GLM 4.5V
Z.AI · released Aug 10, 2025 · zai-org/GLM-4.5V
- Type
- Open weightsMIT License
- Params
- 108B
- Context
- 66K
about 49K words of context
Our take
Written Aug 2, 2026GLM 4.5V is a 108-billion-parameter vision-language model from Z.AI with a permissive MIT licence. It accepts text and images, and its strongest measured skill is coding rather than general chat or creative writing.
Pick this for open-weights vision-language work that needs a genuinely permissive licence, or for coding-heavy workloads where its Arena Coding score is the highest sub-category. Use it when you want predictable budgeting: every tracked host charges the same rate, so there is no arbitrage hunt. Skip it if you need strong creative writing performance, or if throughput consistency matters more than price predictability.
The case for it
- Highest measured skill is coding among Arena sub-categories, with a 50.8-point gap over its general text score.
- MIT licence allows commercial use, modification and redistribution without restriction.
- Identical pricing across all four tracked offers, so provider choice is about speed, not cost.
The case against it
- Creative writing lags its other Arena skills by a wide margin, with a 94.4-point gap below its coding score.
- Throughput varies sharply by provider at the same price: one host delivers 1.66 times the tokens per second of another.
- No disclosed active parameter count, so efficiency claims cannot be verified.
How good is it?
IntelligencePuzzles, maths, exam questions
Arena Text (overall)99th of 143 · 1353.3
CodingWriting and fixing code on its own
Arena Coding100th of 143 · 1404.2
Arena Coding is the only board that has scored it for this.
AgenticPlanning, calling tools, staying on task
Nobody we watch has scored GLM 4.5V for this. We would take the rating from Arena Agent (IPS).
WritingWe do not rate this
Two boards come close and neither tests writing: Arena Creative Writing asks people which of two replies they prefer, and LiveBench Language tests whether a model understood a passage. So we show where GLM 4.5V placed and give it no mark out of five.
These tests check whether a model follows instructions — a precondition for all the work above, but not a measure of how well that work is done, which is why they get no rating.
Every published score for this model6 scoresEvery figure we hold, from 6 boards, with who ran it and a link to the source — including the boards no rating above is built on.
Can you run it yourself?
- Fits in memory
- weights load entirely on the card
- Spills to system RAM
- some weights offload; much slower
- Too large
- will not load even with offload
- est
- size is calculated; the verdict could change by 10%
GeForce RTX 4090 · 24 GB
Too large for this card. The weights do not fit even with part of them offloaded to system memory.
Apple M1 Pro (16-core GPU) · 32 GB
Too large for this card. The weights do not fit even with part of them offloaded to system memory.
Apple M2 Max (38-core GPU) · 96 GB
Borderline fit on an estimated size. It leaves 0.5 GB spare on a size we calculated rather than measured, and a 10% error either way would change the answer.
Memory use by level
Against a 24 GB card.
All 3 sizes here are calculated, not measured. We hold no measured file for this model, so each size comes from the parameter count and every verdict above inherits that uncertainty.
Check against your own machine → · All 71 devices, with every size →
Or rent it from someone else
Cheapest of 4 live listings. Picked at the widest standard context we hold, within one quantisation slice, so the numbers beside it are a price one host actually charges.
- per 1M tokens
- $0.60 in / $1.80 out
- Context served
- 66K
- Throughput
- Not measured
| Provider | In / out per 1M tokens | Context | Throughput | Trains on prompts | Logs prompts | Zero retention |
|---|---|---|---|---|---|---|
| OpenRouter | $0.60 / $1.80 | 66K | not measured | Unknown | Unknown | Unknown |
| Novita AIfp8 | $0.60 / $1.80 | 66K | 63 tok/s | No | No | Confirmed |
| Novita AI | $0.60 / $1.80 | 66K | not measured | Unknown | Unknown | Unknown |
| Z.AIfp8 | $0.60 / $1.80 | 66K | 21 tok/s | No | No | Confirmed |
Novita AIAcross the 4 listings we hold: 2 say they do not train on prompts, 0 say they do and 2 do not say. 2 appear in the zero-retention registry we check; the rest are unknown to us rather than confirmed either way.
What each host's API supports
From the parameter list each endpoint publishes. Streaming is omitted: nothing we hold reports it, for any model.
- ✓
- Supported
- ✗
- Not supported
- Not published
- host gave no parameter list
| Provider | Tool calling | JSON output | Strict schema |
|---|---|---|---|
| OpenRouter | ✓ | ✓ | ✗ |
| Novita AIfp8 | ✓ | ✓ | ✗ |
| Novita AI | |||
| Z.AIfp8 | ✓ | ✓ | ✗ |
Tool calling: 3 of 4 listings say yes, 1 publishes no parameter list. JSON output: 3 of 4 listings say yes, 1 publishes no parameter list. Strict schema: 0 of 4 listings say yes, 3 say no, 1 publishes no parameter list.
Models people weigh against GLM 4.5V
When we formed this view
Dates behind this page
Prices last checked 38h ago
What we do not know about this model yet
- We hold no measured file for it, so all 3 sizes on this page are calculated from the parameter count.
- 1 of 4 listings publish no parameter list, so what their API accepts is unknown to us.
- Nothing we hold says whether an endpoint streams, so we do not show it either way.
- 2 of 4 listings do not say whether they train on prompts.
Licence and identifiers
What the licence allowsMIT License, what it allows commercially, and the identifiers you need to pull this model — its Hugging Face repo, our slug and a machine-readable card.
Licence
MIT License
Fully permissive: do anything with attribution. No patent grant, unlike Apache-2.0.
Identifiers
- Hugging Face
- zai-org/GLM-4.5V
- Architecture
- Mixture of experts
- Modality record
- text+image->text
- Catalogue slug
- z-ai-glm-4-5v