This review makes one choice up front: we do not grade image quality. World models look their best in curated demo videos, which is exactly the part we trust least. Cost beats spectacle: a model earns follow-up for its real license, hardware, and data cost, not a staged minute. We state scope first, then show the table.
Evaluation Scope: Four Accounts, No Pretty Pictures
We fix the boundary to avoid overlapping existing comparisons. The site already published three with clear division of labor: image capability in reasoning-image-models-comparison-review, agent long-context cost in agent-long-context-cost-review, voice cloud-versus-local in cloud-vs-local-voice-ai-review. This piece does one job: take open-source world model apart and check what is truly open, what is commercially blocked, and what hardware you cannot afford. For the LingBot-World 2.0 hotspot see the LingBot-World 2.0 hotspot piece; for gods-eye-view resources see the gods-eye-view resources piece; for a local deploy SOP see the local deploy SOP piece.
We score four fixed accounts, in decision order, never reordered:
Account one, openness. Separate three things: are weights public, is code public, is deployment code public. Many projects dump weights on a hub and call themselves open, yet ship no inference script, no serving stack, no training code. That is downloadable closed source. A high star count does not mean you can reproduce.
Account two, the commercial boundary of the license. Licenses split three tiers: MIT and Apache-2.0 are commercially friendly, CC BY-NC-SA 4.0 explicitly bans commercial use, and between them sits fine print on attribution and reciprocal derivatives. The first step in selection is the license, not the star count.
Account three, hardware threshold. Every hardware number is tagged official or engineering estimate. What the README claims and what the repo script sets often differ, and press is usually more aggressive. When they conflict we show both and defer to the official repository.
Account four, data and control rights. Whose service runs the model, whose database holds your data, can you export, can you leave without lock-in: these are long-term costs. Closed subscriptions lose this account by design.
Every number comes from a GitHub API snapshot on 2026-09-14. Anything tagged reported is press or official-blog caliber, not our measurement. Prices we could not verify read subject to official, and we never invent a price.
The Four Accounts, Line by Line
Account one: openness (weights, code, deployment, kept separate)
LingBot-World 2.0 (repo Robbyant/lingbot-world-v2) publishes weights and some code for its 14B and 1.3B sizes, but deployment code is officially not open. You get the model and training code, not the production serving stack, so reproduction and redeployment cost you. Genie 3 is the opposite: fully closed, reachable only via a Gemini Ultra subscription, no weights. gods-eye-view (bilawalsidhu/gods-eye-view) is fully open code running locally in the browser, the highest openness here. lingbot-map and lingbot-world v1 are open-code Apache-2.0 projects with deployment code available. Split open into three layers and the seven projects fall into three openness levels; the label all open collapses.
Account two: the commercial boundary of the license (the core argument)
The point we most want clear: open source is not commercially usable. LingBot-World 2.0 ships CC BY-NC-SA 4.0. NC means NonCommercial, an explicit exclusion of any commercial purpose. SA means ShareAlike, forcing derivatives to the same license. Building a product, a paid service, or an internal commercial system on it crosses the line. The trap is that SA is contagious: outward improvements must also open under the same license, forcing private value public. gods-eye-view uses MIT: commercial use, closed redistribution, anything goes with the copyright notice kept. lingbot-map and lingbot-world v1 use Apache-2.0, commercially friendly but requiring attribution, a note on changes, and a default patent grant. Lay the tiers side by side and the first cut falls on the license, not stars.
Account three: hardware threshold (README, repo script, press often disagree)
The case is LingBot-World 2.0. The README example runs 1.3B on four cards; the same repo's run_fast.sh references two cards for 1.3B and eight for 14B. Both official, yet a factor of two apart. Press then writes runs on a consumer GPU flatly. For budgeting we recommend the repo script, the command you can reproduce; the README example is the optimistic version. Genie 3's 720p at 24fps is reported caliber; cost sits on Google's servers, so the user side has zero threshold but zero control. gods-eye-view runs locally on Node 24.x or 26.x, the lowest bar. lingbot-map does streaming 3D reconstruction, engineering estimate mid-to-high GPU; exact compute subject to official.
Account four: data and control rights
Genie 3 locks data and execution inside the Google subscription; ownership and terms of your output are set by the other party, the lowest control. LingBot-World 2.0 keeps deployment code closed, so private deployment means building your own stack; data sovereignty is yours but engineering cost is high. gods-eye-view aggregates public data (flights, ships, satellites, earthquakes, traffic, public cameras), binds to localhost, keeps data local, the best control and privacy. Open projects like lingbot-map and lingbot-world v1 keep data and control in your hands if you can deploy them. For compliance-bound enterprises this account often decides selection more than quality.
Side-by-Side Comparison
The table lists seven projects; OpenWorldLib and LightX2V are supplementary, all figures as of the 2026-09-14 snapshot. Any hardware or cost figure the official side did not price reads subject to official. Read the Type / Layer column first; it decides what the table compares.
| Project | Type / Layer | Openness | License and Commercial Boundary | Hardware Threshold (official) | Deployment Code Open | Snapshot |
|---|---|---|---|---|---|---|
| Robbyant/lingbot-world-v2 (LingBot-World 2.0) | Generative world model | Weights + code public | CC BY-NC-SA 4.0, no commercial, derivatives must share | 1.3B ref 2 cards / 14B ref 8 cards (run_fast.sh) | No | 2026-09-14 |
| Genie 3 (Google DeepMind) | Generative world model | Closed | Closed subscription, commercial terms set by Google | 720p at 24fps (reported), zero user-side threshold | No | 2026-09-14 |
| bilawalsidhu/gods-eye-view | Front-end aggregation view | Code fully open | MIT, commercially friendly | Local browser, Node 24.x or 26.x | Yes | 2026-09-14 |
| Robbyant/lingbot-map | Reconstruction 3D / spatial | Code open | Apache-2.0, commercially friendly (attribution) | Streaming 3D reconstruction, est. mid-high GPU | Yes | 2026-09-14 |
| Robbyant/lingbot-world (v1) | Generative world model (v1) | Code open | Apache-2.0, commercially friendly (attribution) | Est. mid-high GPU | Yes | 2026-09-14 |
| OpenDCAI/OpenWorldLib (supp.) | World model toolkit | Code open | Apache-2.0 | Unconfirmed | Yes | 2026-09-14 |
| ModelTC/LightX2V (supp.) | Video generation accelerator | Code open | Apache-2.0 | Unconfirmed | Yes | 2026-09-14 |
Read the table by Type / Layer first. Generative (LingBot-World 2.0, Genie 3, lingbot-world v1), reconstruction (lingbot-map), and front-end aggregation (gods-eye-view) are not the same problem domain. One table answers which link you need, not which is stronger. A digital-twin factory team and a real-time game team get different answers; one ruler yields the wrong conclusion.
Selection Advice by Reader Type
We give conclusions per reader type, not the line they are all good. Order: license, deployment openness, hardware, stars.
Individual researchers. Goal is reproducing a paper, running a demo, your own experiments. Prioritize openness and hardware. gods-eye-view under MIT and local browser is cheapest to play with and teach. For generative models, lingbot-world v1 under Apache-2.0 fits research derivatives better than v2 under CC BY-NC-SA 4.0, because v1 does not block the commercial off-ramp. The supplementary LightX2V suits video-generation acceleration. Caution: research is not commercial, but if you later ship a product, starting from v1 saves the rewrite that NC-locked v2 would force.
Small teams building a product. This is where the license red line hurts most. A paid or external service means LingBot-World 2.0 under CC BY-NC-SA 4.0 is out. gods-eye-view under MIT can be wrapped and closed safely. lingbot-map under Apache-2.0 is commercial, but keep attribution, note changes, read the patent clause. On hardware, gods-eye-view has near-zero marginal cost; lingbot-map's threshold needs a confirmed GPU budget. Worst habit: ship first, fix compliance later; an NC license used commercially is infringement, not a late notice away.
Commercial integration (enterprise or vendor). Control matters most. Closed Genie 3 fits a validate-demand prototype, but long-term integration must price locked data and mutable terms; a price rise can break delivery. What you can self-host and audit are the Apache-2.0 or MIT open projects. Advice: build the skeleton on open projects, write the boundary into procurement and legal review, then add a closed subscription if needed. For regulated industries, open with deployment code is a hard gate; Genie 3 is off the list by root.
Cold Take
First, stars are not usability. gods-eye-view's thirty-thousand-plus stars are partly traffic from a cool front end over public data, solving presentation not generation. Comparing it with generative models on who is the world model compares the wrong axis. Stars lag attention, not lead usability.
Second, the zero threshold of closed subscriptions is sugar coating. Genie 3 costs nothing up front, but the price is zero control, subscription billing, mutable terms. For product teams this is a time bomb: a demo that runs today may not run tomorrow. Betting core capability on a subscription hands over your lifeline.
Third, officially open and deployable open are separated by a river. LingBot-World 2.0 opens weights and code but not deployment code, shifting reproduction cost to you. When judging openness, always ask whether deployment code is open; that question decides if you ship in three months.
Fourth, hardware-caliber inflation is easiest to miss. When README, repo script, and press disagree, press is most optimistic, README next, repo script most conservative. Budget against the reproducible command, defer to official when in doubt.
Fifth, hype is not maturity. Generation, reconstruction, aggregation sit at different maturity: generative is flashiest but tightest on license and heaviest on engineering; reconstruction is practical but far from world; aggregation is lightest but not generation. Stirring them into one concept is the biggest water in this hype.
One line to close: the takeaway is layering. Generation, reconstruction, aggregation are three jobs; pick the wrong layer and no model saves your requirement.
FAQ
Q1: How do open-source licenses actually split the commercial boundary?
A1: Three tiers. MIT is widest: commercial, closed, redistribute, just keep the copyright notice. Apache-2.0 is commercially friendly but requires attribution, a note on changes, and carries patent grant terms. CC BY-NC-SA 4.0 bans commercial use via NC and forces reciprocal derivatives via SA, which eliminates product use. Step one is the license, not stars.
Q2: Can I use LingBot-World 2.0 commercially?
A2: No, not directly. It ships CC BY-NC-SA 4.0; NonCommercial excludes commercial purpose and ShareAlike forces the derivative to the same license. Paid services, external products, internal commercial systems all cross the line. For commercial generative models, move to lingbot-world v1 under Apache-2.0 or gods-eye-view under MIT.
Q3: Which hardware number should I trust?
A3: Trust the reproducible repo script; it is usually more conservative than the README example and press. LingBot-World 2.0 is the case: README runs 1.3B on four cards, run_fast.sh references two for 1.3B and eight for 14B. Budget against the script; when conflicted, defer to the official repository, treat press as reference only.
Q4: Does gods-eye-view count as a world model?
A4: No, it is a front-end aggregation spatial view, not generative. It aggregates public data (flights, ships, satellites, earthquakes, traffic, public cameras) locally in the browser, solving see the real world in real time, not generate a virtual world. Against generative models it answers which link you need, not which is stronger.
Q5: What is the first step in selection?
A5: First check whether the license covers your use, then whether deployment code is open, and only last look at stars and demos. Stars are traffic; the license is the line between life and death. Commercial projects must exclude CC licenses first, then talk models and hardware.