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A Smaller Vision Model That Wants to See, Understand, and Act
Artificial intelligence is moving beyond the cloud. The next major battleground is increasingly becoming the device in your hand, the laptop on your desk, the industrial machine on the factory floor, or the embedded computer quietly operating somewhere in the background.
That is the opportunity Liquid AI is targeting with LFM2.5-VL-3B, a compact vision-language model designed to combine strong visual understanding with the speed and efficiency required for edge deployment.
Liquid AI describes the model as a major step beyond its previous vision-language releases, with improvements in screen and user-interface understanding, object grounding, multi-image reasoning, and function calling. The company is positioning the 3B-class model not simply as a chatbot that can look at pictures, but as a practical multimodal system capable of interpreting documents, understanding digital interfaces, locating objects, processing multiple images, and interacting with tools.
The bigger story, however, is not simply another benchmark result.
It is the idea that useful vision AI does not necessarily have to live inside a massive data center.
The Edge AI Race Is Getting More Serious
For years, the most capable AI models have generally required powerful cloud infrastructure. That approach provides enormous computational resources, but it also introduces latency, bandwidth requirements, infrastructure costs, and privacy concerns.
Edge AI approaches the problem differently.
Instead of sending every image, screenshot, document, or camera frame to a remote server, a compact model can process information locally. That can make applications faster and potentially more private while reducing dependence on an internet connection.
This is precisely where models such as LFM2.5-VL-3B become interesting.
Liquid AI is attempting to squeeze increasingly sophisticated multimodal capabilities into a relatively small model footprint. The company’s previous LFM2-VL-3B model was already designed for lightweight deployment, and the newer LFM2.5-VL generation pushes further toward practical vision-language applications.
The broader LFM2-VL family is explicitly designed for on-device deployment, while its existing 3B checkpoint is distributed through Hugging Face and supports local inference frameworks.
Four Major Improvements Define LFM2.5-VL-3B
Liquid AI highlights four areas where LFM2.5-VL-3B is intended to make a significant difference.
The first is screen and UI understanding.
The second is grounding and object detection through natural-language queries.
The third is reasoning across multiple images.
The fourth is function calling, both when working with text alone and when combining text with visual information.
Together, these capabilities point toward a more useful type of AI assistant.
Instead of merely answering, “What is in this picture?”, a model could potentially interpret what is happening on a screen, identify the location of a particular object, compare several images, and then invoke an external function based on what it has observed.
That transition—from seeing to acting—is one of the most important developments in modern multimodal AI.
Screen Understanding Could Be One of Its Most Important Features
Computer screens are deceptively difficult for AI systems.
A screenshot may contain buttons, menus, icons, text, windows, notifications, tables, charts, advertisements, overlapping elements, and dynamically changing content.
Recognizing the individual components is only part of the problem.
An AI system also needs to understand what those components mean and where they are located.
LFM2.5-VL-3B is specifically designed to improve this kind of interaction. According to the supplied benchmark results, it achieves 78.7 on ScreenSpot-v2 Desktop, 81.2 on Mobile, and 82.2 on Web.
That is a substantial improvement over the previous LFM2-VL-3B results listed in the same evaluation, which recorded 6.0, 7.6, and 2.5, respectively.
Those numbers suggest that Liquid AI has made screen understanding one of the model’s major priorities rather than treating it as a secondary capability.
Grounding Makes Visual Answers More Practical
Vision models can describe objects surprisingly well, but description alone is not enough for many real-world applications.
Suppose a user asks an AI system to identify a particular button, locate a component on a machine, or find an object inside a photograph.
The model needs to connect language to a physical or visual location.
That is the essence of grounding.
LFM2.5-VL-3B scores 87.9 on RefCOCO-avg in the supplied evaluation, compared with 57.1 for the earlier LFM2-VL-3B.
This improvement is particularly important because grounding creates a bridge between perception and action.
A model that can identify “the blue button in the upper-right corner” is far more useful to an agent than a model that simply says “there is a button on the screen.”
Multiple Images Change the Nature of Visual Reasoning
Another important upgrade is multi-image reasoning.
Looking at a single image is one problem.
Comparing several images is another.
A system might need to determine what changed between two screenshots, compare multiple product photographs, examine several pages of a document, track an object across frames, or analyze different views of the same physical environment.
The supplied results show LFM2.5-VL-3B reaching 61.5 on BLINK and 58.3 on MuirBench, significantly above the previous LFM2-VL-3B scores of 50.2 and 34.9.
That does not mean the model wins every comparison. It does mean Liquid AI has substantially improved one of the areas where multimodal models traditionally become more difficult to control.
Function Calling Is Where Vision AI Starts Becoming an Agent
Perhaps the most strategically important improvement is function calling.
A model can recognize a screenshot.
A model can read a document.
A model can identify an object.
But an agent needs to do something with that information.
Function calling provides the mechanism.
In practical terms, a vision-enabled model could potentially inspect an image, determine what information is relevant, and then invoke a software function to perform the next step.
That could mean searching a database, controlling an application, extracting information, triggering an API, or interacting with another software component.
Liquid AI reports a 59.5 score on ToolSandbox, compared with 26.4 for its previous 3B model, while BFCL V4 rises from 20.5 to 32.5.
The results do not establish that LFM2.5-VL-3B is the strongest tool-calling model overall. They do demonstrate a major improvement within Liquid AI’s own model line.
The Architecture Combines Language and Vision
Under the hood, LFM2.5-VL-3B combines a SigLIP2 400M NaFlex vision encoder with the pretrained backbone used by Liquid AI’s LFM2.5-2.6B text model.
The model was reportedly pretrained on approximately 34 trillion tokens, with four times more vision data than earlier training.
The training mixture includes image-caption data, OCR material, grounding datasets, and instruction-following examples.
This combination is important because multimodal performance depends on more than simply attaching an image encoder to a language model.
The model needs to learn how visual information connects with language, instructions, objects, documents, interfaces, and actions.
A Larger Vocabulary Targets Multilingual Use
Liquid AI also says it doubled the vocabulary to 128,000 tokens by extending the tokenizer rather than retraining it from scratch.
The stated goal is better support for non-Latin scripts.
That is an important consideration for edge AI because many real-world deployments operate in multilingual environments.
Documents, menus, applications, signs, receipts, and business records are not limited to English.
A compact model that can process diverse writing systems becomes considerably more useful for international deployment.
Training Did Not Stop at Supervised Fine-Tuning
Liquid AI describes a two-stage post-training process.
The first stage uses supervised fine-tuning, including knowledge distillation from a larger teacher model and what the company calls Antidoom training.
The second stage applies multi-reward reinforcement learning.
This reflects a broader trend in AI development.
The raw pretrained model is increasingly only the starting point. Post-training determines how reliably a model follows instructions, uses tools, interprets complex visual inputs, and behaves under practical workloads.
The goal is no longer simply to produce a model that knows a lot.
The goal is to produce one that can actually do useful work.
Benchmark Results Show a Strong but Uneven Model
Liquid
LFM2.5-VL-3B achieves an overall average of 69.4 across the listed vision benchmarks.
That puts it ahead of several smaller competitors in the supplied comparison and roughly level with the larger InternVL 3.5 4B result of 69.4.
However, it does not dominate every benchmark.
Qwen3.5-4B records a higher overall average of 70.1, while some specialized benchmarks also favor competing models.
This distinction matters.
The most meaningful conclusion is not that LFM2.5-VL-3B wins everything.
The stronger argument is that Liquid AI has created a model with a particularly useful balance of visual capabilities, tool use, and deployment efficiency.
Real-World Image Understanding Is a Key Strength
The model records 73.1 on RealWorldQA, compared with 71.1 for the previous LFM2-VL-3B.
It reaches 77.7 on SEED-Bench image, compared with 76.6 previously.
It also records 81.0 on MMBench dev English v1.1.
These improvements are relatively modest compared with the dramatic gains seen in some other categories.
That is actually revealing.
Liquid AI appears to have concentrated its development efforts not only on conventional visual question answering, but also on the more operational capabilities surrounding vision.
Document Intelligence Remains Central
Documents are one of the clearest commercial applications for vision-language AI.
Companies have enormous amounts of information trapped inside PDFs, scanned forms, invoices, charts, receipts, presentations, and screenshots.
LFM2.5-VL-3B records 91.1 on DocVQA, 84.3 on TextVQA, 84.2 on OCRBench v1, and 81.3 on ChartQA in the supplied results.
These scores indicate that the model is not simply optimized for recognizing photographs.
It is designed to interact with the kind of structured visual information that appears in everyday work.
The Model Is Not Perfect at Complex Reasoning
The benchmark table also provides an important warning.
LFM2.5-VL-3B scores 30.5 on MMMU-Pro and 48.4 on MMMU, while some larger models achieve considerably higher scores.
This suggests that compactness still comes with trade-offs.
A 3B-class model can be extremely efficient, but it should not automatically be considered equivalent to much larger reasoning systems.
That distinction becomes especially important for scientific, mathematical, and highly complex analytical workloads.
Mathematical Reasoning Shows Progress but Leaves Room to Grow
On MathVista mini, LFM2.5-VL-3B scores 68.5, up from 62.1 for the previous LFM2-VL-3B.
LogicVista reaches 37.4, compared with 32.2 previously.
These improvements are meaningful, but they also show that visual reasoning remains a difficult frontier.
Understanding a chart is different from solving a complex mathematical problem embedded inside that chart.
For edge AI, the question may not be whether a small model can match the largest reasoning models.
The more useful question is whether it can solve enough of the common problems locally to justify its lower latency and resource requirements.
Hallucination Remains a Problem
No serious analysis of a vision-language model should ignore hallucinations.
LFM2.5-VL-3B records 47.2 on HallusionBench and 88.7 on POPE in the supplied evaluation.
The scores show progress, but they do not eliminate the fundamental problem.
A vision model can still misunderstand an image, infer something that is not present, or confidently interpret ambiguous information incorrectly.
That matters even more when models are connected to tools.
A hallucination in a casual conversation is inconvenient.
A hallucination that triggers an automated action can become a real operational problem.
Instruction Following Is Improving
The text-only evaluation shows an IFEval score of 82.3, compared with 72.9 for LFM2-VL-3B.
Multi-IF rises from 46.5 to 59.4.
IFBench increases from 20.8 to 25.8.
These improvements suggest that the new model is not merely a better image reader.
It is also becoming a more capable instruction-following model.
That matters because multimodal applications frequently involve long chains of instructions.
Users might ask a model to inspect an image, extract specific fields, format the answer in a particular structure, and then invoke a tool.
The ability to follow those constraints reliably becomes critical.
The Edge Advantage Is About Latency
One of the most impressive claims in the release concerns inference speed.
Liquid AI reports that LFM2.5-VL-3B can decode around 228 tokens per second on an M5 Max and approximately 116 tokens per second on a Ryzen AI Max+ 395.
The company also reports roughly 20 tokens per second on a Galaxy S26 Ultra.
If those figures hold under representative workloads, they demonstrate why small multimodal models are becoming increasingly attractive.
The difference between waiting for a remote server and receiving a response locally can fundamentally change how an application feels.
Three Gigabytes Can Be a Big Deal
Liquid AI says LFM2.5-VL-3B can fit in approximately 3 GB of memory for on-device inference under its stated configuration.
That is an important number.
Memory requirements can determine whether an AI model runs comfortably on a laptop or embedded device—or whether it requires a powerful GPU server.
Smaller models also make it easier for developers to deploy AI in environments where cloud connectivity is unreliable, expensive, or undesirable.
GPU Throughput Could Make the Model Commercially Interesting
Liquid AI also reports approximately 11,000 tokens per second at high concurrency.
The company says this is roughly twice the throughput of the larger 4B-class models it tested and ahead of some smaller 2B-class alternatives.
At that scale, inference efficiency becomes more than a technical curiosity.
It can directly influence the cost of running AI services.
If a smaller model can handle a large number of requests while maintaining acceptable quality, organizations may be able to deploy specialized multimodal systems without paying the infrastructure cost associated with much larger models.
Software Ecosystem Support Matters as Much as the Model
A powerful model is only useful if developers can actually deploy it.
LFM2.5-VL-3B is presented as supporting major inference ecosystems including llama.cpp, MLX, vLLM, SGLang, and ONNX.
The existing LFM2-VL-3B ecosystem already demonstrates this deployment-oriented strategy, with Hugging Face documentation showing support for Transformers, vLLM, SGLang, Docker-based workflows, and other deployment paths.
This broad compatibility could become one of Liquid AI’s strongest competitive advantages.
Hugging Face Makes Experimentation Easier
The model is intended to be available through Hugging Face, giving developers a familiar distribution channel for experimentation, fine-tuning, evaluation, and deployment.
The existing Liquid AI model ecosystem already contains multiple LFM2 and LFM2.5 variants, including quantized and MLX-oriented checkpoints.
That ecosystem matters because developers increasingly choose models based not only on benchmark performance, but also on how easy they are to integrate.
The Developer Workflow Is Straightforward
The supplied release instructions use Python, PyTorch, Transformers, and the model’s processor.
The basic workflow is simple.
Install the necessary libraries.
Load the model and processor.
Provide an image and a text instruction.
Generate the response.
That simplicity is exactly what smaller open models need if they want to compete with hosted AI APIs.
Multi-Image Applications Could Become a Major Use Case
The multi-image capability opens another category of applications.
A developer could potentially feed the model several screenshots and ask it to identify differences.
A field technician could provide several photographs of equipment.
A shopping assistant could compare product images.
A document-processing system could analyze pages from the same document together.
A security system could compare sequential camera frames.
The important shift is that the model is no longer treating each image as an isolated event.
It is beginning to reason across visual context.
AI Assistants Could Become More Visually Aware
Screen understanding and function calling together create an especially interesting possibility.
Imagine an AI assistant that can see the same interface a human sees.
The assistant could identify a button, understand what it does, and call the appropriate software function.
That is much closer to computer-use AI than traditional image captioning.
The edge deployment angle makes this even more interesting because such systems could potentially operate locally rather than constantly transmitting screenshots to remote servers.
Privacy Could Become a Major Advantage
Local vision processing has an obvious privacy benefit.
Images can contain faces, documents, addresses, financial information, business records, internal dashboards, or confidential communications.
Sending those images to a remote API may create compliance and data-governance concerns.
A sufficiently capable local model provides another option.
Organizations could process certain information locally and only send limited outputs to external systems.
That does not automatically make local AI private or secure, but it can significantly reduce the amount of sensitive visual data that needs to leave the device.
Offline AI Is Becoming More Practical
Another major advantage is resilience.
A local model does not necessarily need a constant internet connection.
That could matter in factories, warehouses, vehicles, remote locations, field operations, and environments with unreliable connectivity.
The AI system can continue operating even when cloud access is limited.
This is one reason the edge AI movement is bigger than simply trying to save money.
It is also about creating systems that remain functional when the network disappears.
The Real Competition Is Not Just Parameter Count
The AI industry has spent years treating model size as a rough indicator of capability.
But edge models challenge that assumption.
A 3B model that responds quickly on a phone can be more useful for a particular application than a 100B model that requires a remote data center.
The correct model is the one that satisfies the application’s quality, latency, cost, privacy, and reliability requirements.
LFM2.5-VL-3B is therefore best understood as part of a larger shift from maximum intelligence at any cost toward sufficient intelligence at practical scale.
What Undercode Say:
1. The Bigger Story Is Efficiency
LFM2.5-VL-3B is interesting because Liquid AI is attacking one of AI’s most persistent problems: capability versus compute.
2. Small Does Not Automatically Mean Weak
A 3B-class multimodal model can now perform tasks that previously required substantially larger systems.
- Benchmarks Tell Only Part of the Story
The supplied scores show clear strengths, but they also reveal areas where larger models remain ahead.
4. Screen Understanding Stands Out
The huge improvement in ScreenSpot-v2 results is arguably one of the most important parts of the release.
5. Grounding Is Equally Important
The RefCOCO improvement indicates a substantial leap in connecting language with visual locations.
6. Tool Use Changes the Equation
Function calling turns the model from a passive observer into something that can participate in workflows.
- Vision Plus Tools Is the Bigger Opportunity
A model that sees and acts can become considerably more useful than one that only describes.
8. Multi-Image Reasoning Matters
Real-world tasks frequently involve multiple images rather than a single photograph.
9. Documents Are a Natural Target
OCR, charts, forms, and screenshots represent enormous pools of business information.
10. Local Processing Can Reduce Latency
Removing the round trip to a cloud server can make interactive AI substantially faster.
11. Local Processing Can Also Reduce Bandwidth
Large image workloads can generate significant network traffic.
12. Privacy Could Drive Adoption
Sensitive visual information does not always need to leave the device when capable local inference is available.
13. The 3 GB Footprint Is Significant
Memory efficiency can determine whether a model can realistically run on consumer hardware.
14. Smartphone AI Is Moving Forward
The reported Galaxy S26 Ultra performance shows the direction edge AI is taking.
15. Laptop AI Is Another Natural Target
Apple Silicon and
- The Model Is Not a Universal Winner
Several competitors score higher on individual benchmarks.
17. Qwen Still Has Strong Competition
The supplied table shows Qwen3.5-4B outperforming LFM2.5-VL-3B on the overall average.
18. InternVL Remains Competitive
InternVL 3.5 4B also demonstrates strong performance in several visual categories.
19. Bigger Models Still Have Advantages
Complex reasoning remains one of the areas where additional capacity can matter.
20. MMMU-Pro Highlights the Limitation
A 30.5 score shows that compact multimodal systems still have room to improve on demanding reasoning tasks.
21. Hallucination Has Not Disappeared
A stronger visual model can still make incorrect assumptions.
22. Tool Calling Raises the Stakes
When hallucinations are connected to actions, mistakes become more consequential.
23. Developers Need Guardrails
Local deployment should not be confused with automatic reliability.
24. Human Oversight Remains Important
High-impact decisions should not be delegated simply because a model is fast.
25. Fine-Tuning Could Be the Secret Weapon
A compact general model can become much more effective when adapted to a specific domain.
26. Specialized Edge AI Makes Sense
Factories, retail, logistics, healthcare administration, and field services all have specialized visual workloads.
27. Open Deployment Creates Flexibility
Developers can choose hardware and inference infrastructure instead of being locked into one hosted API.
28. Ecosystem Support Is Critical
Compatibility with Transformers, vLLM, SGLang, MLX, llama.cpp, and ONNX increases practical value.
29. Quantization Could Push Deployment Further
Smaller numerical representations can make edge deployment possible on even more devices.
- The AI Industry Is Moving Toward Heterogeneous Inference
Some workloads will remain in the cloud while others move onto phones, PCs, vehicles, and embedded systems.
31. Hybrid AI Will Probably Win
The strongest architecture may combine local models with cloud models rather than choosing only one.
32. Local Models Can Handle Routine Work
Simple visual tasks can potentially be completed without consuming expensive cloud inference resources.
33. Cloud Models Can Handle Difficult Problems
When reasoning becomes complicated, applications can escalate the request to a larger system.
- This Creates a New AI Routing Layer
Future applications may automatically decide which model should handle each task.
35. Vision Agents Are Becoming More Realistic
Grounding, screen understanding, multi-image reasoning, and function calling are foundational pieces for visual agents.
- The Browser Could Become an AI Runtime
WebGPU demonstrations show how increasingly capable models can be experienced without traditional server-side infrastructure.
37. Edge AI Could Become Invisible
The most successful local AI may not feel like a separate application at all.
- It Could Simply Become a Device Capability
Cameras, operating systems, browsers, productivity tools, and industrial software can quietly integrate local vision intelligence.
- Liquid AI Is Betting on Practical Intelligence
The
40. The Long-Term Message Is Clear
LFM2.5-VL-3B suggests that the future of multimodal AI will not belong exclusively to giant cloud models. Increasingly, useful intelligence will be distributed across the edge, where speed, privacy, cost, and autonomy matter just as much as raw benchmark scores.
Deep Analysis: The Commands Behind the Model
Command One: See
The first capability is perception.
The model must transform pixels into meaningful representations of objects, text, interfaces, charts, and scenes.
Command Two: Understand
Seeing an object is not the same as understanding its role.
The language model must connect visual evidence with the user’s instruction and the surrounding context.
Command Three: Ground
Grounding gives the model a way to connect language with a specific location in an image or interface.
Command Four: Compare
Multi-image reasoning allows the system to compare separate visual inputs and derive relationships between them.
Command Five: Read
OCR and document understanding allow the model to extract information from the visual world rather than treating images as purely photographic content.
Command Six: Reason
The model can combine visual observations with language instructions to answer questions and solve tasks.
Command Seven: Decide
Once the model understands the request, it can determine what information or operation is needed next.
Command Eight: Call
Function calling provides the bridge between the
Command Nine: Act
The ultimate goal is not merely producing text.
It is completing a useful task.
Command Ten: Repeat
An agentic system can potentially observe a result, process the new information, and continue the workflow.
Why the Edge Strategy Matters Now
Cloud AI Has a Cost
Large-scale inference consumes expensive compute, memory, networking, and energy.
Latency Has a Cost
Every request sent to a remote server introduces communication overhead.
Privacy Has a Cost
Sensitive images can create governance and compliance challenges when processed remotely.
Reliability Has a Cost
An application that depends entirely on cloud connectivity can fail when the network does.
Edge AI Addresses All Four
A capable local model can reduce dependence on remote infrastructure for selected workloads.
But Edge AI Has Its Own Trade-Offs
Local devices have limited memory, compute, thermal capacity, and battery life.
Efficiency Is Therefore the Core Battle
The winning edge model will not necessarily be the most intelligent model in absolute terms.
It will be the model that delivers the best combination of capability and resource consumption.
✅ The
The
✅ The Edge-Deployment Positioning Is Supported
Liquid
⚠️ Benchmark and Speed Numbers Should Be Treated as Vendor-Reported Results
The detailed benchmark scores and inference-speed figures in the source are presented as Liquid AI’s evaluation results. They should not be interpreted as universally reproducible performance across every device, workload, quantization level, or inference configuration.
Prediction
(+1) Edge Vision AI Will Become a Major AI Category
The combination of better mobile hardware and increasingly capable compact models strongly suggests that multimodal AI will continue moving away from exclusive dependence on centralized cloud infrastructure.
(+1) Screen-Aware Assistants Will Expand
As models become better at interpreting interfaces and calling tools, computer-use assistants are likely to become more practical on laptops, phones, and embedded systems.
(+1) Local Document Processing Will Grow
Businesses have strong incentives to process invoices, forms, screenshots, receipts, and internal documents locally when possible.
(+1) Hybrid AI Architectures Will Become Normal
The most effective applications will likely combine small local models for routine tasks with larger cloud models for difficult reasoning.
(+1) Multimodal Agents Will Become More Capable
Grounding, multi-image reasoning, tool use, and screen understanding are exactly the capabilities required for the next generation of visual AI agents.
(-1) Small Models Will Not Replace Large Models Everywhere
Complex reasoning, difficult scientific tasks, and highly demanding multimodal workloads will continue to favor larger models.
(-1) Hallucination Will Remain a Major Challenge
Better perception does not guarantee factual correctness, and connecting AI models to tools makes reliability even more important.
The Bigger Picture: Intelligence Is Moving Closer to the User
LFM2.5-VL-3B represents something larger than another entry in an increasingly crowded multimodal model market.
It reflects a fundamental change in how AI is being designed.
The question is no longer simply, “How powerful can an AI model become?”
The question is becoming, “How much useful intelligence can we put everywhere?”
A model that can understand screens, read documents, locate objects, compare images, follow instructions, call tools, and operate locally can become part of an entirely different computing paradigm.
Instead of users constantly sending information to AI services, devices themselves can increasingly understand what is happening around them.
A phone could understand its screen.
A laptop could understand a workflow.
A camera could understand its environment.
An industrial computer could understand machinery.
A browser could understand the page it is displaying.
And an AI assistant could potentially connect all of those observations to software actions.
That is the promise behind edge multimodal intelligence.
Final Verdict
LFM2.5-VL-3B should not be judged solely by whether it beats every larger vision-language model on every benchmark.
Its more interesting achievement is balance.
It combines a relatively compact parameter footprint with stronger visual understanding, significantly improved grounding, better multi-image reasoning, improved instruction following, and much stronger tool-use performance.
Its benchmark profile is not perfect, and larger models remain superior for some complex reasoning tasks.
But that misses the point.
For many real-world applications, the ideal AI model is not the largest one available.
It is the model that is fast enough, small enough, capable enough, affordable enough, and private enough to actually run where the user needs it.
That is where LFM2.5-VL-3B becomes compelling.
The future of AI may not be one giant model answering every request from a distant data center.
It may be thousands of smaller models quietly operating across phones, PCs, browsers, vehicles, robots, cameras, factories, and embedded devices.
And if that future arrives, models like LFM2.5-VL-3B will be among the technologies helping build it.
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