Molmo2-4B and LFM2.5-VL-3B are small vision-language models built for different jobs: one handles video and multi-image tasks, while the other is designed for images on relatively modest hardware. That makes model fit, not a league-table score, the practical choice for developers.
Watch Desk analysis
What happened
An OrcaRouter comparison sets out the trade-offs between the two models. It says LFM2.5-VL-3B’s weights occupy 6.27 GB, with the vendor claiming an inference footprint below 3.3 GB; it also lists GGUF, ONNX and MLX exports. Molmo2-4B’s distributed float32 weights total 19.42 GB, and the comparison says AI2 publishes no equivalent runtime-memory figure.
The capabilities differ too. The comparison describes LFM2.5-VL-3B as an image-and-text model suited to tasks such as OCR, screen reading and object detection. Molmo2-4B supports video and multi-image tasks, with answers that can include image coordinates and video timestamps. OrcaRouter also notes that Molmo2 became supported in upstream Transformers in October, so its separate remote-code setup is no longer the only route.
Why it matters
A smaller model that runs on a phone or laptop may be the useful choice for documents, screenshots or single photographs. If the task is to find an event in a video or locate an object in a frame, Molmo2 has capabilities LFM2.5-VL-3B does not offer. The extra footprint is a real deployment cost, not a footnote.
The headline numbers do not settle which model is better. OrcaRouter compares vendor-reported results from different benchmark suites, with different prompts and settings, and says neither vendor has published results on the other’s suite. Those averages are not a fair head-to-head test. The article also flags distinct licence conditions for each model, so teams should check the terms against their intended use rather than treating a permissive-sounding label as the whole answer.
Our read
This is a useful deployment comparison, not a winner’s podium. Start with the inputs your application actually needs, then test the likely model on your own hardware and workload. If video matters, LFM2.5-VL-3B is out of the running; if a compact image model is the goal, Molmo2’s larger weights need a compelling reason.
What to watch
- Whether AI2 publishes practical memory and speed figures for Molmo2-4B.
- Whether either team publishes results on a shared benchmark harness.
- How the models’ licence terms affect commercial deployment.
Discussion spark: For a real deployment, would you prioritise a smaller model that is easier to run, or broader capabilities that demand more hardware?
Sources and evidence
- Molmo2-4B vs LFM2.5-VL-3B: 19 GB vs a 3 GB Phone Model – OrcaRouter (5 November 2026, 19:49 UTC)
Watch Desk is operated by WittyWires as an independent cross-cutting AI news tracker. It does not speak for the organisations or people it covers.