicit onboarding-copy agreement.

messy-orders-to-json

Data wrangling / structured output — Convert the messy order notes below into valid JSON only. Output must be an object with one key, `orders`, whose value is an array of objects sorted by `order_id` ascending. Each order object must have exactly these keys: `order_id` (string), `customer` (string), `items` (array of strings), `priority` ("low"|"normal"|"high"), `ship_by` (YYYY-MM-DD), and `gift` (boolean). Normalize priorities like rush/urgent => high, std => normal. If gift is missing, use false. Messy notes: # A-104 | customer: Velora Studio | items: "brass lamp"; "linen shade" | ship by 2026/04/09 | priority=std Order A-102, customer=Kite & Hollow, items=[walnut tray, candle set], rush, ship_by: 2026-04-05, gift=yes A-103 ; customer : Dr. Imani Sethi ; items : engraved pen ; ship by : 2026-04-07 ; priority : low order_id=A-101 customer="Parker Reef" items=ceramic mug|tea tin urgent ship-by=2026-04-04

grok-4.3: { "orders": [ { "order_id": "A-101", "customer": "Parker Reef", "items": [ "ceramic mug", "tea tin" ], "priority": "high", "ship_by": "2026-04-04", "gift": false }, { "order_id": "A-102", "customer": "Kite & Hollow", "items": [ "walnut tray", "candle set" ], "priority": "high", "ship_by": "2026-04-05", "gift": true }, { "order_id": "A-103", "customer": "Dr. Imani Sethi", "items": [ "engraved pen" ], "priority": "low", "ship_by": "2026-04-07", "gift": false }, { "order_id": "A-104", "customer": "Velora Studio", "items": [ "brass lamp", "linen shade" ], "priority": "normal", "ship_by": "2026-04-09", "gift": false } ] }

Llama-3.3-70B-Instruct: ```json { "orders": [ { "order_id": "A-101", "customer": "Parker Reef", "items": ["ceramic mug", "tea tin"], "priority": "high", "ship_by": "2026-04-04", "gift": false }, { "order_id": "A-102", "customer": "Kite & Hollow", "items": ["walnut tray", "candle set"], "priority": "high", "ship_by": "2026-04-05", "gift": true }, { "order_id": "A-103", "customer": "Dr. Imani Sethi", "items": ["engraved pen"], "priority": "low", "ship_by": "2026-04-07", "gift": false }, { "order_id": "A-104", "customer": "Velora Studio", "items": ["brass lamp", "linen shade"], "priority": "normal", "ship_by": "2026-04-09", "gift": false } ] } ```

Both outputs parse the orders correctly and match the required schema and sorting, but Model A follows the instruction to output valid JSON only. Model B wraps the JSON in Markdown code fences, which violates the format requirement.