GLM-4.5
Up to 30%GLM-4.5 is available on LLM.API as an OpenAI-compatible chat endpoint—route GLM / Zhipu quality through one key with transparent token pricing.
What is GLM-4.5?
On LLM.API, GLM-4.5 (glm-4.5) serves as a GLM / Zhipu conversational model for products that need 128K tokens context windows and predictable token pricing. glm-4.5 provided by zai, embercloud. It is wired for API access through LLM.API with OpenAI-compatible patterns.
Providers
LLM.API routes GLM-4.5 to the providers below, with discounted effective rates versus list price.
List price by provider ($ / 1M tokens)
InputOutputProvider list prices; the LLM.API discount applies on top.
| Provider | Pricing | Context | Capabilities |
|---|---|---|---|
| in $600; out $2200 per 1M tokens | 128K tokens | tools, streaming, reasoning, JSON, structured | |
| in $600; out $2200 per 1M tokens | 128K tokens | tools, streaming, reasoning, JSON, structured | |
| in $600; out $2200 per 1M tokens | 128K tokens | tools, streaming, reasoning, JSON, structured |
Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 21 Sept 2026.
Try this model
Test GLM-4.5 right here — free to start.
Suggestions for your first prompt
Code snippet
Call GLM-4.5 through the OpenAI-compatible API — POST /v1/chat/completions.
from openai import OpenAI
client = OpenAI(
api_key="YOUR_LLMAP_KEY",
base_url="https://api.llmapi.ai/v1",
)
resp = client.chat.completions.create(
model="glm-4.5",
messages=[
{"role": "system", "content": "You are a precise product assistant."},
{"role": "user", "content": "Give me three crisp launch checklist items."},
],
)
print(resp.choices[0].message.content){
"model": "glm-4.5",
"messages": [
{"role": "system", "content": "You are a precise product assistant."},
{"role": "user", "content": "Give me three crisp launch checklist items."}
]
}5 Core Capabilities
Structured outputs
Can produce JSON-friendly or schema-oriented responses when prompted carefully. Tuned to how teams typically call GLM-4.5.
Instruction following
Follows detailed system and user instructions with strong adherence to format and tone. Tuned to how teams typically call GLM-4.5.
Safety-aware replies
Supports product policies with refusals and cautious handling of sensitive topics. Relevant for `glm-4.5` workloads on LLM.API.
Code assistance
Helps write, explain, refactor, and debug application code across common languages. Reflects GLM / Zhipu positioning for this endpoint.
Conversational UX
Maintains coherent multi-turn assistant behavior for product chat surfaces. Relevant for `glm-4.5` workloads on LLM.API.
6 Most Valuable Use Cases
- Sales and success email drafting with CRM context with GLM-4.5
- Internal knowledge assistants grounded with your retrieval layer
- Customer support copilots that draft accurate, on-brand replies with GLM-4.5
- Meeting-note cleanup and action-item generation
- Policy Q&A bots with careful refusal behavior with GLM-4.5
- Research synthesis across long documents and tickets
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
-
Intelligent AI Routing
Automatically route each request to the best model across providers based on latency, cost, and quality—without changing your integration or redeploying code.
One endpoint, every model. -
Cost-Aware Execution
Control spend with per-request cost estimation, smart model selection, and centralized quotas so teams can experiment fast without runaway bills or manual tracking.
More performance, less spend. -
Resilient Fallback Flows
Define automatic, provider-agnostic fallbacks to keep your app up during outages, rate limits, or timeouts—no brittle failover logic scattered through your codebase.
Never go dark on users. -
Deep LLM Observability
Trace every call across providers with logs, metrics, and request replay so you can debug, tune prompts, and optimize model choices from one unified dashboard.
See every token, everywhere. -
Task-Level Orchestration
Describe tasks, not models. LLM.API maps them to the right tools, models, and prompts so you ship complex AI workflows with minimal glue code.
Think tasks, not models. -
High-Throughput Batch APIs
Process millions of inferences efficiently with optimized batch pipelines, concurrency controls, and retry logic—all behind the same simple interface you use for single calls.
Scale from 1 to millions.
Why run GLM-4.5 on LLM.API?
Unified AI Routing
Reach GLM-4.5 and sibling models through one OpenAI-compatible endpoint.
Cost Control
Compare provider price points and keep spend visible as you scale GLM-4.5.
Reliability Layer
Retry and route across configured providers when a single upstream blips.
Observability
Trace prompts, tokens, and errors for GLM-4.5 alongside the rest of your stack.
Drop-in SDKs
Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.
Model Breadth
Production: Swap GLM-4.5 for chat, media, or embedding alternatives without rewriting auth.
When to Use — When NOT to Use
Use it if...
- You want OpenAI-compatible chat completions through a single LLM.API key (GLM-4.5)
- Your prompts benefit from the model's family strengths (reasoning, speed, or cost) (GLM-4.5)
- You need provider failover options exposed for this model id (GLM-4.5)
- You need a general-purpose text model for assistants, agents, or content workflows (GLM-4.5)
Avoid if...
- You need guaranteed real-time hard latency SLAs without benchmarking the provider
- Your use case depends on unpublished proprietary benchmarks not listed here
- You require on-prem only deployment with no cloud inference
- You need pure embedding, OCR, or media generation instead of chat
COMMUNITY
What developers say about GLM models
Summarised from publicly published developer and community reviews of this model family. Opinions are the sources’, not LLM.API’s, and may not be specific to GLM-4.5.
- LocalLLM communities describe GLM as a cost-effective everyday coding model, popular for saving quota on more expensive assistants.
- Blind multi-judge code reviews on Reddit scored GLM well for production-readiness against much pricier competitors.
- Reported weak spots: large monorepos, complex debugging, and context stability under long sessions — results vary by provider and harness.
SOURCES
Frequently Asked Questions
Where is the canonical page for GLM-4.5?
https://llmapi.ai/models/zhipu-glm-4-5/
What are limitations of GLM-4.5?
Like other API models, GLM-4.5 can be wrong, incomplete, or uneven on edge cases. Validate outputs for high-stakes use. Media/OCR/STT models additionally depend on input quality.
What is GLM-4.5?
glm-4.5 provided by zai, embercloud. It is wired for API access through LLM.API with OpenAI-compatible patterns. On LLM.API it is addressed as `glm-4.5`.
When should I choose GLM-4.5?
You need provider failover options exposed for this model id — especially when you specifically need GLM-4.5.
Can I use tools or structured outputs with GLM-4.5?
Tool and/or structured-output flags appear on one or more providers for this model—confirm in the providers table.
Which providers serve GLM-4.5?
LLM.API currently lists: embercloud, llmapi-os, zai. Availability can vary by region and account.
What modalities does GLM-4.5 support?
GLM-4.5 accepts text and produces text according to its architecture metadata on LLM.API.
Does GLM-4.5 support streaming?
Yes—at least one listed provider advertises streaming.
Is GLM-4.5 a chat model?
Yes—GLM-4.5 is exposed as a chat/completions-style endpoint on LLM.API.
How do I call GLM-4.5 via API?
Send OpenAI-compatible requests to https://api.llmapi.ai/v1 with model "glm-4.5" and your LLM.API key. See the code snippet on this page.
What is the context length for GLM-4.5?
Reported context for GLM-4.5 is 128K tokens. Always verify the active provider row if multiple providers are listed.
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