Models/Z.ai/GLM 4.5V

GLM 4.5V

GLM-4.5V is a vision-language foundation model for multimodal agent applications. Built on a Mixture-of-Experts (MoE) architecture with 106B parameters and 12B activated parameters, it achieves state-of-the-art results in video understanding,...

01

Identity

65,536Context in
16,384Context out
2025-08-11 (OpenRouter created 1754922288)Released
OpenWeights
Canonical name
GLM 4.5V
Aliases
z-ai/glm-4.5v, zai-org/GLM-4.5V
Continuum hosted id
Not listed as a Continuum hosted id
OpenRouter slug
z-ai/glm-4.5v
Hugging Face
zai-org/GLM-4.5V
Modalities
text, image
Open weights Not on Continuum host list Open

Context window on the 2026-08-19 OpenRouter row: 65,536 tokens.

Max completion tokens on that row: 16,384.

Input / output on that row: $0.60 / $1.80 per 1M tokens.

Architecture fields: text+image->text · Other.

Hugging Face id on that row: zai-org/GLM-4.5V.

02

Should I use this for coding agents

GLM-4.5V is a vision-language foundation model for multimodal agent applications.

When you want the 2025-08-11 catalog SKU, not a later rename.

DeepSWE

No official DeepSWE mini-swe-agent row for this identity on the public board we fetched.

omitted, not invented as of 2026-08-13 source
Artificial Analysispublished integer
7
bench
Artificial Analysis
version
Intelligence Index
harness
published integer
metric
Intelligence Index
value
7
independent as of 2026-08-19 source
Vals Index

We did not open a vals.ai card for this identity. Chip omitted.

omitted, not invented as of 2026-08-19 source

Ranked view: Best coding models: the independent leaderboard puts this row and every other card that carries an independent coding score on one board, with the confidence intervals left visible.

Named benches, printed only

Copied from AA or vals HTML we opened. 0.0% placeholder bars are omitted. Do not average these into the hero tiles, and do not invent CI, $/task, or steps for Artificial Analysis.

BenchPrintedNoteSourceURLAs of
SciCode 18.8% Printed on the AA Intelligence Evaluations grid for GLM-4.5V (Non-reasoning). Not a DeepSWE chip. Artificial Analysis GLM-4.5V source 2026-08-19
Humanity's Last Exam 3.5% Printed on the AA Intelligence Evaluations grid for GLM-4.5V (Non-reasoning). Not a DeepSWE chip. Artificial Analysis GLM-4.5V source 2026-08-19
GPQA Diamond 57.3% Printed on the AA Intelligence Evaluations grid for GLM-4.5V (Non-reasoning). Not a DeepSWE chip. Artificial Analysis GLM-4.5V source 2026-08-19
AA-Omniscience Accuracy 18.2% Printed on the AA Intelligence Evaluations grid for GLM-4.5V (Non-reasoning). Not a DeepSWE chip. Artificial Analysis GLM-4.5V source 2026-08-19
03

When not to use it

Trust this before you buy

  • When you need a Continuum-hosted SKU from this lab, use the hosted card on this hub instead of this catalog row.
  • $0.60 in / $1.80 out per 1M on the 2026-08-19 OpenRouter row. Context 65,536 in / 16,384 out.
  • Need a denser published sibling on this hub? GLM 5.3 is the card already on the cluster.

Plus is $25/mo with $25 weekly hosted usage. The Mac app stays free with your own keys. Get Plus is not Download for Mac.

04

Artifact and Hugging Face downloads

Artifact

SPDX / license
mit
Total params
HF safetensors.total: 107,710,933,120 stored tensors
Native precision
HF tensors F32 + BF16
Repo size
HF API usedStorage 215,444,371,315 bytes
HF created
2025-08-10T13:55:30.000Z
Official HF repo
zai-org/GLM-4.5V
HF downloads
111,691
HF likes
721

Official weights id on the 2026-08-19 OpenRouter row: zai-org/GLM-4.5V. Community GGUF is a quant, not a second Continuum card.

Official weights

This is the model repo, not Download for Mac. Downloads and likes from the Hugging Face API on 2026-08-19.

Hub file tree

Safetensor shards
46
Shard bytes
215,424,400,616 bytes
Other file bytes
21,900,358 bytes
Tree file count
56
Hub usedStorage
215,444,371,315 bytes

usedStorage and the sum of .safetensors sizes can differ (LFS pointers, non-shard files, duplicate copies). Cited separately. Source: recursive tree API, 2026-08-19.

PathBytes
model-00001-of-00046.safetensors5,170,151,456
model-00002-of-00046.safetensors4,683,041,216
model-00003-of-00046.safetensors4,683,041,216
model-00004-of-00046.safetensors4,683,041,216
model-00005-of-00046.safetensors4,683,041,216
model-00006-of-00046.safetensors4,683,041,216
model-00007-of-00046.safetensors4,683,041,216
model-00008-of-00046.safetensors4,683,041,216
model-00009-of-00046.safetensors4,683,041,216
model-00010-of-00046.safetensors4,683,041,616
model-00011-of-00046.safetensors4,683,041,616
model-00012-of-00046.safetensors4,683,041,616
model-00013-of-00046.safetensors4,683,041,616
model-00014-of-00046.safetensors4,683,041,616
model-00015-of-00046.safetensors4,683,041,616
model-00016-of-00046.safetensors4,683,041,616
model-00017-of-00046.safetensors4,683,041,616
model-00018-of-00046.safetensors4,683,041,616
model-00019-of-00046.safetensors4,683,041,616
model-00020-of-00046.safetensors4,683,041,616
model-00021-of-00046.safetensors4,683,041,616
model-00022-of-00046.safetensors4,683,041,616
model-00023-of-00046.safetensors4,683,041,616
model-00024-of-00046.safetensors4,683,041,616
model-00025-of-00046.safetensors4,683,041,616
model-00026-of-00046.safetensors4,683,041,616
model-00027-of-00046.safetensors4,683,041,616
model-00028-of-00046.safetensors4,683,041,616
model-00029-of-00046.safetensors4,683,041,616
model-00030-of-00046.safetensors4,683,041,616
model-00031-of-00046.safetensors4,683,041,616
model-00032-of-00046.safetensors4,683,041,616
model-00033-of-00046.safetensors4,683,041,616
model-00034-of-00046.safetensors4,683,041,616
model-00035-of-00046.safetensors4,683,041,616
model-00036-of-00046.safetensors4,683,041,616
model-00037-of-00046.safetensors4,683,041,616
model-00038-of-00046.safetensors4,683,041,616
model-00039-of-00046.safetensors4,683,041,616
model-00040-of-00046.safetensors4,683,041,616
model-00041-of-00046.safetensors4,683,041,616
model-00042-of-00046.safetensors4,683,041,616
model-00043-of-00046.safetensors4,683,041,616
model-00044-of-00046.safetensors4,683,041,616
model-00045-of-00046.safetensors5,362,602,328
model-00046-of-00046.safetensors3,520,860,544
tokenizer.json19,970,699
model.safetensors.index.json1,898,765
README.md15,318
tokenizer_config.json7,341
chat_template.jinja3,858
config.json1,848
.gitattributes1,570
video_preprocessor_config.json365
preprocessor_config.json364
generation_config.json230

All 56 files the tree API returned are listed: every one of the 46 safetensor shards plus 10 other files. Files tab: https://huggingface.co/zai-org/GLM-4.5V/tree/main.

05

Continuum serving

OpenRouter slug z-ai/glm-4.5v on the 2026-08-19 catalog.

First party: https://docs.z.ai/.

Not on the Continuum host list fetched 2026-08-19.

Continuum hosted id not on the host list

We list first-party and OpenRouter, plus Continuum only when the live public allowlist named the id. This is not a 15-host routing table.

06

Economics

OpenRouter 2026-08-19: $0.60 in / $1.80 out per 1M tokens. Context 65,536 in / 16,384 out.

No separate internal-reasoning price on that row.

Price this model Opens the pricing calculator preloaded with GLM 4.5V.
07

Provenance

ClaimSourceAs of
OpenRouter id z-ai/glm-4.5v; context 65536; created 1754922288; $0.60 / $1.80 per 1MOpenRouter /api/v1/models2026-08-19
AA Intelligence Index 7; 4 printed Index benchesArtificial Analysis model page2026-08-19
OpenRouter id z-ai/glm-4.5v, context 65,536OpenRouter /api/v1/models2026-08-19
HF downloads 111,691Hugging Face API zai-org/GLM-4.5V2026-08-19
HF safetensors.total: 107,710,933,120 stored tensors; HF API usedStorage 215,444,371,315 bytes; created 2025-08-10T13:55:30.000Z; HF tensors F32 + BF16Hugging Face API zai-org/GLM-4.5V2026-08-19
46 safetensor shards; shard bytes 215,424,400,616; tree files 56Hugging Face tree API zai-org/GLM-4.5V2026-08-19
08

Compare, FAQ, and Get Plus

No other card in this cluster shares a published DeepSWE official row we can put next to this one. We will not compare on vendor-blog numbers.

Continue through Z.ai's model family with GLM 4.5.

FAQ

Why does the lab blog disagree with DeepSWE or Scale?

Lab posts pick a harness, an effort, a split, and sometimes a private eval set. DeepSWE publishes the official mini-swe-agent row with cost, tokens, and steps. Scale SWE-bench Pro only counts when the public shared-harness board has a row we can fetch. A higher lab number is usually a different test, not a better one.

OpenAI-compatible call

No Continuum hosted id is verified for this model, so this page does not invent a curl target. Use the first-party API or the OpenRouter slug z-ai/glm-4.5v.

Plus is $25/mo with $25 weekly hosted usage. The Mac app stays free with your own keys. Get Plus is not Download for Mac.