GLM-5V-Turbo API: Endpoint & Pricing
GLM 5V Turbo is Z.
What is GLM 5V Turbo?
GLM 5V Turbo is a multimodal foundation model from Z.ai designed to handle visual and textual inputs for code generation and environment-aware reasoning. It is mainly used for vision-grounded programming tasks such as turning screenshots, GUIs, and document layouts into executable code, and for powering autonomous agents that must perceive visual context before planning and executing actions. It belongs to Z.ai’s GLM-5 family of models as the vision-focused counterpart to text-centric GLM-5 and GLM-5 Turbo.
Can you self-host GLM 5V Turbo?
No. We found no public weights for GLM 5V Turbo on Hugging Face, so there is nothing to download, quantize or serve on your own GPUs: no VRAM budget, no vLLM or GGUF build to plan for. The only way to run it is over an API.
Skip the deploy — use LLM.API as your endpoint
No GPUs, no quantization trade-offs. Same model, OpenAI-compatible, with custom volume discounts on request.
Providers
Different companies host the same model. OpenRouter routes your request to one of them based on the routing mode you pick — Balanced (price + speed), Nitro (fastest), or Exacto (highest tool-calling accuracy).
List price by provider
InputOutputProvider list prices; the LLM.API discount applies on top.
| Provider | Input | Output | Cache read /M | Latency | Throughput | Uptime |
|---|---|---|---|---|---|---|
| in $1.2; out $4 per 1M tokens | 200K tokens | vision, tools, streaming, reasoning, JSON, structured |
Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 11 Oct 2026.
Try this model
Test GLM 5V Turbo right here — free to start.
Suggestions for your first prompt
Code snippet
Call the model through the OpenAI-compatible API.
Switching from OpenAI? Change 2 lines.
- base_url="https://api.openai.com/v1"
- api_key="YOUR_OPENAI_KEY"
+ base_url="https://api.llmapi.ai/v1"
+ api_key="YOUR_LLMAPI_KEY"Everything else stays the same — SDK, message format, tools, streaming. Set model to glm-5v-turbo and the call runs unchanged.
curl https://api.llmapi.ai/v1/chat/completions \
-H "Authorization: Bearer $LLMAPI_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "glm-5v-turbo",
"messages": [
{"role": "user", "content": "Give me three crisp launch checklist items."}
]
}'from openai import OpenAI
client = OpenAI(
api_key="YOUR_LLMAPI_KEY",
base_url="https://api.llmapi.ai/v1",
)
resp = client.chat.completions.create(
model="glm-5v-turbo",
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)import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.LLMAPI_KEY,
baseURL: "https://api.llmapi.ai/v1",
});
const resp = await client.chat.completions.create({
model: "glm-5v-turbo",
messages: [
{ role: "user", content: "Give me three crisp launch checklist items." },
],
});
console.log(resp.choices[0].message.content);stream = client.chat.completions.create(
model="glm-5v-turbo",
messages=[{"role": "user", "content": "Write a haiku about latency."}],
stream=True,
)
for chunk in stream:
delta = chunk.choices[0].delta.content
if delta:
print(delta, end="", flush=True){
"model": "glm-5v-turbo",
"messages": [
{"role": "system", "content": "You are a precise product assistant."},
{"role": "user", "content": "Give me three crisp launch checklist items."}
]
}ENDPOINTS
Supported endpoints & features
Base URL https://api.llmapi.ai/v1 · model ID glm-5v-turbo · OpenAI-compatible.
POST /v1/chat/completionsChat Completions — supported
| Feature | Status | How to set it |
|---|---|---|
| Function calling (tools) | Supported | tools, tool_choice |
| Parallel tool calls | Not supported | parallel_tool_calls |
| JSON mode | Supported | response_format: {"type": "json_object"} |
| Structured outputs | Supported | response_format: {"type": "json_schema", …} |
| Streaming | Supported | stream: true |
| Vision (image input) | Supported | image_url content parts |
| Web search | Not supported | web_search |
ERRORS
Errors & fallback routing
| Code | What it means | What to do |
|---|---|---|
401 | Missing or invalid API key. | Send Authorization: Bearer YOUR_LLMAPI_KEY and check the key is active. |
429 | Rate limited, or the upstream provider is throttling the request. | Back off and retry with jitter; the gateway also retries the request on another provider where one is available. |
5xx | Upstream provider error or timeout. | Retry; fallback routing sends the retry to the next healthy provider for this model. |
When a provider fails or throttles, the request is routed to the next provider serving this model — the ones listed in the providers table above.
GLM-5V-Turbo in production
What the model is good at.
-
Multimodal Inputs
Processes text, images, and video jointly, enabling tasks that require combined visual and textual understanding in a single workflow.
-
Visual Reasoning
Understands complex scenes, UI layouts, and document structures from screenshots to support agentic navigation and inspection tasks.
-
Vision Coding Agent
Supports interactive chat, following instructions, multi-step reasoning, and agent-style task execution across diverse knowledge and productivity scenarios.
-
Code and Tools
Enables vision-based coding, code generation, and tool use, integrating with agent frameworks for automated software and workflow tasks.
-
Cross-Lingual Text
Understands and generates text in multiple languages, enabling cross-lingual reasoning and content transformation between different language inputs.
What you get when you call GLM-5V-Turbo through LLM.API.
-
Unified AI Routing
Automatically route each request to the optimal model across providers based on latency, cost, and quality—without changing your integration.
One endpoint, any model -
Cost-Aware Orchestration
Control spend with policy-based routing, tiered model selection, and detailed cost breakdowns per request, team, and environment.
Optimize tokens, not code -
Resilient Fallback Flows
Define fallback chains that retry on errors, timeouts, or quota limits and transparently fail over to backup models or providers.
Stay online under load -
End-to-End Observability
Get traces, logs, metrics, and prompt-level analytics for every call so you can debug latency, failures, and quality issues in minutes.
See every token hop -
Task-Level Abstractions
Describe the task once—chat, classification, extraction, tools—and let LLM.API pick and configure the right underlying models and parameters.
Think tasks, not models -
High-Throughput Batch Jobs
Run massive offline workloads with parallelized batching, automatic rate-limit handling, and structured outputs you can pipe directly into your data stack.
Ship millions of calls
When to Use — When NOT to Use
Use it if...
- You need a cost-effective multimodal model for handling both text and images.
- You need a general-purpose assistant for chatbots, virtual agents, or user support.
- Your use case involves batch-processing many short prompts with reasonable quality demands.
- Your use case involves prototyping applications on Z.ai where tight provider integration helps.
- You need a versatile model for everyday coding help, content drafting, and Q&A.
- You need multilingual understanding and generation across common languages without top-tier specialization.
Avoid if...
- You need state-of-the-art reasoning or coding comparable to the very strongest frontier models.
- Your workload requires guaranteed support for extremely long contexts or large codebases.
- You need highly specialized domain performance, like advanced legal, medical, or scientific reasoning.
- You need battle-tested enterprise features such as extensive ecosystem tools and integrations.
- Your workload requires rigorously benchmarked safety, robustness, and compliance for regulated environments.
- You need ultra-low, predictable latency at massive scale with mature global infrastructure.
COMMUNITY
What developers say about GLM 5V Turbo
Summarised from public developer discussions and write-ups. Opinions are the sources’, not LLM.API’s.
- Developers on Hacker News report mixed results, praising the model's speed and API reliability while noting it underperformed in specific coding and reasoning tests compared to the non-visual GLM 5.1 version.
- Engineers using the model for frontend development on personal blogs and CSDN highlight strong performance in UI/UX tasks and screenshot-to-HTML conversion, though some experienced inconsistent execution without manual oversight.
- Developers on GitHub and CSDN noted that while the model is effective for agentic workflows, its processing speed can be significantly slower than text-only variants like GLM-5-Turbo, particularly during peak usage.
SOURCES
Frequently Asked Questions
-
What is GLM 5V Turbo?
GLM 5V Turbo is a multimodal large language model by Z.ai optimized for fast, cost-efficient text and vision understanding.
-
What modalities does GLM 5V Turbo support?
GLM 5V Turbo supports text input/output and image understanding, enabling vision-language applications like image captioning, description, and grounded Q&A.
-
How do I access GLM 5V Turbo through LLM.API?
You can call GLM 5V Turbo by setting the provider to "zai" (or equivalent) and the model name to "glm-5v-turbo" in LLM.API requests.
-
What is the context window of GLM 5V Turbo?
GLM 5V Turbo supports a context window of up to 32K tokens, allowing relatively long prompts and multi-step interactions.
-
What is GLM 5V Turbo best suited for?
GLM 5V Turbo is best for multimodal applications combining text and images, such as document understanding, UI analysis, and visual question answering.
-
How does the pricing of GLM 5V Turbo work on LLM.API?
On LLM.API, GLM 5V Turbo is billed per input and output token, with rates defined in the LLM.API pricing configuration for Z.ai models.
-
How fast is GLM 5V Turbo in terms of latency?
GLM 5V Turbo is optimized for low latency responses, especially for interactive chat and tool-calling scenarios, though exact speed depends on request size.
-
How does GLM 5V Turbo compare to similar multimodal models?
Compared to similar multimodal models, GLM 5V Turbo targets a balance of strong vision-language quality with lower cost and faster responses.
-
Does GLM 5V Turbo support streaming responses via LLM.API?
Yes, GLM 5V Turbo supports token streaming over LLM.API when you enable the streaming option in your request.
-
What are the main limitations of GLM 5V Turbo?
GLM 5V Turbo can hallucinate, may misinterpret complex images, and should not be relied on for safety-critical or legally binding decisions.
COMPARE
Competitive Models
GLM-5-Turbo
$1.2 in / $4 out per 1M tokens, 200,000 context — the closest current alternative to GLM-5V-Turbo.
Qwen3 Max 2026-01-23
$1.2 in / $6 out per 1M tokens, 262,144 context — the closest current alternative to GLM-5V-Turbo.
Claude Haiku 4.5 (2025-10-01)
$1 in / $5 out per 1M tokens, 200,000 context — the closest current alternative to GLM-5V-Turbo.
GLM 4.6V is Z.ai’s open-source, large-scale vision-language model that supports images, video, documents, and text with a long context window and native tool use. It is notable for combining high-quality multimodal understanding with function calling and cloud- or local-friendly variants.
GLM 4.7
GLM 4.7 is Z.ai’s flagship large language model, optimized for strong coding performance and stable multi-step reasoning. It is notable for its very large context window and open-source availability under Apache 2.0.
GLM 5.1
GLM 5.1 is Z.ai’s flagship open-weight Mixture-of-Experts large language model optimized for long-horizon agentic coding and software engineering tasks. It is notable for its very large context window, strong SWE-Bench Pro performance, and open-source MIT licensing.
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