- Text Generation
Qwen3 VL 32B Instruct is a 32-billion-parameter multimodal vision-language model from Qwen, designed for high-precision understanding and reasoning over text, images, and video with a very…
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GLM 4.6V
- Text Generation
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.
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 4.6V?
GLM 4.6V is Z.ai’s 106B-parameter multimodal foundation model for visual reasoning over text, images, and video. It is mainly used for tasks like document and image understanding, code and data analysis, and agent-style workflows that rely on native function calling. It also powers applications needing long-context (around 128K–131K tokens) multimodal chat and reasoning, from research assistants to enterprise AI tools. GLM 4.6V belongs to the GLM-V family and follows earlier GLM-4.5V and GLM-4.5-Air models, alongside the smaller GLM-4.6V-Flash variant.
Model capabilities
5 Core Capabilities
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Multimodal Chat
Engages in context-aware conversations with long text and mixed media inputs using a large 128K context window.
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Image Understanding
Analyzes images, complex layouts, charts, and documents, extracting structure and semantics for downstream reasoning or generation.
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Advanced Reasoning
Performs multi-step reasoning on text and visual inputs, supporting chain-of-thought style problem solving and complex analysis.
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Visual OCR
Reads and interprets text from screenshots, scanned documents, tables, and natural images as part of its visual understanding pipeline.
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Language Translation
Translates between multiple languages within multimodal conversations, preserving context from accompanying images or documents.
Use cases
6 Most Valuable Use Cases
- Document Visual Parsing
- Legal Case Review
- Regulatory Case Monitoring
- Retail Product Analytics
- Multimodal Agent Tooling
- Vision-Based Tagging
Transparent pricing
Cost Comparison
LLM API offers the lowest cost and latency for GLM 4.6V-class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 80ms | 120 tps | 99.99% | $0.20 | $0.40 | 200K |
| Z.ai | Global | ~220ms | ~40 tps | ~99.9% | ~$0.60 | ~$1.20 | ~128K |
| OpenAI (GPT-4.1 mini vision-equivalent) | Global | ~180ms | ~80 tps | 99.9% | ~$0.50 | ~$1.00 | 128K |
| Google (Gemini 1.5 Flash Vision-equivalent) | Global | ~190ms | ~70 tps | 99.9% | ~$0.45 | ~$0.90 | 128K |
| Anthropic (Claude 3.5 Sonnet Vision-equivalent) | Global | ~210ms | ~50 tps | 99.9% | ~$0.70 | ~$1.40 | 200K |
Performance benchmarks
Technical Specifications
| Metric | GLM 4.6V | GPT-4o | Claude 3.5 Sonnet |
|---|---|---|---|
| Latency per Image | ~220ms | ~250ms | ~260ms |
| Throughput | 45 img/s | 40 img/s | 35 img/s |
| Max Resolution | 4K | 4K | 4K |
| Price per Image | $0.003 | $0.005 | $0.004 |
| Supported Formats | PNG, JPG, WEBP | PNG, JPG, WEBP, GIF | PNG, JPG, WEBP |
| Max Output Tokens (per call) | 4K | 4K | 4K |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 11.4B
- Prompt tokens processed (last 30 days)
- 7.8M
- API requests served (last 30 days)
- 9.6B
- Completion tokens generated (last 30 days)
- 99.8%
- 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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Dynamic AI Routing
Define routing rules once and automatically send each request to the optimal model across providers based on latency, cost, or quality—without changing your app code.
One endpoint, any model -
Cost-Aware Orchestration
Mix premium and budget models with granular controls, rate limits, and caps so you can aggressively optimize spend without sacrificing reliability or user experience.
Lower cost per call -
Automatic Fallback Logic
Configure provider-agnostic retries and fallbacks so requests seamlessly fail over to backup models on timeouts, rate limits, or outages—no brittle error handling.
Resilience by default -
End-to-End Observability
Get centralized logs, traces, and metrics for every AI call across providers, with request replay and tagging to debug issues and tune performance quickly.
See every token -
Task-Level Abstractions
Describe tasks like chat, extraction, or tools once and let LLM.API handle model-specific prompts, parameters, and formats behind a stable, versioned contract.
APIs, not prompts -
High-Throughput Batching
Send thousands of requests in a single batch with concurrency controls and retries, maximizing throughput while keeping provider limits and costs under control.
Scale without throttling
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a vision-language model to interpret images and generate grounded textual descriptions.
- You need multimodal question answering where users query charts, screenshots, or UI images.
- You need to build assistants that can read photographed documents and extract key fields.
- Your use case involves educational tools that explain diagrams, figures, or handwritten notes.
- You need a general-purpose LLM for everyday coding, drafting, and chat-style interactions.
- Your use case involves multimodal chatbots that must reference both text and image context.
Avoid if...
- You need guaranteed support for extremely long text contexts beyond typical commercial LLM limits.
- Your workload requires certified compliance regimes like HIPAA, FedRAMP, or specific regional mandates.
- You need highly optimized, low-latency inference on constrained edge devices without GPU acceleration.
- Your workload requires exhaustive tool use, plugins, or tightly integrated proprietary ecosystem features.
- You need battle-tested performance on very specialized domains like theorem proving or formal verification.
- Your workload requires stable, versioned APIs with long-term enterprise support and SLAs today.
FAQ
Frequently Asked Questions
-
What is GLM 4.6V?
GLM 4.6V is a multimodal Z.ai model accessible through LLM.API, designed for combined text and image understanding and generation.
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What is GLM 4.6V best suited for?
GLM 4.6V is best for vision-language tasks like image captioning, visual question answering, UI understanding, and workflows mixing images with natural language.
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What modalities does GLM 4.6V support?
GLM 4.6V supports text input and output plus image input, enabling rich vision-language interactions via a single API.
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What is the context window of GLM 4.6V?
GLM 4.6V supports a 32K token context window for prompts and conversation history combined.
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How fast is GLM 4.6V on LLM.API?
GLM 4.6V is optimized for low-latency responses, with typical first-token times under a second for short prompts, excluding network overhead.
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How is GLM 4.6V priced on LLM.API?
LLM.API charges for GLM 4.6V on a pay-per-token basis for prompt and completion tokens, following the Z.ai GLM 4.6V pricing tier.
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How do I call GLM 4.6V through LLM.API?
Use the unified LLM.API chat or completions endpoint and set the model parameter to the GLM 4.6V identifier provided in the dashboard.
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How does GLM 4.6V compare to similar multimodal models?
GLM 4.6V targets strong vision-language quality with competitive cost, generally trading slightly lower raw performance for better efficiency than frontier multimodal models.
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What are the main limitations of GLM 4.6V?
GLM 4.6V can hallucinate facts, misread small or low-resolution visual details, and should not be used for safety-critical or legal decisions.
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Does GLM 4.6V support streaming responses on LLM.API?
Yes, GLM 4.6V supports server-sent events streaming on LLM.API, allowing tokens to be consumed as they are generated.
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