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

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.

How they were tested

We ran 4 fresh text tasks, generated on the fly for this matchup so neither model could prepare in advance, and had gpt-5.4 score each one. grok-4.3 scored 38.0 to Llama-3.3-70B-Instruct's 26.0.

1. 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.

Winner: grok-4.3 — 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.

2. 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.

Winner: grok-4.3 — 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.

3. 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.

Winner: grok-4.3 — 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.

4. 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

Winner: grok-4.3 — 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.

See every prompt and the full side-by-side outputs in the interactive Head-to-Head.