LTX-2.5 can generate a 10-second AI video from an image in just 6.8 seconds on Nvidia superchips — and it's open weights

LTX , the open world model company spun out of Lightricks, today released LTX-2.5, the newest version of its open-weights video and "world" model and it arrives natively integrated into ComfyUI , the node-based workflow tool that has become the de facto prototyping environment for open generative media, through a strategic day-one launch partnership between the two companies. The model is available now as open weights on Hugging Face , inside ComfyUI, and through the LTX API for teams that want managed generation. It is free to use for organizations under $10 million in annual recurring revenue; larger companies negotiate a license. LTX says its models have passed 33 million downloads, making the LTX family the most-used "open world" model line on the market. Ahead of the launch, VentureBeat spoke exclusively with LTX co-founder and CEO Zeev Farbman and ComfyUI co-founder and CEO Yoland Yan about the release, the partnership, and why both companies are betting that open weights — not closed APIs — will win the video and world model market. "We're trying to maintain the same efficiency and the inference speed that we're known for, but constantly pushing the quality up," Farbman said. "We are introducing many cool things in this release: multi-shot support, a diffusion decoder for better quality, new conditioning modes, better support for autoregressive models that are critical for real-time use cases and robotics." What's new in LTX-2.5 According to the company's announcement, LTX-2.5 rebuilds nearly every stage of the generation pipeline rather than bolting new capabilities onto an older core. The headline changes: A new diffusion video decoder that reduces visual artifacts in high-motion footage and reconstructs fine detail like text and faces, while preserving LTX's high compression ratio. Native multishot generation that renders a full sequence as a single output, holding character, scene, and voice consistent across cuts rather than stitching individually generated shots together. A custom Gemma 4 language backbone and dedicated prompt enhancer for more accurate handling of complex, multi-subject prompts. A pretrained checkpoint tuned for physical AI and robotics giving teams a base to fine-tune on domain data that looks nothing like cinematic video. A substantially improved distilled model that delivers near-full-model quality at lower cost and faster inference, and, through an optimization effort with NVIDIA, runs locally on NVIDIA RTX GPUs with reduced memory requirements. The company claims roughly one-eighth the cost and one-seventh the render time of comparable models, with output that runs on hardware ranging from data center GPUs down to a Mac. Checked against published rates, the cost multiple doesn't survive contact with the models that publish pricing. LTX-2.5 generates 720p video with audio at $0.09 per second on its Fast tier, putting a 10-second clip at $0.90 — genuinely cheap, but about one-quarter the cost of full Veo 3.1 ($4.00), half of FLUX 3 Video ($1.70) and HappyHorse 1.0 (~$1.82), and only 10% under Google's budget tiers, Veo 3.1 Fast and Gemini Omni Flash ($1.00 each), while Veo 3.1 Lite ($0.50) is actually cheaper. Nothing in the published field costs eight times LTX's rate; if the one-eighth figure holds anywhere, it would be against premium models like Kling 3.0 Pro or Seedance 2.5 that don't publish comparable per-second pricing — or against self-hosting the open weights, where the marginal cost is whatever your GPU costs to run. The render-time multiple is better supported, at least by LTX's own end-to-end measurements: 6.8 seconds for a 10-second clip against 52 seconds for the fastest rival API (Gemini Omni Flash) is roughly one-seventh — though that figure comes from self-hosting on two GB200 superchips, and through LTX's own managed API the same job took 23.7 seconds, cutting the advantage to about half. Here is how the published rates compare, normalized to the cost of a finished 10-second 720p clip with synchronized audio — the configuration LTX and Black Forest Labs have both used for their own evaluations: Rank Model Per second Per 10-second clip Unique differentiator Notes 1 Veo 3.1 Lite (Google) $0.05 $0.50 The category's price floor — cheapest published rate anywhere No 4K, no clip extension 2 LTX-2.5 Fast (Lightricks) $0.09 $0.90 Only open-weights model in the field — self-host free under $10M ARR, fine-tuning permitted Scales to 4K at $0.30/sec; up to 20s single generation at 24/25 fps 3 Veo 3.1 Fast (Google) $0.10 $1.00 Cheapest closed-API path to 4K ($0.30/sec) Budget tier of the Veo line 3 Gemini Omni Flash (Google) $0.10 $1.00 Independently measured quality leader — tops both Artificial Analysis text-to-video arenas as of Aug 2026 720p only; 10-second maximum; best iteration tooling 5 LTX-2.5 Pro (Lightricks) $0.12 $1.20 Quality-tuned tier of the only open-weights family — prompt adherence, faces, typography (vendor-described) Tops out at 1080p and 10 seconds 6 FLUX 3 Video (Black Forest Labs) $0.17 $1.70 First to ship 20-second single-generation clips with audio (July 2026) — a ceiling since matched by LTX-2.5 Fast HD band; audio included; Draft tier at $0.06/sec ($0.60/clip, HD only) 7 HappyHorse 1.0 (Alibaba) ~$0.182 ~$1.82 Arena quality leader at launch (April 2026), since overtaken; "open source" claims never matched by verified downloadable weights Third-party reseller rate; audio included at no extra charge 8 Veo 3.1 (Google) $0.40 $4.00 Only model supporting clip extension beyond a single generation Premium tier; 8x Veo 3.1 Lite; 1080p at no premium over 720p Sources: LTX API pricing documentation ; bfl.ai/pricing; Google AI for Developers model pricing; HappyHorse reseller rates via third-party API platforms; Artificial Analysis text-to-video arena leaderboards. All rates verified August 11, 2026, and subject to change. How fast and how good LTX says it is The most eye-catching number in LTX's launch materials is speed: the company says LTX-2.5 generates a 10-second, 720p image-to-video clip in 6.8 seconds faster than real time. The caveat is the hardware behind it. That figure was measured self-hosted on two of NVIDIA's top-end GB200 chips at steady state, a configuration far beyond what most teams have racked; the same job through LTX's own managed API took 23.7 seconds, albeit rendered at the higher 1080p resolution (the API has no 720p tier). By the company's end-to-end measurements of competing APIs on the same task, Google's Gemini Omni Flash came in at 52 seconds, xAI's Grok 1.5 at 63 seconds, Google's Veo 3.1 at 70 seconds (for an 8-second clip), MiniMax H3 at 180 seconds, ByteDance's Seedance 2.5 at 317 seconds, and Kuaishou's Kling 3.0 Pro at 398 seconds. On quality, LTX shared results from blind, side-by-side human preference tests, in which evaluators voted on videos generated from the same prompt without knowing which model produced which. LTX-2.5 recorded a 67% win rate, narrowly ahead of Seedance 2.5 at 65%, with Gemini Omni Flash at 55%, MiniMax H3 at 50%, Seedance 2.0 at 44%, Wan 2.6 at 42%, and FLUX 3 at 28%. All of these figures are vendor-reported measured or commissioned by LTX itself, not independently verified and the company labels the preference results preliminary, noting it expects them "to evolve as evaluation expands." They are directional claims a buyer should test against their own workloads rather than settled rankings. The independent benchmark that does exist cuts the other way for now: as of this month, Gemini Omni Flash — which LTX's commissioned tests place 12 points behind its own model — leads both of Artificial Analysis' text-to-video arena leaderboards , and the arena does not yet score LTX-2.5 at all. Until it does, the 67% figure remains untested on neutral ground. The launch materials also lean on deployment terms rather than raw performance: LTX-2.5 runs on any GPU with a minimum of 16GB of VRAM, deploys on-premises, at the edge, or via API, carries no visible watermark on output — though the license requires users to disclose that content is machine-generated and forbids removing any embedded provenance or "latent disclosure" features (more on this below) — and can be fine-tuned on a customer's own data and IP flexibility the company contrasts with closed API-only rivals and with open-licensed competitors whose weights are unavailable in the U.S. and Europe or whose licenses restrict fine-tuning. Betting against the API business model For Farbman, the release is another installment in a strategy that began as a reaction to the industry's consolidation around closed models. "We started with our own models out of necessity, because around the time that Sora came out, we realized that all the big guys are trying to close their models, and working through APIs just doesn't work for many businesses, including the kind of stuff that we wanted to build," he said. The technical argument, he explained, is that video and world models have a fundamentally wider "surface area" of use cases than language models. "With LLMs, the surface area of the API is pretty narrow, we're typically asking some kind of question, passing words and getting words back," Farbman said. "With video models, world models, there are so many different use cases that require people to get access to the weights and create flows that really work for them." He was blunt that the openness is not charity. "We're definitely not doing this as philanthropy," he said. "Our answer is open weights with licenses that allow individuals and companies below a certain amount of revenue to use the model for free, and once they're successful, to come up with some kind of licensing agreement with us." "We're trying to build a model that builders can confidently build upon," he added. "We're coming and saying: guys, open weights is not some kind of one-time philanthropic fluke for us. It's the strategy. We believe this is the right way to serve these models, and we're going to keep doing that." What the license actually says "Open weights" and "open source" part ways in the fine print. LTX-2.5 ships under the LTX-2.x Community License, a custom agreement that would not qualify as open source under the Open Source Initiative's definition: it discriminates by revenue and by field of use, both disqualifying restrictions. The headline mechanic works as advertised — organizations are free to use, modify, self-host, and even sublicense the model, with the $10 million annual revenue threshold (measured across all affiliates and subsidiaries, so a small subsidiary of a large parent doesn't slip under it) triggering the paid license. Notably, even companies above the line can download and evaluate the model free in non-production environments — the license effectively codifies the prototype-in-ComfyUI-then-license funnel Farbman describes. It also gives that funnel teeth: unauthorized commercial use obligates the violator to pay back-fees at LTX's standard rates, due within 30 days of written demand. The stickiest provisions concern what counts as a "derivative." The definition sweeps in not just fine-tuned checkpoints and LoRA adapters but distillations and any model trained on LTX-2.5's outputs or synthetic data — meaning a company that generates training clips with LTX-2.5 and uses them to train its own unrelated model has, by the license's terms, created a derivative locked to the same agreement. All derivatives must be redistributed under the same license, a fine-tune transferred to a $10 million-plus company triggers that company's own paid-license obligation regardless of who built it, and commercial users are barred outright from using the model to train or improve any competing AI system. A separate clause prohibits deploying LTX-2.5 in any product that competes with Lightricks' own offerings without a negotiated license. There are also control provisions unusual for a self-hosted model. Lightricks claims no rights in generated output, but the license requires users to disclose that content is machine-generated, forbids removing or circumventing any watermarking, content-provenance, or "latent disclosure" features embedded in the model, and reserves Lightricks' right to restrict usage "remotely or otherwise" and to push updates — with immediate license revocation as the penalty for disabling disclosure features. The license also declares Lightricks' intent that LTX-2.5 be treated as a "free and open-source general purpose AI model" under Article 53(2) of the EU AI Act, a derogation that lightens the company's own regulatory obligations — a classification legal observers may contest precisely because of the revenue threshold and use restrictions in this same document. And one restriction bears directly on the physical-AI pitch: military, warfare, and weapons-development uses are banned entirely, so the robotics checkpoint is off-limits to the defense sector without separate terms. From Facetune to world models and the node graph that became a standard LTX grew out of Lightricks , the Jerusalem-headquartered company best known for consumer creative apps including Facetune and Videoleap. Bootstrapped and profitable, Lightricks pivoted to foundation models in 2022, launched its LTX Studio filmmaking platform in early 2024, and released its first open-weights LTX Video model (LTXV) in November 2024 , following it with a 13-billion-parameter version in May 2025 . Farbman co-founded the company alongside CTO Yaron Inger and CMO Nir Pochter, and the LTX brand now fronts its world model business, with offices in New York, London, and Chicago. ComfyUI began in January 2023 as an open-source side project by a pseudonymous developer known as "comfyanonymous," who built a node-based graphical interface for Stable Diffusion that let users chain models and processing steps into repeatable visual workflows. It has since become one of the fastest-growing open-source projects in generative media the standard environment where new image and video models are tested, combined, and pushed into production and is now backed by a company, Comfy Org, which raised $17 million to keep developing the tool. Yan, a co-founder, serves as its CEO. Why ComfyUI is the front door for enterprise adoption For readers wondering why a model company and a tooling company are launching arm-in-arm, Farbman's answer was unusually candid: ComfyUI is where LTX's paying customers come from. "A whole lot of our customers are starting their journey with Comfy," he said. "It's already this prototyping system that's extremely popular in the industry, and a lot of the potential customers are coming to us after they already figured out the flow inside Comfy. It's already working, so for us it's a no-brainer that we have to provide zero-day support for the Comfy integration, because it's basically our customer acquisition channel." Yan described ComfyUI's role as the connective layer of the open ecosystem. "Comfy at the core is sitting as a layer on top, giving people accessibility to the open-weight models that people can inference on their local machine, or tap into closed models as well through our partner node system," he said. "In the end, [they] combine everything together into a workflow that empowers various things, from the creative side all the way to data pipeline and robotics type of scenarios." That flywheel, Yan argued, is what sustains open models commercially: "We help promote and push these models into the world... people do all sorts of workflow and model innovation on top of it, and that further propagates these models into studios or robotics labs. Those companies would end up acquiring licenses and then contribute a part of the value gained back to LTX and the rest of the ecosystem." What enterprises should know Both executives pushed back on the assumption that a video model is only for generating videos. Farbman rattled off a list of enterprise deployments that have little to do with cinematic clips. "We have hardware customers that are trying to figure out how to do computational photography with diffusion models, for example, taking a stream of raw pixels that are coming from the sensors, which is typically very noisy, and trying to figure out how to reduce noise there," he said. "Or think about the production studios that are trying to figure out how to do VFX, how to do water simulation, how to turn day into night. Or think about animation studios: they're trying to figure out how to streamline their pipeline, where animators are creating keyframes and then the system uses them as interpolation." For enterprises weighing where to start, the recommended path is the one their own employees have probably already taken. "A lot of enterprises have already adopted Comfy, and I think many others will follow," Farbman said. "It gives this right level of structure, where you can tweak things a lot, but it still abstracts a lot of things away... Enterprises are typically reaching out after people internally have already played with the model, played with Comfy." Yan described a consistent two-track pattern among studios and companies already running LTX and other open models in production. "They have their research, or R&D, creative pipeline, anything goes," he said. "Once in a while, some of these pipelines get good enough that they graduate into some kind of production environment. And somewhere along the line, the enterprise conversation gets started. On our end, it's more around tooling, and on the LTX side, it's more around the licensing." Because the weights are open, that entire experimentation phase can happen on a company's own hardware, with no per-generation billing and no data or IP leaving its systems, a meaningful distinction for enterprises with sensitive footage, proprietary characters, or regulated data. The commercial trigger only arrives with scale: organizations above $10 million in ARR need a license. Yan framed the stakes for slower-moving companies in starker terms. "This is a trend that is just fundamentally going to disrupt the entire creative industry," he said. "Studios are heavily trying to figure out what is the roadmap and how do we get ahead, sometimes not even get ahead, just how do we avoid falling behind the AI adoption wave." Developers, real-time apps, and the edge For software developers, the release leans into a growing real-time story. Alongside ComfyUI, LTX named two other launch partners: Asteria, the AI film studio producing original film and video on LTX, and Reactor, a developer platform that runs LTX-2.5 on low-latency inference infrastructure to power interactive avatars, live worlds, and real-time robotics workloads, so developers can build production-grade real-time experiences without standing up that infrastructure themselves. Yan pointed to a viral example of what open weights plus low latency makes possible: Flipbook, an interactive experience that spread on Reddit in which an entire clickable world is generated on the fly. "Everything people see on that interface is generated using an LTX model, live-streamed," he said. "It's an environment, or a world, where anywhere you click, it just generates a brand-new interaction... That type of experience and experimentation wouldn't exist without an open-weight model, without LTX's type of performance." Farbman said efficiency at the edge is a deliberate design target, not a side effect. "For us, it's very important to create an extremely efficient model that people can run on edge devices, both on consumer hardware and close to the edge with physical AI," he said, while acknowledging the relentless pace of the field: "These days, it's almost hard to take a vacation. Things are progressing so quickly that while you're releasing one model, you're already deeply into training another one, and new papers are coming on a daily basis." Filmmakers: virtual production now, easier slopes later For professional filmmakers and studios, Yan sees real-time world models changing the shape of production itself, collapsing the gap between shooting and post. "These days you see real-time models, or world models, getting adopted in studios as part of what's called virtual production, meaning you can shoot and then immediately get close to what the post-production result looks like," he said. "You give a much better experience to the producer or director to say, 'okay, this is what I want,' or 'this is not what I want let me actually reiterate.' Whereas before, the entire Hollywood pipeline is, in my opinion, a giant mess where it has to constantly go between multiple departments." He also cautioned against reading head-to-head model comparisons too literally, given how differently models specialize across animation, photorealism, gaming, 3D, and robotics. "Various models have simply different characteristics," he said. "It's like comparing Michael Phelps with, I don't know, Michael Jordan. It's not really a comparison of who's a better athlete, there are just different specialties here." As for amateur and indie creators intimidated by ComfyUI's famously steep learning curve, Yan was direct that the tool will meet them partway, but only partway. "It's kind of like skiing," he said. "There are easy slopes that you can go down using Comfy, and hopefully we can create more and more of these easy slopes overall. But we'll never sacrifice the existence of the double-black-diamond type of lanes, because the real technical, professional creatives actually need and couldn't live without that type of core power. That's actually our core differentiator compared to a mobile-app type of creative tool." LTX-2.5 is available today on Hugging Face , natively in ComfyUI , and through the LTX API . Updated several hours after publication with additional details from LTX's public blog post and API pricing page.