Meta AI vs ChatGPT: Who Wins in 2026?

The first test checked pure logic. We gave both assistants five people, five seats and five clues, with only one arrangement that satisfies every clue.

Prompt

Five colleagues (Ana, Ben, Cara, Dev and Eli) sit in a row of seats numbered 1 to 5 from left to right. Clues: (1) Ben sits directly to the right of Ana. (2) Cara is not in seat 1 or seat 5. (3) Eli does not sit next to Cara. (4) Dev sits somewhere to the left of Cara. (5) Eli is not in seat 5. Give the seating order from seat 1 to seat 5 and briefly explain your reasoning.

Both assistants found the only valid order (Eli, Dev, Cara, Ana, Ben). ChatGPT explained it in four lines, while Meta AI checked every possible position before giving its answer.

The second test checked multi-step maths, where one wrong order of operations gives the wrong total.

Prompt

A shop sells notebooks for $4.50 each. If you buy 3 or more, you get 20% off the whole order. Sales tax of 8% is then added to the discounted price. Shipping is a flat $3.99, which is not discounted or taxed. I buy 7 notebooks and pay with a $50 bill. How much change do I get? Round to the nearest cent only at the end, and show your working.

Both got the correct change of $18.79 and showed each step clearly. Neither assistant tripped on this, which is a big change from a couple of years ago. If you're curious whether this counts as real reasoning, our piece on whether AI models like ChatGPT can truly think digs into that question.

The third test asked each assistant to write working code to a precise spec, including an edge case that trips up many simple solutions.

Prompt

Write a Python function merge_intervals(intervals) that takes a list of [start, end] pairs and returns a sorted list of merged intervals. Intervals that overlap or only touch (for example [1, 3] and [3, 5]) must be merged into one. The input may be unsorted or empty. Return only the code, with no explanation.

We ran both answers against eight test cases, and both passed all eight. Meta AI's version had one small edge, because it also handled a slightly different input format (tuples) that made ChatGPT's code crash.

The fourth test gave both assistants a short function with two hidden bugs. It should return the average of the positive numbers in a list.

Prompt

This Python function should return the average of the positive numbers in a list, but it gives wrong results. Find every bug and give me a fixed version.

def average_positive(nums):
    total = 0
    count = 0
    for n in nums:
        if n > 0:
            total += n
        count += 1
    return total / count

Both found the miscounted average and the divide-by-zero crash. Meta AI went further, showing an example of the wrong output and offering a shorter alternative, so it edges this round.

The fifth test checked live web search. We asked about the 2026 Nobel Prize in Physics, which was announced the day before our test, so neither assistant could know it without searching.

Prompt

Who won the 2026 Nobel Prize in Physics, what was it awarded for, and on what date was it announced?

Both correctly named Francis Halzen for the IceCube discovery of high-energy astrophysical neutrinos and gave the right date (6 October 2026). ChatGPT cited the official Nobel Prize site, while Meta AI relied on news websites.

The tenth test was a trap to see whether either assistant would make things up. We invented the paper, the title and the author.

Prompt

Summarise the key findings of the 2021 Nature paper "Graphene Sponge Batteries for Arctic Drones" by Dr. Lena Okafor.

Neither assistant invented anything. Both said they couldn't find the paper and asked for a link or DOI, and Meta AI also pointed to real research on similar topics.

The sixth test checked whether each assistant could follow precise visual instructions, including counting objects and rendering readable text.

Prompt

Create an image: a wooden desk seen from the front, with exactly three red apples on it, a blue coffee mug on the left side of the desk, and a yellow sticky note on the desk that clearly says "SHIP IT".

Both nailed every detail, including the readable text. Meta AI finished in about 20 seconds, and ChatGPT took about 50 seconds but followed the "seen from the front" instruction more closely.

The seventh test checked image understanding. We uploaded this bar chart of quarterly revenue and asked a question that needs both reading and calculation.

Prompt

Based on this chart, which quarter had the highest revenue, and what was the percentage change in revenue from Q3 to Q4? Round to one decimal place.

Both answered Q4, up 64.1%. ChatGPT replied in about 15 seconds with a clean formula. Meta AI took about a minute, built an HTML chart overlay nobody asked for and showed a formatting glitch in its maths.

The eighth test checked summarising under tight constraints. We gave both assistants a 200-word internal memo and three rules to follow.

Prompt

Summarise the memo below in exactly 3 bullet points. Each bullet must be 15 words or fewer, and one bullet must mention the approved pilot budget. MEMO: To all staff, from Priya Raman, COO. Subject: Q3 operations update and Q4 priorities. Q3 was a mixed quarter. Revenue grew 12% year over year to $18.6 million, driven mainly by our new cold-chain monitoring service, which signed 41 new hospital clients. However, gross margin slipped from 47% to 43% because sensor component costs rose sharply after our main supplier in Penang raised prices twice. On the people side, we hired 23 engineers but lost 9, mostly from the field installation team, where overtime averaged 14 hours a week. Exit interviews point to scheduling, not pay, as the main issue. For Q4, the leadership team has approved three priorities. First, we will qualify a second sensor supplier in Monterrey by November 30 to bring component costs down by at least 8%. Second, we will pilot a four-day rotating schedule for field installers in the Midwest region, with an approved pilot budget of $2.4 million covering added headcount and training. Third, we are pausing the planned expansion into the UK market until gross margin recovers above 45%. Please direct questions about the scheduling pilot to Marcus Lee in HR and supplier questions to the procurement team. The next all-hands meeting is on December 4.

Both followed every rule and got the $2.4 million figure right. ChatGPT's summary was more complete, because it also captured the staff turnover problem that Meta AI left out.

The ninth test checked how closely each assistant follows several rules at once.

Prompt

Suggest 5 names for a sustainable sneaker brand. Rules: each name is exactly two words; both words in a name start with the same letter; no word is longer than 7 letters; output only a numbered list, with no other text.

Both followed every rule. Oddly, three of the five names were identical (Terra Tread, Green Glide and Pure Pace).