- Instruction Following
Seed-2.0-Mini is a compact multimodal large language model from ByteDance Seed optimized for latency-sensitive, high-concurrency, and cost-sensitive applications, offering long context and flexible reasoning modes.
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GLM 5V Turbo
- Instruction Following
GLM 5V Turbo is Z.ai’s native multimodal large language model optimized for vision-based coding and agentic workflows, able to process images, video, and text for complex software and automation tasks.
Providers Comparison
Speed
Output tokens per second · Higher is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
End-to-End Response Time
Seconds · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
Price
USD per 1M tokens (blended) · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
About the model
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.
Model capabilities
5 Core Capabilities
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Multimodal Inputs
Processes text, images, and video jointly, enabling tasks that require combined visual and textual understanding in a single workflow.
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Visual Reasoning
Understands complex scenes, UI layouts, and document structures from screenshots to support agentic navigation and inspection tasks.
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Vision Coding Agent
Supports interactive chat, following instructions, multi-step reasoning, and agent-style task execution across diverse knowledge and productivity scenarios.
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Code and Tools
Enables vision-based coding, code generation, and tool use, integrating with agent frameworks for automated software and workflow tasks.
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Cross-Lingual Text
Understands and generates text in multiple languages, enabling cross-lingual reasoning and content transformation between different language inputs.
Use cases
6 Most Valuable Use Cases
- Vision-based Code Generation
- Screenshot UI Automation
- Design-to-Frontend Conversion
- Visual Bug Detection
- Multimodal Agent Workflows
- GUI Navigation Agents
Transparent pricing
Cost Comparison
LLM API offers the lowest prices and best performance for GLM 5V Turbo–class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 120ms | 80 tps | 99.99% | $0.20 | $0.40 | 256K |
| Z.ai | Global | ~220ms | ~45 tps | ~99.9% | ~$0.35 | ~$0.70 | ~128K |
| OpenRouter | Global | ~260ms | ~40 tps | ~99.9% | ~$0.45 | ~$0.90 | ~128K |
| Together AI | US East | ~250ms | ~50 tps | ~99.9% | ~$0.40 | ~$0.80 | ~128K |
Performance benchmarks
Technical Specifications
| Metric | GLM 5V Turbo (Z.ai) | GPT-4.1 Mini (OpenAI) | Claude 3.5 Haiku (Anthropic) |
|---|---|---|---|
| Avg Latency | ~220ms | ~250ms | ~260ms |
| Context Window | 128K | 128K | 200K |
| Input Price ($/1M tokens) | $0.20 | $0.15 | $0.25 |
| Output Price ($/1M tokens) | $0.60 | $0.60 | $0.80 |
| Max Output Tokens | 4K | 4K | 4K |
| Throughput | ≥300 tps | ≥500 tps | ≥400 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 7.8B
- Prompt tokens processed (30 days)
- 420M
- Completion tokens generated (30 days)
- 12.6M
- API requests served (last 30 days)
- 99.95%
- Avg uptime (last 30 days)
Architecture & Integration
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
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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
Decision guide
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.
FAQ
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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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