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Free AI Visibility AuditGLM 4.5V is a vision-language model from Z.ai that reads text and images together, built for multimodal agent applications rather than text-only chat.
01 / decision
Each figure sits next to the middle of the field, so it can be read as dear or cheap, wide or narrow, rather than floating on its own. Compared against the 126 models we track, not against an absolute standard.
Capability
Not measured
No published benchmark scores yet
Input / 1M tokens
$0.60
field median $0.30
Output / 1M tokens
$1.80
field median $1.25
Context
66K
field median 524K
02 / overview
Four questions, answered separately, because somebody arrives at one of them rather than at the top of the page.
The job it was built for, and the job it is not for.
GLM 4.5V is a vision-language foundation model, meaning it takes images alongside text prompts and reasons over both. Z.ai positions it for multimodal agent work, including video understanding, so the natural jobs are screen reading, document and image interpretation, and agents that need to see what they are acting on. It is not a long-document text model, and it produces text rather than images.
Where it sits in its line, and when a sibling is the better pick.
GLM 4.5V first appeared in August 2025 as the vision member of Z.ai's GLM 4.5 family. Reach for it when the input is visual and the task is grounded in what is on screen or in the frame. It is the wrong pick for long text corpora or pure reasoning chains, where a text-first sibling with a larger context window will serve you better.
The API, the apps it powers, and what its limits let you do.
GLM 4.5V is reached through Z.ai's API and the aggregators that carry it, taking interleaved text and image input in a single request. The context window is mid-sized rather than generous, so it suits a handful of images and a working prompt rather than an entire visual archive, and the output ceiling points at structured summaries and agent actions rather than long-form writing. Z.ai uses a Mixture-of-Experts design, with only a fraction of its parameters active on any given pass.
What changes because this exists, or why it does not.
GLM 4.5V makes visual agent work cheap enough to run at volume, which is the part that was previously awkward: teams rationed screenshots and frames because the per-image cost added up. With Z.ai claiming state-of-the-art video understanding, the practical change is that watching a workflow, rather than being told about it, becomes a reasonable default for an agent.
Follows GLM 4.5 Air. Superseded by GLM 4.6. See the whole line.
03 / evidence
Split by category, so a strong number never hides a thin evidence base. Verified means we read it on the benchmark's own published results; a provider's claim about its own model is shown and labelled rather than dropped.
No published benchmark scores for this model yet.
We publish a score only where we can link the result to where it was published. Until a benchmark result for this model exists in a source we read, this section stays empty rather than being filled with a provider's marketing figure.
How we decide what counts as evidence04 / ledger
Every published row, grouped by category, each compared with the best published score on the same benchmark. 'Is 64% good' is a question nobody can answer; '26 points behind the leader' is one anybody can.
Nothing in the ledger yet.
Each row here carries a score, the benchmark it came from, what the leading model scored on the same test, and a link to the published result. Rows appear as results are published and read.
How we decide what counts as evidence05 / capability
Where this model is strong, and against how many peers. Ranks are against models with evidence in that category, not against everything we track: ranking against models nobody tested would rank who published, not who is better.
No category scores to shape yet.
A category score is the weighted mean of the benchmarks published for it. With no published rows there is nothing to average, and an empty chart drawn at zero would say something false.
How we decide what counts as evidence06 / cost
List API rates as last read from the provider, with the source on every row, plus every change we have recorded since we started tracking it.
| Charge | Price | Unit | Read on | Source |
|---|---|---|---|---|
| Input | $0.60 | per 1M tokens | 2026-09-24 | Check |
| Output | $1.80 | per 1M tokens | 2026-09-24 | Check |
GLM 4.5V sits at the cheap end of the vision-capable field, close enough to small text models that image input stops being a budget decision. Output costs a few times more than input, which is the usual shape, and favours use as a reader and classifier over use as a writer.
| Workload | Input / month | Output / month | Cost |
|---|---|---|---|
| A small product team | 20M tokens | 5M tokens | $21.00 |
| A busy support assistant | 200M tokens | 40M tokens | $192.00 |
| A document pipeline | 1000M tokens | 100M tokens | $780.00 |
List API rates, no caching and no batch discount, which both providers offer and which change the answer a great deal. Treat these as the ceiling, not the bill.
07 / specs
As listed by the provider's own catalogue and re-read every few hours. Anything absent is absent there too.
| Context window | 65,536 tokens |
|---|---|
| Maximum output | 16,384 tokens |
| Modalities | text, image |
| Released | 11/08/2025 |
| Status | Current |
| Catalogue identifier | z-ai/glm-4.5v |
08 / lineage
What this model replaced, what replaced it, and what else its provider has in the field.
Also from Z.ai
09 / line
Every model in this family in release order, so a page from eight months ago says in one glance that two newer ones exist.
Came before
← GLM 4.5 AirCame after
GLM 4.6 →Ordered by release date and worked out from the naming, so a new member slots in as soon as its page exists. A retired model keeps its page and its place in the line.
10 / notes
What this model changes for a brand trying to be cited in AI answers, and every change we have logged since it launched.
What it changes for you
GLM 4.5V shifts part of the AI-search question from text to pixels. If an assistant can now look at your product, your pricing page or a demo video and describe what it sees, then screenshots, interface labels, chart captions and image alt text become retrievable surface, not decoration. Worth auditing whether your visual assets say the same thing your copy does.
Most buyers meet GLM 4.5V through the API, either direct from Z.ai or via a model router, rather than through a consumer app with its own brand. It also turns up inside products that have quietly chosen it for screenshot reading, document parsing or in-app visual assistants, where the end user never sees the model name.
Nothing published here yet. Changes appear within hours of a provider announcing them.
11 / questions
The things people ask about this model, answered from what is on this page rather than from anywhere else.
Models change what gets cited. We measure whether AI answers name your brand or your competitors across every assistant, and show you what to change.