What is GLM 4.5V API?
GLM 4.5V is available through AIMLAPI.
Technical Specifications
- Supported inputs: Image, Text
- Output: Text
- Context window: 65,536 tokens / Max output: 16,384 tokens
- Endpoint: /chat/completions
Performance Benchmarks
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GLM 4.5V API Pricing
- Input: $0.78 / 1M tokens
- Output: $2.34 / 1M tokens
Where to Use GLM 4.5V
Coding and code review
Generate, debug, and review code across languages, reading whole files or repos in a single prompt.
Agentic and multi-step workflows
Reliable tool-calling and instruction-following suit agent loops that plan, call tools, and iterate.
Complex reasoning
Extended reasoning improves reliability on multi-step math, logic, and technical analysis.
Long-document analysis
Summarize and extract from contracts, papers, and reports without aggressive chunking.
Code Sample
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Measured on the AI/ML API gateway across live traffic. Figures are medians unless stated. Last updated 2026-10-12.
GLM-4.5V is documented as suited to webpage coding, where it can analyze webpage screenshots or screen recordings, understand layout and interaction logic, and generate usable webpage code.
It is also documented for grounding tasks, including precisely identifying and locating target objects for practical uses such as security checks, quality inspections, content reviews, and remote sensing monitoring.
Another documented use case is GUI agent work, where it can recognize and process screen images and support actions like clicking and sliding for operational tasks.
It is further documented for complex long document interpretation, including summarization, translation, chart extraction, and producing insights from lengthy documents.
import requests r = requests.post( "https://api.aimlapi.com/v1/chat/completions", headers={"Authorization": "Bearer " + AIMLAPI_KEY}, json={ "model": "z-ai/glm-4.5v", "messages": [ { "role": "user", "content": "Hello!" } ] }, ) print(r.json())
const r = await fetch("https://api.aimlapi.com/v1/chat/completions", { method: "POST", headers: { Authorization: `Bearer ${process.env.AIMLAPI_KEY}`, "Content-Type": "application/json", }, body: JSON.stringify({ "model": "z-ai/glm-4.5v", "messages": [ { "role": "user", "content": "Hello!" } ] }), }); console.log(await r.json());
curl -X POST https://api.aimlapi.com/v1/chat/completions \ -H "Authorization: Bearer $AIMLAPI_KEY" \ -H "Content-Type: application/json" \ -d '{"model":"z-ai/glm-4.5v","messages":[{"role":"user","content":"Hello!"}]}'
OpenAI-compatible — swap the base URL and it works with your existing SDK.
| Type | Price |
|---|---|
| Input | |
| Output | |
| Cached input |
| Benchmark | Score | What it measures | Source | Retrieved |
|---|---|---|---|---|
| OSWorld | 35.8% | Computer-use across real desktop applications | Source | July 12, 2026 |
| MMMU | 75.4% | College-level multimodal understanding + reasoning | Source | July 12, 2026 |
| Intelligence | 7.6 | Composite score across standardised reasoning, knowledge and problem-solving evaluations, measured independently by Artificial Analysis | Source | September 12, 2026 |
| Math | 73 | Composite score across standardised mathematics evaluations, measured independently by Artificial Analysis | Source | September 12, 2026 |
| Model | Input | Output | Context | Best for |
|---|---|---|---|---|
GLM 4.5V This page | Reasoning + agents | |||
| Reasoning + agents | ||||
| Balanced coding + agents | ||||
| Long-context, multimodal & agentic workflows | ||||
| Reasoning + agents |
GLM 4.5V has a 65,536 tokens context window and can return up to 16,384 tokens.
GLM 4.5V takes image, text as input and returns text.
Use z-ai/glm-4.5v as the model id. Requests go to https://api.aimlapi.com/v1/chat/completions.
GLM 4.5V is priced at input $0.82524 / 1M tokens, output $2.47572 / 1M tokens, cached input $0.151294 / 1M tokens.
Yes, GLM 4.5V can stream responses as they are generated.
Yes, GLM 4.5V accepts image input alongside text.
GLM 4.5V was built by Zhipu AI.
