Zhipu AI का GLM-4.5 ओपन-वेट्स चेन-ऑफ-थॉट रीजनिंग और उच्च एल्गोरिदमिक दक्षता के लिए निर्मित एक उन्नत AI मॉडल है, जो 131K कॉन्टेक्स्ट विंडो, 75 टोकन/सेकंड स्पीड और निःशुल्क ओपन-वेट्स एक्सेस के साथ 80/100 इंटेलीजेंस स्कोर प्रदान करता है।
Technical Specifications
- Provider
- Zhipu AI
- Category
- open-source
- Release Date
- 2025-07
- Batch API Discount
- Standard rate
- Consumer Subscription
- Free / Self-Hosted
- Reasoning Effort Control
- Configurable (Low / Med / High)
- 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-4-5
- 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)
- Weights Precision & Quantization
- BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0)
- Minimum GPU VRAM Requirement
- 81 GB VRAM (4-bit Quantized) · 280 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-4.5 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-4.5:
Benchmark Scores & 6-Axis Capability Radar
- SWE-bench Verified
- 42%
- GPQA Diamond
- 61%
- MMLU-Pro
- 75%
- HumanEval
- 86%
- Chatbot Arena Elo
- 1290pts
Strengths
- 131K कॉन्टेक्स्ट विंडो और अधिकतम 8K आउटपुट टोकन क्षमता (SWE-bench Verified 42% · GPQA Diamond 61%)
- 75 टोकन/सेकंड की तेज़ जनरेशन स्पीड और मात्र 550ms का प्रथम टोकन लेटेंसी समय (TTFT)
- ओपन-वेट्स आर्किटेक्चर (130B MoE) — vLLM, Ollama या निजी GPU क्लस्टर पर सेल्फ-होस्टिंग के लिए उपयुक्त
Limitations & Trade-offs
- पूर्ण प्रिसिजन में लोकल GPU सर्वर पर चलाने के लिए पर्याप्त VRAM और हार्डवेयर संसाधनों की आवश्यकता होती है
- अत्यंत जटिल वैज्ञानिक शोध कार्यों में शीर्ष फ्लैगशिप रीजनिंग मॉडल की तुलना में गहराई थोड़ी कम हो सकती है
Editorial Assessment
80/100 इंटेलीजेंस इंडेक्स, 131K कॉन्टेक्स्ट और 75 टोकन/सेकंड की गति के साथ GLM-4.5 (Zhipu AI) अपनी श्रेणी में एक अत्यंत भरोसेमंद और मूल्यवान विकल्प है।
Best for: ओपन-वेट्स चेन-ऑफ-थॉट रीजनिंग और उच्च एल्गोरिदमिक दक्षता, एंटरप्राइज RAG सिस्टम और स्केलेबल API इंटीग्रेशन के लिए सर्वोत्तम।
Compare GLM-4.5
Frequently Asked Questions about GLM-4.5
How much does the GLM-4.5 API cost per 1M tokens?+
GLM-4.5 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-4.5 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-4.5's context window?+
GLM-4.5 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-4.5 locally on my own GPU (Open Weights)?+
Yes, GLM-4.5 (130B MoE) 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-4.5 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-4.5 perform on coding and reasoning benchmarks?+
GLM-4.5 achieves a composite Intelligence Index of 80/100, scoring 42% on SWE-bench Verified, 61% on GPQA Diamond, 75% on MMLU-Pro, and 1290 pts on Chatbot Arena Elo.
What is GLM-4.5 best used for?+
GLM-4.5 is best suited for: ओपन-वेट्स चेन-ऑफ-थॉट रीजनिंग और उच्च एल्गोरिदमिक दक्षता, एंटरप्राइज RAG सिस्टम और स्केलेबल API इंटीग्रेशन के लिए सर्वोत्तम।. Assessment: 80/100 इंटेलीजेंस इंडेक्स, 131K कॉन्टेक्स्ट और 75 टोकन/सेकंड की गति के साथ GLM-4.5 (Zhipu AI) अपनी श्रेणी में एक अत्यंत भरोसेमंद और मूल्यवान विकल्प है।
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-4.5:
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.