Apply your own routing policies, reduce token costs automatically, and see every routing decision in real time with Merge Gateway.
What GLM-4.5V costs to run
Test GLM-4.5V
with Gateway’s Simulator
See a prompt's output, token spend, latency, and more with GLM-4.5V.
Route requests to GLM-4.5V in minutes
1$ pip install merge-gateway-sdk1from merge_gateway import MergeGateway
2
3client = MergeGateway(api_key="YOUR_API_KEY")
4
5response = client.responses.create(
6 model="openai/gpt-5.2",
7 input=[
8 {"type": "message", "role": "system", "content": "You are a helpful programming tutor. Explain the concepts clearly with practical examples."},
9 {"type": "message", "role": "user", "content": "Explain the concept of recursion in programming with a simple set of examples."},
10 ],
11)
12
13print(response.output[0].content[0].text)1response = client.responses.create(
2 model="anthropic/claude-sonnet-4-20250514",
3 input=[
4 {"type": "message", "role": "system", "content": "You are a helpful programming tutor. Explain the concepts clearly with practical examples."},
5 {"type": "message", "role": "user", "content": "Explain the concept of recursion in programming with a simple set of examples."},
6 ],
7)1from openai import OpenAI
2
3client = OpenAI(
4 api_key="YOUR_API_KEY",
5 base_url="https://api-gateway.merge.dev/v1/openai",
6)1response = client.chat.completions.create(
2 model="gpt-5.2",
3 messages=[
4 {"role": "system", "content": "You are a helpful programming tutor. Explain the concepts clearly with practical examples."},
5 {"role": "user", "content": "Explain the concept of recursion in programming with a simple set of examples."},
6 ],
7)
8
9print(response.choices[0].message.content)1npm install merge-gateway-ai-sdk-provider ai1import { createMergeGateway } from "merge-gateway-ai-sdk-provider";
2
3const gateway = createMergeGateway({
4 apiKey: "YOUR_API_KEY",
5});1import { generateText } from "ai";
2
3const { text } = await generateText({
4 model: gateway("openai/gpt-4o"),
5 prompt: "Explain the concept of recursion in programming with a simple set of examples.",
6});
7
8console.log(text);1import { createOpenAI } from "@ai-sdk/openai";
2
3const gateway = createOpenAI({
4 apiKey: "YOUR_API_KEY",
5 baseURL: "https://api-gateway.merge.dev/v1/ai-sdk",
6});
7
8// All generateText/streamText calls work unchanged1from anthropic import Anthropic
2
3client = Anthropic(
4 api_key="YOUR_API_KEY",
5 base_url="https://api-gateway.merge.dev/v1/anthropic",
6)
7
8message = client.messages.create(
9 model="claude-sonnet-4-20250514",
10 max_tokens=1024,
11 messages=[
12 {"role": "user", "content": "Explain the concept of recursion in programming with a simple set of examples."},
13 ],
14)
15
16print(message.content[0].text)Explore other models available in Merge Gateway
GLM-4.5V FAQ
Heading
What other models does Z.ai offer?
Z.ai's GLM family spans text reasoning flagships, fast tiers, and multimodal models. Here are some other models Z.ai supports:
- GLM-5: the text reasoning flagship, a large Mixture-of-Experts model for complex, long-horizon tasks
- GLM-5-Turbo: the speed-and-cost-optimized tier of the GLM-5 line for interactive, high-volume workloads
- GLM-4.5: the text model GLM-4.5V is built on, for general reasoning without vision
How does GLM-4.5V differ from Z.ai's other models?
GLM-4.5V is Z.ai's open-source vision-language model, adding multimodal reasoning to the GLM-4.5 base.
- Modality: accepts image and video input alongside text, unlike the text-only GLM models, and is strong at OCR and document understanding
- Architecture: a Mixture-of-Experts model with around 106B total and 12B active parameters, which keeps inference efficient
- Context and output: roughly a 64K to 131K-token context window with up to about 32K output tokens
- Pricing: around $0.60 per 1M input and $1.20 per 1M output
- Licensing: open-source, so you can self-host it if you prefer to run multimodal inference in your own environment
Reach for GLM-4.5V when you need open, cost-effective vision-language reasoning over images and video.
What models should I consider using alongside GLM-4.5V?
No single model is optimal for every task. Here are models worth pairing with GLM-4.5V depending on what your product needs:
- Qwen3-VL Plus as another vision-language option to benchmark against on your image mix
- GLM-5 for the text-only reasoning steps that follow image or video extraction
- Claude Opus 4.8 when extracted visual data feeds complex downstream reasoning or code
- Gemini 3.5 Flash for high-volume, low-cost multimodal tasks where peak accuracy isn't required
What are the challenges of using GLM-4.5V in my product?
Like any production LLM, GLM-4.5V comes with tradeoffs worth planning for:
- Vision accuracy varies: quality depends on image type, such as dense documents, handwriting, or low resolution, so evaluate on your real inputs
- Provider dependency: relying only on Z.ai creates fragility during outages or model retirements
- Cost at scale: image and video tokens add up fast, so high-volume multimodal traffic needs active budgeting
- Data residency and procurement: Z.ai is a China-based provider, which can raise data-governance or vendor-approval questions for some enterprises
- Context ceiling: the roughly 131K-token window is modest next to frontier long-context models for very large inputs
Why should I use Merge Gateway to route LLM requests with GLM-4.5V and every other model?
Routing GLM-4.5V through Merge Gateway pairs the model with the infrastructure you'd otherwise build yourself:
- One API, every provider: Call GLM-4.5V and every other major model from a single endpoint and key, switching models with a string change rather than a code change
- Intelligent routing and automatic failover: Merge fails over around Z.ai outages on its own, and routing on cost, latency, or quality can lower spend by 40 to 60% with no code edits
- Cost governance: Cap GLM-4.5V spend with project budgets, and see every request attributed by model, project, and tag in one cross-provider dashboard
- Build Your Own Router: Define "best" for your traffic with curated benchmarks or your own evals, and let the router choose per request and explain why
- Security and compliance controls: Run DLP and prompt injection checks before requests reach Z.ai, and enforce per-project model and region rules centrally
How can I start routing requests to GLM-4.5V via Merge Gateway?
Getting GLM-4.5V running through Merge Gateway takes a few minutes:
1. Create an account and get your API key from the dashboard.
2. Install the Merge Gateway SDK: run pip install merge-gateway-sdk (Python) or npm install merge-gateway-sdk (Node). Alternatively, if you're already using the OpenAI SDK, set base_url = "https://api-gateway.merge.dev/v1/openai" and your existing code works as-is.
3. Make your first request using the provider/model format. For GLM-4.5V, the model string is zhipu/glm-4.5v (confirm the exact slug in the dashboard). Swap the model string to route to any other provider without changing anything else.
4. Configure a routing policy in the dashboard to set failover behavior, cost limits, and optimization strategy. Your first policy can be as simple as naming GLM-4.5V as primary with one fallback.
Full setup instructions and SDK references are in the Merge Gateway docs.
Try GLM-4.5V through Merge Gateway
Route, observe, and control AI requests across providers from one API.
