An LLM (large language model) takes text in and produces text out. A VLM (vision language model) adds a vision encoder in front of a language model, so it can take images as well as text and answer in text. Every VLM contains a language model; most LLMs cannot see. That one difference decides what each can do on a production line, how fast it runs and what it costs per decision.
This article is part of our guide to industrial AI, which covers where AI is used in manufacturing, the model types behind it and how to deploy them. Here we compare VLMs and LLMs in detail and answer the question most manufacturers ask next: can a VLM inspect parts?
What is an LLM?
A large language model is a neural network, almost always a transformer, trained on very large amounts of text to predict the next token. A token is a piece of a word: in English, one token averages about four characters, or roughly three-quarters of a word. Given a prompt, the model generates its answer one token at a time.
Because it has absorbed patterns from code, manuals, standards and technical writing, an LLM is good at reading and writing: summarizing a test report, drafting a work instruction, explaining an error log or writing a first version of a test sequence. It has no direct sense of the physical world; it only knows what text describes.
What is a VLM?
A vision language model is a multimodal model that accepts images (and sometimes video) alongside text. It has three parts:
How a vision language model turns an image into a text answer. Remove the first three boxes and what is left is an LLM.
- Vision encoder. Usually a vision transformer (ViT). It cuts the image into small square patches, for example 14×14 or 28×28 pixels, and converts each patch into a vector. A 336×336-pixel image cut into 14-pixel patches becomes 24 × 24 = 576 patches.
- Projector. A small network that translates those vectors into the same space as the language model’s word tokens, so the image becomes a sequence of “image tokens”.
- Language model. The same kind of LLM described above. It reads the image tokens and the text prompt together and generates a text answer.
Because images become tokens, they consume the model’s context and compute the same way text does. A single 1-megapixel image typically costs more than a thousand tokens, which is why image size matters so much for speed and cost. We work through the numbers in VLM token usage: how image tokens are counted.
VLM vs LLM: side-by-side comparison
| LLM | VLM | |
|---|---|---|
| Input | Text (and code) | Images or video plus text |
| Output | Text | Text (some also output boxes or masks) |
| Architecture | Transformer language model | Vision encoder + projector + language model |
| How input is measured | Text tokens, about 0.75 words each | Text tokens plus image tokens, often 500–1,600+ per image |
| Training data | Text | Image-text pairs, then instruction tuning |
| Speed per request | Fast for short prompts | Slower: every image adds hundreds to thousands of tokens |
| Strengths | Reading, writing, summarizing, code, reasoning over documents | Describing scenes, answering questions about images, reading labels and charts, zero-shot recognition |
| Weaknesses | Cannot see; can state wrong facts confidently | Misses fine detail; answers can vary between runs; costly at line rate |
| Typical industrial role | Work instructions, reports, test-code drafts, troubleshooting help | Defect triage and explanation, natural-language queries over images, operator assistance |
VLM, LLM, SLM, VLA and CNN: the related terms
The acronyms multiply quickly. These are the ones that come up in manufacturing projects:
| Term | What it is | Where it fits |
|---|---|---|
| LLM | Large language model: text in, text out | Documentation, reports, code, chat assistants |
| SLM | Small language model: a compact LLM, typically a few billion parameters or fewer | Runs on local or edge hardware where data must stay on site; less capable than large models |
| VLM | Vision language model: images and text in, text out | Visual question answering, defect description, image triage |
| VLA | Vision-language-action model: maps images and instructions to robot motions | Research and early pilots in robot manipulation; safety validation still immature |
| CNN / ViT detector | Purpose-trained vision model for one task: classify, detect or segment | The inline decision in AI vision inspection and counting |
| Anomaly detection model | Learns what normal parts look like and flags anything different | Rare or unknown defects, few bad samples |
Can a VLM inspect parts?
For 100% inline pass/fail inspection at production speed, not yet. For several jobs around inspection, yes. The reasons come down to four requirements a production inspection has to meet:
1. Accuracy on fine, rare defects
General VLMs are trained on web images, not on your castings or connectors. On MMAD, a 2024 benchmark of multimodal models on industrial anomaly detection, the best commercial model averaged about 75% accuracy. A production inspection is normally specified to well above 99% on its defect set, with a fixed false-reject rate. Purpose-trained detection and anomaly models reach that level because they are trained and validated on the actual parts and defects.
2. Repeatability
Ask a VLM about the same image twice and the wording, and sometimes the verdict, can change. Settings such as low temperature reduce this, but a classifier that outputs the same score for the same image every time is far easier to validate, gauge and audit.
3. Speed and cost per part
At 300 parts per minute, each part has a 200 ms window. A VLM must first encode the image into hundreds or thousands of tokens and then generate an answer token by token. A purpose-trained model on an edge GPU returns a score in a fraction of that time, at no per-image fee.
4. Validation
Quality systems need a known false-reject rate and escape rate measured on known-good and known-bad sets, and a way to show the system has not changed. That is straightforward for a frozen, purpose-trained model and much harder for a general model that may be updated by its provider.
Where VLMs do help on the plant floor
- Triage of flagged parts. The inline model rejects or flags a small share of parts; a VLM reviews those images, describes the defect and suggests a category for the operator or the quality record.
- Novel defects. When something new appears that the production model was never trained on, a VLM can often describe it, which speeds up labeling new training data.
- Natural-language questions. Engineers can ask “show me the scratches near the bore from last night’s second shift” across stored inspection images.
- Faster setup. Some inspection rules can be prototyped by describing them in words before a dedicated model is trained.
- Documentation. Turning inspection results into readable shift reports and nonconformance descriptions.
The practical architecture is therefore a hybrid: a fast, purpose-trained model makes the call on every part at the edge, and the VLM works on the exceptions. If you are deciding between AI and classical tools for the inline decision itself, see deep learning vs rule-based machine vision.
Which model for which job?
| Task | Best tool | Why |
|---|---|---|
| Pass/fail on every part at line speed | Purpose-trained CNN/ViT or anomaly model | Fast, repeatable, validated to fixed reject and escape rates |
| Dimensions, presence, alignment | Rule-based machine vision | Precise, needs no training data, fully traceable |
| Describe or categorize flagged images | VLM | Language output and general visual knowledge |
| Search stored images in plain English | VLM | Answers questions about images |
| Summarize test results or write work instructions | LLM | Text in, text out; no image needed |
| Draft test sequences or analysis code | LLM, reviewed by an engineer | Strong at code, but output must be verified |
Not sure which AI belongs on your line?
Sciotex designs and builds industrial AI systems for inspection, counting and test. We test on your parts first and use VLMs, purpose-trained models or classical vision where each one fits, designed, programmed, built and supported in the US since 1997.
Frequently asked questions
An LLM takes text in and produces text out. A VLM adds a vision encoder and projector in front of a language model, so it can accept images as well as text and answer questions about them in text.
Modern chat assistants such as ChatGPT, Claude and Gemini are built on multimodal models that accept images as well as text, so in that sense they work as VLMs. The underlying language component is an LLM.
VLM stands for vision language model, also written vision-language model: a model that understands images and text together and generates text.
Not for inline pass/fail inspection today. VLMs are slower and costlier per image and less accurate and repeatable on fine industrial defects than purpose-trained models. They work well alongside them, to triage, describe and search flagged images.
A VLM outputs text about an image. A vision-language-action (VLA) model goes one step further and outputs robot actions from images and instructions. VLAs are moving from research into pilots, but industrial safety validation is still immature.
