AI Company Profile
Meta
Muse Image launched publicly in Meta AI; Muse Video was previewed as a coming-soon video generation experience rather than a fully available public launch.
Company Overview
Meta is tracked in the Kingy AI company directory because it is connected to public AI launch and tool records. Muse Image launched publicly in Meta AI; Muse Video was previewed as a coming-soon video generation experience rather than a fully available public launch.
The current public graph connects 8 launches and 2 tools to Meta. That turns this profile into a working research hub: use it to move from the company to its product surface, then into dated launch records with source checks, verification status, and related Kingy AI context.
For AI Artists, Creators, Designers, Marketers, Small Business Owners, the useful question is not only what Meta says about itself. The useful question is what the launch pattern shows: which categories the company is active in, which tools have durable profiles, whether pricing and demos are clear, and whether the source trail is strong enough for deeper editorial or creator coverage.
AI Product Evidence
This company profile is backed by a linked AI launch record: Muse Image launched publicly in Meta AI; Muse Video was previewed as a coming-soon video generation experience rather than a fully available public launch.
Research Notes
When reviewing Meta, check the official product surface, docs, demos, pricing, model or API pages, and linked Kingy AI launch records before relying on claims for buying, writing, comparison, or creator-coverage decisions.
Source-Backed Profile Notes
This profile is checked against public source links where available. Official product pages, documentation, model pages, pricing pages, launch announcements, and verified company pages should carry more weight than social posts or unsourced summaries.
Market Position
Kingy AI currently classifies Meta around AI Image Tools, AI Models, AI Video Tools. Those categories are not meant to be a marketing slogan; they are a practical way to understand where the company shows up in the launch database and how it may intersect with product strategy, adoption, search demand, and creator education.
Audience Fit
This profile is especially useful for AI Artists, Creators, Designers, Marketers, Small Business Owners. A complete read should start with the company snapshot, continue through the linked tools and launch timeline, and end with the source panel so claims can be checked before a buying decision, article, comparison, or video brief.
Latest Tracked Move
The most recent linked launch for Meta is Ray-Ban Meta Gen 3 / new Meta glasses family from September 23, 2026. Ray-Ban Meta Gen 3 is on sale. The wider glasses announcement also contains limited editions and products with later sale dates. Access: Gen 3 available; family contains limited editions.
Product Surface
One linked tool profile is Llama. Meta’s model family; this buying review scopes the older Llama 2 chat workflow actually demonstrated. The tool profile is where Kingy AI keeps product-level details such as what it does, pricing clarity, API availability, alternatives, related launches, and source-backed evaluation notes.
Launch Graph And Timeline
The launch graph is the most important part of this page. It shows how Meta appears through dated AI product events instead of a generic company description. Each launch record can include category, audience, source links, pricing notes, demo signals, scoring, and a best-next-link path for deeper research.
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Ray-Ban Meta Gen 3 is on sale. The wider glasses announcement also contains limited editions and products with later sale dates. Access: Gen 3 available; family contains limited editions.
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Ray-Ban Meta Audio glasses open for preorder on September 23, with shipment scheduled for October 13. Access: Preorders; ships October 13.
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Meta Ray-Ban Display expands sales to Canada and the UK, with preorders in France, Italy and Germany. Access: Canada/UK sales; France/Italy/Germany preorders.
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Muse Image launched publicly in Meta AI; Muse Video was previewed as a coming-soon video generation experience rather than a fully available public launch.
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Meta introduced the Llama 4 herd with Scout and Maverick as open-weight natively multimodal mixture-of-experts models, plus a Behemoth preview.
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Meta released Llama 3.2 with 11B and 90B vision models plus lightweight 1B and 3B text models for edge and mobile devices.
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Meta launched Llama 3.1 including the 405B open-weight frontier model, upgraded 8B and 70B models, 128K context, multilingual support, and tool use.
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Meta released Llama 3 with 8B and 70B pretrained and instruction-tuned language models, plus safety tools for developers.
Company Links
Tool Portfolio
The tool portfolio section turns the company profile into a navigable product map. For Meta, Kingy AI currently links 2 tools that can be reviewed separately for pricing, demos, use cases, alternatives, related launches, and source-backed notes.
Llama
Meta’s model family; this buying review scopes the older Llama 2 chat workflow actually demonstrated.
- Pricing
- Canada/CAD: C$0 Llama 2 weight fee; hosting separate
- API
- Unknown
Muse Image
Meta’s Muse Image hosted generation/editing model; Muse Video preview is a separate product.
- Pricing
- US/USD fal: US$0.01 per generated image
- API
- yes
Launch Record Cards
Use these cards when you want the shorter scan view: category, launch date, scores, open/API/free-plan signals, and the path into the full launch profile.
Ray-Ban Meta Gen 3 / new Meta glasses family
Ray-Ban Meta Gen 3 is on sale. The wider glasses announcement also contains limited editions and products with later sale dates. Access: Gen 3 available; family contains limited…
Check the exact Ray-Ban Meta frame and regional listing. Gen 3 availability does not mean every family member ships immediately.
Ray-Ban Meta Audio glasses
Ray-Ban Meta Audio glasses open for preorder on September 23, with shipment scheduled for October 13. Access: Preorders; ships October 13.
Ray-Ban Meta Audio has a preorder event on September 23. Verify the scheduled October 13 shipment before buying for a fixed deadline.
Meta Ray-Ban Display regional expansion
Meta Ray-Ban Display expands sales to Canada and the UK, with preorders in France, Italy and Germany. Access: Canada/UK sales; France/Italy/Germany preorders.
Meta Ray-Ban Display access depends on country and shipping status. Announced fall hologram features remain future access.
Muse Image launched publicly; Muse Video was previewed rather than fully launched
Muse Image launched publicly in Meta AI; Muse Video was previewed as a coming-soon video generation experience rather than a fully available public launch.
- Launch readiness
- 7.2 / 10
- Demo evidence
- Not scored yet
- Creator-story fit
- Not scored yet
Score definitions and rubric
These are launch-record readiness heuristics, not product ratings.
Launch readiness
How complete and reviewable the launch record is, not the quality of the product.
Inputs and weights: Launch date 15%; qualifying source 10%; what launched 10%; demo 15%; category 10%; audience 10%; editorial assessment 10%; traction evidence 10%; creator or audience fit 10%.
Evidence inputs: Reviewed launch metadata, public source links, demo links, taxonomy, audience, editorial notes, and recorded traction signals.
Demo evidence
Whether the record contains useful, reviewable demonstration evidence; it is not a rating of product output quality.
Inputs and weights: Working demo URL 45%; video walkthrough 25%; clear description of what launched 10%; audience 10%; editorial assessment 10%.
Evidence inputs: Demo and video URLs plus the reviewed launch description, audience, and editorial notes.
Creator-story fit
Whether a launch has enough demonstrable evidence and audience relevance for a useful creator story; it does not predict views or guarantee coverage.
Inputs and weights: Demo evidence 25%; visual creator category 15%; audience 15%; editorial assessment 15%; traction evidence 10%; pricing clarity 10%; API or open-weight evidence 10%.
Evidence inputs: Reviewed demo, category, audience, editorial, traction, pricing, API, and open-weight fields.
- Scale
- 0.0–10.0. A present qualifying input receives its published weight; a missing input receives zero. Scores are rounded to one decimal.
- Assigned by
- Suggested by the deterministic field-completeness helper and assigned or approved by a Kingy editorial reviewer.
- Rubric and check date
- Rubric version P0-2026-08-10. The record’s “Last verified” date is the score check date. Checked: 2026-07-08.
- Confidence and missing data
- Confidence depends on source completeness. “Not scored yet” means no reviewed value; “Needs review” means the value or score set failed validation.
- Freshness
- Recalculate after a material launch, source, demo, pricing, audience, or traction change and during the record freshness review.
- Disputes
- Use “Suggest a correction” on the record and cite the relevant evidence. Commercial relationships cannot buy or alter a score.
Meta launched Muse Image publicly in Meta AI — free for everyday creation with subscription paths for more — while previewing Muse Video as…
The Llama 4 herd
Meta introduced the Llama 4 herd with Scout and Maverick as open-weight natively multimodal mixture-of-experts models, plus a Behemoth preview.
- Launch readiness
- 7.2 / 10
- Demo evidence
- Not scored yet
- Creator-story fit
- Not scored yet
Score definitions and rubric
These are launch-record readiness heuristics, not product ratings.
Launch readiness
How complete and reviewable the launch record is, not the quality of the product.
Inputs and weights: Launch date 15%; qualifying source 10%; what launched 10%; demo 15%; category 10%; audience 10%; editorial assessment 10%; traction evidence 10%; creator or audience fit 10%.
Evidence inputs: Reviewed launch metadata, public source links, demo links, taxonomy, audience, editorial notes, and recorded traction signals.
Demo evidence
Whether the record contains useful, reviewable demonstration evidence; it is not a rating of product output quality.
Inputs and weights: Working demo URL 45%; video walkthrough 25%; clear description of what launched 10%; audience 10%; editorial assessment 10%.
Evidence inputs: Demo and video URLs plus the reviewed launch description, audience, and editorial notes.
Creator-story fit
Whether a launch has enough demonstrable evidence and audience relevance for a useful creator story; it does not predict views or guarantee coverage.
Inputs and weights: Demo evidence 25%; visual creator category 15%; audience 15%; editorial assessment 15%; traction evidence 10%; pricing clarity 10%; API or open-weight evidence 10%.
Evidence inputs: Reviewed demo, category, audience, editorial, traction, pricing, API, and open-weight fields.
- Scale
- 0.0–10.0. A present qualifying input receives its published weight; a missing input receives zero. Scores are rounded to one decimal.
- Assigned by
- Suggested by the deterministic field-completeness helper and assigned or approved by a Kingy editorial reviewer.
- Rubric and check date
- Rubric version P0-2026-08-10. The record’s “Last verified” date is the score check date. Checked: 2026-06-08.
- Confidence and missing data
- Confidence depends on source completeness. “Not scored yet” means no reviewed value; “Needs review” means the value or score set failed validation.
- Freshness
- Recalculate after a material launch, source, demo, pricing, audience, or traction change and during the record freshness review.
- Disputes
- Use “Suggest a correction” on the record and cite the relevant evidence. Commercial relationships cannot buy or alter a score.
Meta introduced the Llama 4 herd with Scout and Maverick as open-weight natively multimodal mixture-of-experts models, plus a Behemoth preview (ai.meta.com). The release shifts…
Llama 3.2 vision and edge models
Meta released Llama 3.2 with 11B and 90B vision models plus lightweight 1B and 3B text models for edge and mobile devices.
- Launch readiness
- 8.0 / 10
- Demo evidence
- Not scored yet
- Creator-story fit
- Not scored yet
Score definitions and rubric
These are launch-record readiness heuristics, not product ratings.
Launch readiness
How complete and reviewable the launch record is, not the quality of the product.
Inputs and weights: Launch date 15%; qualifying source 10%; what launched 10%; demo 15%; category 10%; audience 10%; editorial assessment 10%; traction evidence 10%; creator or audience fit 10%.
Evidence inputs: Reviewed launch metadata, public source links, demo links, taxonomy, audience, editorial notes, and recorded traction signals.
Demo evidence
Whether the record contains useful, reviewable demonstration evidence; it is not a rating of product output quality.
Inputs and weights: Working demo URL 45%; video walkthrough 25%; clear description of what launched 10%; audience 10%; editorial assessment 10%.
Evidence inputs: Demo and video URLs plus the reviewed launch description, audience, and editorial notes.
Creator-story fit
Whether a launch has enough demonstrable evidence and audience relevance for a useful creator story; it does not predict views or guarantee coverage.
Inputs and weights: Demo evidence 25%; visual creator category 15%; audience 15%; editorial assessment 15%; traction evidence 10%; pricing clarity 10%; API or open-weight evidence 10%.
Evidence inputs: Reviewed demo, category, audience, editorial, traction, pricing, API, and open-weight fields.
- Scale
- 0.0–10.0. A present qualifying input receives its published weight; a missing input receives zero. Scores are rounded to one decimal.
- Assigned by
- Suggested by the deterministic field-completeness helper and assigned or approved by a Kingy editorial reviewer.
- Rubric and check date
- Rubric version P0-2026-08-10. The record’s “Last verified” date is the score check date. Checked: 2026-06-08.
- Confidence and missing data
- Confidence depends on source completeness. “Not scored yet” means no reviewed value; “Needs review” means the value or score set failed validation.
- Freshness
- Recalculate after a material launch, source, demo, pricing, audience, or traction change and during the record freshness review.
- Disputes
- Use “Suggest a correction” on the record and cite the relevant evidence. Commercial relationships cannot buy or alter a score.
Meta released Llama 3.2 — 11B and 90B vision models plus lightweight 1B and 3B text models for edge and mobile — with weights…
Introducing Llama 3.1
Meta launched Llama 3.1 including the 405B open-weight frontier model, upgraded 8B and 70B models, 128K context, multilingual support, and tool use.
- Launch readiness
- 7.3 / 10
- Demo evidence
- Not scored yet
- Creator-story fit
- Not scored yet
Score definitions and rubric
These are launch-record readiness heuristics, not product ratings.
Launch readiness
How complete and reviewable the launch record is, not the quality of the product.
Inputs and weights: Launch date 15%; qualifying source 10%; what launched 10%; demo 15%; category 10%; audience 10%; editorial assessment 10%; traction evidence 10%; creator or audience fit 10%.
Evidence inputs: Reviewed launch metadata, public source links, demo links, taxonomy, audience, editorial notes, and recorded traction signals.
Demo evidence
Whether the record contains useful, reviewable demonstration evidence; it is not a rating of product output quality.
Inputs and weights: Working demo URL 45%; video walkthrough 25%; clear description of what launched 10%; audience 10%; editorial assessment 10%.
Evidence inputs: Demo and video URLs plus the reviewed launch description, audience, and editorial notes.
Creator-story fit
Whether a launch has enough demonstrable evidence and audience relevance for a useful creator story; it does not predict views or guarantee coverage.
Inputs and weights: Demo evidence 25%; visual creator category 15%; audience 15%; editorial assessment 15%; traction evidence 10%; pricing clarity 10%; API or open-weight evidence 10%.
Evidence inputs: Reviewed demo, category, audience, editorial, traction, pricing, API, and open-weight fields.
- Scale
- 0.0–10.0. A present qualifying input receives its published weight; a missing input receives zero. Scores are rounded to one decimal.
- Assigned by
- Suggested by the deterministic field-completeness helper and assigned or approved by a Kingy editorial reviewer.
- Rubric and check date
- Rubric version P0-2026-08-10. The record’s “Last verified” date is the score check date. Checked: 2026-06-08.
- Confidence and missing data
- Confidence depends on source completeness. “Not scored yet” means no reviewed value; “Needs review” means the value or score set failed validation.
- Freshness
- Recalculate after a material launch, source, demo, pricing, audience, or traction change and during the record freshness review.
- Disputes
- Use “Suggest a correction” on the record and cite the relevant evidence. Commercial relationships cannot buy or alter a score.
Meta launched Llama 3.1, including the 405B open-weight model alongside upgraded 8B and 70B models, 128K context, multilingual support, and tool use (ai.meta.com). Developers,…
Introducing Meta Llama 3
Meta released Llama 3 with 8B and 70B pretrained and instruction-tuned language models, plus safety tools for developers.
- Launch readiness
- 7.2 / 10
- Demo evidence
- Not scored yet
- Creator-story fit
- Not scored yet
Score definitions and rubric
These are launch-record readiness heuristics, not product ratings.
Launch readiness
How complete and reviewable the launch record is, not the quality of the product.
Inputs and weights: Launch date 15%; qualifying source 10%; what launched 10%; demo 15%; category 10%; audience 10%; editorial assessment 10%; traction evidence 10%; creator or audience fit 10%.
Evidence inputs: Reviewed launch metadata, public source links, demo links, taxonomy, audience, editorial notes, and recorded traction signals.
Demo evidence
Whether the record contains useful, reviewable demonstration evidence; it is not a rating of product output quality.
Inputs and weights: Working demo URL 45%; video walkthrough 25%; clear description of what launched 10%; audience 10%; editorial assessment 10%.
Evidence inputs: Demo and video URLs plus the reviewed launch description, audience, and editorial notes.
Creator-story fit
Whether a launch has enough demonstrable evidence and audience relevance for a useful creator story; it does not predict views or guarantee coverage.
Inputs and weights: Demo evidence 25%; visual creator category 15%; audience 15%; editorial assessment 15%; traction evidence 10%; pricing clarity 10%; API or open-weight evidence 10%.
Evidence inputs: Reviewed demo, category, audience, editorial, traction, pricing, API, and open-weight fields.
- Scale
- 0.0–10.0. A present qualifying input receives its published weight; a missing input receives zero. Scores are rounded to one decimal.
- Assigned by
- Suggested by the deterministic field-completeness helper and assigned or approved by a Kingy editorial reviewer.
- Rubric and check date
- Rubric version P0-2026-08-10. The record’s “Last verified” date is the score check date. Checked: 2026-06-08.
- Confidence and missing data
- Confidence depends on source completeness. “Not scored yet” means no reviewed value; “Needs review” means the value or score set failed validation.
- Freshness
- Recalculate after a material launch, source, demo, pricing, audience, or traction change and during the record freshness review.
- Disputes
- Use “Suggest a correction” on the record and cite the relevant evidence. Commercial relationships cannot buy or alter a score.
Meta released Llama 3 with open-weight 8B and 70B pretrained and instruction-tuned models plus developer safety tools (ai.meta.com). Developers, Enterprises, and Researchers got what…
Kingy coverage
Kingy AI articles about Meta and its products, newest first.
- BlogMeta Muse Spark Open: Benchmarks, Specs, Hardware and Costs
- NewsMeta Launches Muse Wonder: AI Agents Built to Remember and Keep Working
- BlogIs Muse Wonder Full Worth $50 a Month? Real Tasks, Usage Limits and Finished Projects
- NewsMeta Enterprise Platform Explained: Muse, Business Agent, API and Code
- BlogAmazon Blocked Meta’s Muse From Shopping. The Fight Is About Who Controls the Checkout
- NewsMeta Launches Muse, an AI Agent That Can Shop, Email and Plan Trips for You
- BlogMeta Muse: Features, Specs, Privacy and How It Compares
- BlogGemini 3.8 Flash vs Muse Spark 1.3: 232-Call Benchmark
More coverage (12)
- BlogMeta Muse Spark 1.3 Review: Real World Performance, Benchmarks & Verdict
- BlogMeta Muse Voice Transcribe: Benchmarks, Pricing, API Limits, and What It Changes
- BlogMeta Muse Glimmer 30B: Benchmarks, Hardware, Pricing and How to Run It
- BlogMuse Code and Muse Spark 1.2: Meta’s Benchmarks vs the Verified Leaderboard
- BlogMuse Spark 1.1 Benchmarks: Meta’s New Agent Model Is Cheap, Long-Context, and Surprisingly Competitive
- BlogMeta Muse Image Is Here: Why Meta Built Its New AI Image Model for Social Feeds
- NewsMeta Starfire Kylie Edition Review: Stylish AI Glasses, Not a Gen 2 Upgrade
- NewsMeta Is Building an AI Clone of Mark Zuckerberg — And It’s Wilder Than You Think
- NewsMeta Snaps Up Thinking Machines Lab Co-Founder Amid AI Talent Wars
- NewsLlama 3.1 Nemotron Ultra: a New Standard for Efficient AI Solutions
- NewsLlama 4: The New Frontier in Multimodal AI – Includes Benchmarks
- NewsModern AI on Vintage Hardware: How Llama 2 Brought Windows 98 Back to Life
How To Evaluate This Company
A useful Meta review should combine company-level context with product-level evidence. Start with the official site, then inspect the linked tools and launch records for source quality, pricing clarity, demo availability, audience fit, and how recently the profile was verified.
- Check whether the company has a clear official product path, documentation, demo, pricing page, or public launch announcement.
- Compare the linked tool profiles against the launch timeline to see whether the product story is current or stale.
- Use the category and audience tags as discovery aids, then verify claims through the source links before making a buying, writing, or creator-coverage decision.
- Treat unknown funding, founder, or contact fields as research gaps, not negative signals. They identify where the profile needs more public evidence.
- When a launch or tool looks important but under-documented, submit a correction or related launch so the graph can be improved.
Editorial And Creator Coverage Notes
For Kingy AI, Meta is most interesting when the company has a clear product change, a useful demo surface, a founder or team story, a strong comparison angle, or a launch that helps buyers understand where the AI market is moving. The current graph gives editors and creators a starting point without pretending that every profile is already complete.
Good coverage candidates usually have a specific workflow, visible product proof, a concrete audience, and enough official or high-quality public sources to avoid thin summaries. If those pieces are missing, this page should be read as a research queue as much as a company profile.
Verification And Source Notes
The verification panel below summarizes profile freshness and key source checks. It is intentionally visible because AI company pages become low-trust quickly when dates, product claims, funding notes, or source links are not kept current.
Verification & Sources
- Evidence state
- Recheck due
- Source links
- 2
- Freshness
- Needs recheck: checked July 8, 2026
- Last updated
- June 8, 2026
What this evidence state means
- Definition
- The claim was previously checked, but its review window expired or a material change may have invalidated it.
- Required provenance
- The prior evidence and check date are retained, together with the expiry or change signal that triggered recheck.
- Owner
- Kingy freshness queue owner and assigned editorial reviewer
- Freshness rule
- This is already outside its freshness rule. It must not be presented as current until reviewed against current evidence.
- Disputes and corrections
- Use “Suggest a correction” on the record. Kingy editorial reviews the cited evidence, records material corrections, and changes or removes the state when it is not supported.
Key source checks
Suggest a correction
Creator Coverage Next Steps
This company has launch, tool, or audience signals that may support demos, reviews, creator education, founder storytelling, or practical product explainers.