LFM3 is Liquid AI's open-weight on-device model family you self-host. Kompozy is the self-serve alternative that generates and publishes finished content.
If Liquid AI's LFM3 caught your eye — the six open-weight 2B-class multimodal models announced October 27, 2026 — it helps to be clear about what you are actually choosing between. LFM3 is a family of model weights: text, vision, audio, omni, and agent models you download, fine-tune, and run on your own device. That is a genuinely clever release, and its on-device efficiency pitch is real.
But if you landed on an 'LFM3 alternative' search, the odds are you are not an ML engineer looking to embed a model — you want finished content published across platforms, and you want it without standing up a GPU, writing integration code, and building a pipeline around raw weights. That is a different job, and LFM3 does not do it: it generates and understands content, then stops. There is no captioning for feeds, reframing, formatting, scheduling, or publishing in it.
I run Kompozy, so I have a stake here, and I will keep this honest. Where LFM3 genuinely wins — private on-device inference, low latency, single-GPU fine-tuning, and a tiny footprint — this page says so plainly and points you to LFM3. What Kompozy is the alternative to is the assumption that open model weights are a content workflow. They are a starting point for builders; Kompozy is the finished product for creators, generating 18 formats and publishing across nine destinations, self-serve, in any browser.
Everything below is grounded in Liquid AI's launch material (and the launch-day coverage) plus Kompozy's own capabilities — no invented specs or prices. Several of LFM3's benchmark figures are self-reported and, as of this writing, not independently verified; this page treats them as such.
LFM3 is Liquid AI's third-generation Liquid Foundation Models — a family of six open-weight models in the 2B-parameter class, built from one backbone pre-trained on 22 trillion tokens. It spans a text model (LFM3-1.7B, 256K context), a vision model that reads images and video, an audio speech model, an 'omni' model that takes text, vision, and audio together and returns text and speech, and two 'decision' models for agentic action. It is designed to run on-device — Liquid AI says the text model fits in roughly 1 GB at 4-bit, is fine-tunable on a single GPU, and decodes faster than comparable small models locally. The weights are on Hugging Face, the Liquid Playground, and the Liquid AI API, under an 'LFM Open License' that launch coverage describes as free for commercial use below roughly a $10M annual-revenue threshold. What LFM3 does not do is leave the model layer. It can transcribe audio, describe an image, draft text, and act as a narrow on-device agent, but it does not caption for vertical feeds, reframe a clip, build a carousel, write a publish-ready blog, enforce a brand voice across many pieces, or post anything anywhere. The weights are the deliverable; everything a content program needs after that is yours to build or bring.
The reason to look past LFM3 is not that it is weak — it is that it solves a problem most creators do not have, and leaves the one they do have untouched. LFM3 answers 'how do I run a private, efficient multimodal model on my own hardware?' A creator's actual question is 'how do I turn one idea or one video into a week of on-brand posts across every platform?' Between those two sits transcription cleanup, vertical reframing, caption styling, carousel layout, blog and newsletter drafting, brand-voice governance, scheduling, and publishing — none of which LFM3 provides. Then there is the build cost. Open weights are free, but a workflow is not: using LFM3 to actually publish anything means hosting the model, writing the glue code, assembling the formatting and distribution layer, and maintaining it all. That is a software project with ongoing upkeep, not an afternoon. For an individual creator, marketer, or small team, that engineering burden is the wall. If your goal is to be seen everywhere, reliably, starting now, the missing layer is self-serve generation-at-scale plus publishing — and that is what an alternative has to provide.
| Feature | Liquid AI LFM3 | Kompozy | Note |
|---|---|---|---|
| Self-serve, no-code sign-up | No — download weights and build | Yes — open sign-up in a browser | LFM3 is for developers; Kompozy needs no engineering. |
| Runs fully on-device / private | Yes — local inference | No — cloud generation | On-device privacy is LFM3's genuine edge. |
| Finished content (not just model output) | No — raw text/vision/audio only | Yes — publish-ready posts | Kompozy turns output into finished pieces. |
| Multi-format output from one source | No | Yes — 18 formats | Kompozy fans one source into clips, carousels, blogs, and more. |
| Per-platform reframing + captions | No | Yes — 9:16 / 1:1 / 16:9, burned-in captions | Kompozy only. |
| Persona / avatar video from a script | No | Yes — Persona Shorts, Frames, HeyGen | LFM3 has no talking-head avatar generation. |
| Brand-voice governance | No | Yes — Persona Brief + banned words | LFM3 generates text, not a consistent voice across pieces. |
| Scheduling & autopilot | No | Yes — one queue, review gate or autopilot | Kompozy only. |
| Publishing to platforms | No | Yes — 9 destinations | LFM3 stops at model output. |
| Fine-tune a custom private model | Yes — single-GPU | No — uses managed third-party models | LFM3 wins for teams wanting a bespoke on-device model. |
| Open weights you can host anywhere | Yes — on Hugging Face | No — a finished product, not a model layer | LFM3 wins for builders who need portability and control. |
| Published, predictable pricing | Free weights + your compute/eng | Yes — public credit-based plans | LFM3 has no sticker price but a real engineering cost. |
| Tier | Liquid AI LFM3 plan | Liquid AI LFM3 price | Kompozy plan | Kompozy price |
|---|---|---|---|---|
| Entry | LFM3 open weights (self-hosted) | Free weights + your compute & engineering | Kompozy Starter | $199/mo (5,500 credits) |
| Mid | LFM3 via Liquid AI API | API access (pricing on Liquid AI; not detailed at launch) | Kompozy Pro | $499/mo (18,000 credits) |
| Top | LFM Open License (commercial) | Free below ~$10M revenue; licensed above (confirm on model card) | Kompozy Agency | $999/mo |
The cleanest way to think about it: LFM3 is a component for people who build software, and Kompozy is the finished product for people who make content. You could treat LFM3's open weights as the foundation of a home-grown content pipeline — host the model, write the integrations, build the formatting and publishing layer, and maintain all of it — or you could skip that entire project. Kompozy already is that pipeline: it generates content in 18 formats — persona and avatar shorts, clips, carousels, infographics, quote graphics, blogs, newsletters — all held to one Persona Brief, then captions, reframes, schedules, and publishes across the eight social platforms plus blog and email from a single review queue on autopilot. If LFM3 ever ends up embedded in a tool you use for on-device drafting or transcription, that sits upstream of Kompozy, not against it. But if your actual goal is reach rather than running your own model, Kompozy is the alternative that does the job open weights never will on their own — and you can start today, in a browser, with no engineering at all.
Not really — they are different layers. LFM3 is a foundation-model family a developer builds on; Kompozy is a finished content engine a creator uses. The only sense in which they compete is if you were considering building your own content workflow on open weights; Kompozy is the managed alternative to that project, generating and publishing across platforms with no engineering.
Not on its own. LFM3 generates and understands content — transcripts, image descriptions, text drafts — but it has no captioning, reframing, scheduling, or publishing. You would have to build that layer yourself. Kompozy provides it out of the box across eight social platforms plus blog and email.
LFM3 weights are free, but a content workflow built on them is not: you pay in hosting, engineering, and maintenance, and you still have to build the formatting and publishing layer. Kompozy bundles generation across 18 formats plus multi-platform publishing on a listed plan, so the comparison is free-weights-plus-a-dev-team versus a finished product you can use today.
When you want a private, efficient multimodal model running on your own device or embedded in your own app, and you have the technical capacity to fine-tune and host it. For on-device privacy, offline operation, and custom models, LFM3 is a strong pick and Kompozy is not the tool for that job.
Yes. If a tool you use embeds LFM3 for on-device transcription or drafting, treat that output as a raw ingredient: drop the file and text into Kompozy, and it turns them into clips, carousels, persona video, a blog, and a newsletter, all on one brand voice, and publishes them across platforms.