Zhipu AI の GLM-4.6V は、テキストと画像のネイティブ・マルチモーダル理解および図表・ドキュメント解析向けに設計されたAIモデルです。131K のコンテキストウィンドウ、毎秒 80 トークンの生成速度、知能指数 77/100（無料のオープンウェイト提供）を実現しています。
Technical Specifications
- Provider
- Zhipu AI
- Category
- open-source
- Release Date
- 2025-12
- Batch API Discount
- Standard rate
- Consumer Subscription
- Free / Self-Hosted
- Reasoning Effort Control
- Fixed
- Structured Outputs
- Yes
- Output Modalities
- text
- Official Documentation
- Visit Official Docs ↗
Data Privacy, Licensing & Deployment Governance
Official Provider Release Intelligence, API Quotas & Agentic Specs
- Official API Model ID (SDK String)
- glm-4-6v
- Supported SDK & API Protocol
- OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
- Lifecycle & Deprecation Status
- Active GA (Guaranteed support through 2027+)
- Production Rate Limits (RPM & TPM)
- Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
- Long-Context Pricing Tier (>128K / >200K)
- Free across full context window (only KV-cache VRAM scales)
- Prompt Cache Write & TTL Policy
- Automatic prefix KV-caching in vLLM / SGLang (0 cost)
- Computer Use & Browser GUI Agent
- API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)
- MCP & Parallel Tool Calling
- Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
- Web Search Grounding & Code Sandbox
- Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools
- Fine-Tuning & Distillation Support
- Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
- Multimodal Tokenization Billing
- Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
- Weights Precision & Quantization
- BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0)
- Minimum GPU VRAM Requirement
- 15 GB VRAM (4-bit Quantized) · 52 GB VRAM (Native BF16)
- Supported Inference Engines
- vLLM, SGLang, Ollama, llama.cpp, TensorRT-LLM, HuggingFace TGI
- System Card & Safety Framework
- Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
- Enterprise Compliance (SOC2 / HIPAA / ZDR)
- 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
GLM-4.6V Cost, Context, Hardware & API Integration
Open-weights or free-tier model: $0 API license cost when self-hosted.
Same Monthly Workload Cost Across Popular Reference Models
Live Prompt Paste Tokenizer & Cost Counter
Paste real system instructions, JSON schemas, or code to measure BPE token counts across tokenizers.
Hybrid Prompt Routing Cascade Simulator
Route routine queries to a fast lightweight model ($0.15/1M) and escalate only complex reasoning turns to GLM-4.6V:
Benchmark Scores & 6-Axis Capability Radar
- MMLU-Pro
- 74%
- HumanEval
- 82%
- Chatbot Arena Elo
- 1280pts
Strengths
- 131K のコンテキストウィンドウと最大 8K トークンの出力容量（MMLU-Pro 74% · Arena Elo 1280）
- 毎秒 80 トークンの高速スループットと、初回トークン到達時間（TTFT）わずか 510ms の低遅延
- オープンウェイトモデル (24B VLM)として vLLM・SGLang・Ollama を用いた自社GPUでのローカル運用が可能
Limitations & Trade-offs
- 非量子化のフル精度でローカル推論を行うには十分なGPU VRAM容量が必要です
- 極めて高度な専門科学の証明問題では、上位の大型推論特化モデルの方がより深い洞察を提供する場合があります
Editorial Assessment
知能指数 77/100、131K コンテキスト、毎秒 80 トークンの速度を兼ね備えた GLM-4.6V は、Zhipu AI のラインナップにおいて非常に実用性の高い主力モデルです。
Best for: テキストと画像のネイティブ・マルチモーダル理解および図表・ドキュメント解析、エンタープライズRAG基盤、スケーラブルな本番API統合に最適。
Compare GLM-4.6V
Frequently Asked Questions about GLM-4.6V
How much does the GLM-4.6V API cost per 1M tokens?+
GLM-4.6V by Zhipu AI is priced at $0 per 1M input tokens and $0 per 1M output tokens. With prompt caching enabled, cached input tokens cost $0 per 1M tokens, and asynchronous Batch API requests receive a 0% discount.
Should I use a $20/month subscription or the GLM-4.6V pay-as-you-go API?+
A flat $20/month consumer subscription is predictable for daily interactive chat and manual coding. However, if you are building software, running automated workflows, or sending low-to-moderate volume with prompt caching enabled, the pay-as-you-go API is typically far more cost-effective and offers higher rate limits without manual UI caps.
How many words and pages fit in GLM-4.6V's context window?+
GLM-4.6V features a 131K tokens context window, which equals approximately 98,304 English words or ~262 standard single-spaced pages in a single prompt. Its maximum output generation per turn is 8,192 tokens.
Can I run GLM-4.6V locally on my own GPU (Open Weights)?+
Yes, GLM-4.6V (24B VLM) releases open weights that can be self-hosted locally or on private cloud GPUs using vLLM, Ollama, SGLang, or llama.cpp.
Does Zhipu AI train on data sent to the GLM-4.6V API, and is commercial use allowed?+
100% Isolated when self-hosted on private GPUs or VPC. License tier: Open-Weights Research & Commercial License. Full commercial ownership of generated outputs. Supported deployment channels include: Self-Hosted GPU (vLLM / Ollama), Private VPC, or Managed Serverless API.
How does GLM-4.6V perform on coding and reasoning benchmarks?+
GLM-4.6V achieves a composite Intelligence Index of 77/100, scoring N/A% on SWE-bench Verified, N/A% on GPQA Diamond, 74% on MMLU-Pro, and 1280 pts on Chatbot Arena Elo.
What is GLM-4.6V best used for?+
GLM-4.6V is best suited for: テキストと画像のネイティブ・マルチモーダル理解および図表・ドキュメント解析、エンタープライズRAG基盤、スケーラブルな本番API統合に最適。. Assessment: 知能指数 77/100、131K コンテキスト、毎秒 80 トークンの速度を兼ね備えた GLM-4.6V は、Zhipu AI のラインナップにおいて非常に実用性の高い主力モデルです。
Zhipu AI Family Lineage, Routing Tiers & Upgrade Path
Step down to a lighter sibling for high-volume routing or step up to Zhipu AI's flagship reasoning tier:
GLM-5.2
GLM-5.1
GLM-5V-Turbo
GLM-5-Turbo
GLM-5
GLM-4.7-FlashX
AI Glossary & Architecture Concepts
View full glossary →Key machine learning concepts and benchmarks relevant to GLM-4.6V:
The basic unit of text that a language model processes, typically representing about 3 to 4 English characters or roughly 0.75 words. For example, the sentence "Artificial intelligence is fast" breaks down into about 5 tokens.
A standardized evaluation suite used to objectively measure and rank an AI model's capabilities across specific tasks — such as SWE-bench Verified for software engineering, AIME for competitive math, or MMLU for general knowledge.
The maximum number of tokens a model can consider at once — both the input it reads and the output it writes. A 1,000,000-token context window can hold roughly 750,000 English words in a single request.
An AI model trained via reinforcement learning to spend dynamic compute thinking through complex logic, math, and architectural problems before outputting a solution. Examples include OpenAI o1, o3, and DeepSeek R1.
A step-by-step reasoning technique where an AI model generates intermediate problem-solving thoughts before arriving at a final answer. Native reasoning models like OpenAI o3 and DeepSeek R1 use CoT to solve complex math and coding challenges.
The ability of a language model to recognize when an external capability is needed and output structured JSON arguments to invoke external APIs, databases, or terminal commands. The runtime executes the tool and passes results back to the model.
Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
公的ソース: Zhipu AI Open Source Hub↗