Models
Meta
AI Model Benchmark Comparison

MetaX · Jul 9, 2026 · official

AI Model Benchmark Comparison

Meta's table chart drew 94.8K views on X: 2.6x its median post and #391 of 1,162 AI chart posts we hold by views. Read Oct 12, 2026.

AI model benchmark scores across agent, coding, and multimodal tasks

XData Table chart Muse Spark All Meta modelsMuse 1.1Gemini 3.1 Pro (high)Opus 4.8 (max)GPT 5.5 (xhigh)

Meta: AI Model Benchmark Comparison — Published by Meta on X, Jul 9, 2026. Source: x.com

50/ 100

ViralChart score

Wide but thin

More views than the median post but a lower engagement rate: many saw it, few acted. Common for big accounts.

#616 of 1,162 AI chart posts we hold · #12 of 31 Meta posts. Score = views 40%, likes 30%, engagement rate 15%, save rate 15%, each as a percentile of every AI chart post we hold.

Our read

It lands in Wide but thin, with a score rank of #616 of 1,162 AI chart posts.

Its save rate of 0.027% sits at the 14th percentile among AI chart posts calmer than the 0.076% median.

The post ranked #1 of 4 by views among posts from this account that day.

At 94.8K views it ran 2.6x the company median of 36.4K, ranking #8 of 31 Meta posts.

0.11views per follower

94.8K views from an account with 865.4K followers.

bottom 14%save rate

0.027% of viewers bookmarked it to come back to. The median post gets 0.076%.

4posts from Meta that day

Part of a run of 4 posts on Jul 9, 2026. This one was #1 of 4 by views that day.

2.6xMeta's median post

94.8K views, where Meta's typical post gets 36.4K.

Public X numbers from our read on Oct 12, 2026. Peers: 1,162 AI chart posts we hold and 31 from Meta.

Where it sits

Reach against resonance

Each dot is a post. Right is more views, up is a higher engagement rate. The lines are the medians, so the four corners are the four kinds of post.

Among 1,162 AI chart posts we hold

Score #616 of 1,162

Views	94.8K	#391	top 34%
Likes	370	#517	top 44%
Engagement rate	0.51%	#821	bottom 29%
Save rate	0.027%	#1,003	bottom 14%

Among 31 Meta posts

Score #12 of 31

Views	94.8K	#8	top 24%
Likes	370	#11	top 34%
Engagement rate	0.51%	#23	bottom 27%
Save rate	0.027%	#29	bottom 8%

Growth

One read so far

Views94.8K

Age at our read95 days

Views per day of life995views divided by days since it was posted

Dots are our reads. The dotted line from the day it was posted to our first read is a gap, not data.

Meta's posts over time

Every post from this account we hold, by date and views. This one is marked; the line is the running median of the last 10.

News · Jul 9, 2026

Meta posts AI model benchmark comparison table chart

The July 9, 2026 table chart drew 94.8K views and 370 likes, with a save rate of 0.027%.

What happened

Meta published a table chart on July 9, 2026 comparing AI model benchmark scores across agent, coding, and multimodal tasks. It was one of 4 posts from the account that day, alongside Meta Internal Coding Bench, Multi-agent vs Single Agent Performance, and Vibe Code Bench v1.1 - SWE Atlas Codebase.

The numbers

It reached 94.8K views, ranking #391 of 1,162 AI chart posts and #8 of 31 Meta posts. Its score rank is #616 of 1,162 AI chart posts.

The lesson

A save rate of 0.027% against the 0.076% median among AI chart posts shows this table chart was read more than kept. The 5-model comparison drew 94.8K views but ranked #1,003 of 1,162 AI chart posts on saves.

What it means

At #12 of 31 Meta posts by score, this chart sits mid-pack for the account despite 2.6x the company median views. The 0.027% save rate against the 0.076% median marks the gap between views and bookmarks.

Written by ViralChart from public numbers read on Oct 12, 2026. Every number is checked against our data before it is shown.

Updates: what Meta posted next

Data and API

54 points read off this chart

Estimated from the image 81%

Series	Group	Value	Read
Muse Meta 1.1	MCP Atlas	88.1 score	printed
Muse Spark	MCP Atlas	82.2 score	printed
Gemini 3.1 Pro (high)	MCP Atlas	78.2 score	printed
Opus 4.8 (max)	MCP Atlas	82.2 score	printed
GPT 5.5 (xhigh)	MCP Atlas	75.3 score	printed
Muse Meta 1.1	JobBench	54.7 score	printed
Muse Spark	JobBench	17 score	printed
Gemini 3.1 Pro (high)	JobBench	49.4 score	printed
Opus 4.8 (max)	JobBench	15.9 score	printed
GPT 5.5 (xhigh)	JobBench	48.4 score	printed
Muse Meta 1.1	Toolathon-Verified	75.6 score	printed
Muse Spark	Toolathon-Verified	49.4 score	printed

Showing 12 of 54. All points, the redraw and the maker

Max plan · API

GET https://viral-chart.com/api/posts/877395712500.json
GET https://viral-chart.com/api/charts/877395712500.json
GET https://viral-chart.com/api/charts/877395712500.skill.md

Make one like this Open the post JSON Chart data JSON Skill file API docs

Static files in this prototype. The post JSON holds the public numbers, ranks, score, quadrant, our reads and, when checked, our copy.

API · Max

Use this chart via API

On the Max plan you can pull this chart's numbers with the provenance of each one, as JSON or CSV, with its redraw spec, or call it from an agent over MCP. Chart id 877395712500.

See the Max plan API docs

	Path	Returns	Plan
GET	`/api/v1/charts/877395712500`	Title, publisher, date, form, point count; with a Max key, the data link	Open
GET	`/api/v1/charts/877395712500/data`	Every point with its provenance and confidence, plus the redraw spec. Add ?format=csv for CSV	Max
GET	`/api/v1/charts/877395712500/render?view=1`	The draw-only view this page uses	Open
POST	`/mcp`	Tool get_chart_data with this id, for Claude, Cursor and other agents	Max

curlMax

curl -s https://viral-chart.com/api/v1/charts/877395712500/data \
  -H "Authorization: Bearer $VIRALCHART_KEY"

Claude CodeMax

claude mcp add --transport http viralchart https://viral-chart.com/mcp \
  --header "Authorization: Bearer $VIRALCHART_KEY"

60 requests a minute per key; over that you get 429 with Retry-After. Up to 5 keys per account, made on your account page and shown once. Without Max, the data route answers 402 with a link to the plans. The numbers describe a chart its publisher owns, and every file carries a license note.