GLM-4.7-Flash di Zhipu AI è un modello di intelligenza artificiale con finestra di contesto da 200K token e tariffazione API di $0.015/1M token in input e $0.03/1M in output, ideale per coding, ragionamento e automazione aziendale.
Technical Specifications
- Provider
- Zhipu AI
- Category
- lightweight
- Release Date
- 2026-01
- Batch API Discount
- 50% off async batch
- 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-4-7-flash
- Supported SDK & API Protocol
- OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
- Lifecycle & Deprecation Status
- Active GA (Guaranteed support through 2028+)
- 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)
- Flat rate ($0.015/1M in) across full 200K window
- Prompt Cache Write & TTL Policy
- Cached input at $0.002/1M tokens
- 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
- 43 GB VRAM (4-bit Quantized) · 151 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-4.7-Flash Cost, Context, Hardware & API Integration
Pay-as-you-go API is cheaper for this volume (saves $19.04/mo vs $20 flat plan).
Break-even point: ~10,417 requests/day equals $20/month.
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-4.7-Flash:
Benchmark Scores & 6-Axis Capability Radar
- MMLU-Pro
- 72%
- HumanEval
- 83%
- Chatbot Arena Elo
- 1270pts
Strengths
- Architettura Optimized High-Speed Lightweight Transformer avanzata di Zhipu AI con supporto fino a 200K token di contesto
- Ottimo rapporto qualità-prezzo ($0.015/1M input, $0.03/1M output) e bassa latenza
- Elevate prestazioni nei benchmark di programmazione, logica e comprensione multilingue
Limitations & Trade-offs
- L'utilizzo intensivo di contesti molto ampi richiede un attento monitoraggio dei costi dei token
- Per casi d'uso verticali complessi è consigliabile un prompt engineering mirato
Editorial Assessment
GLM-4.7-Flash di Zhipu AI offre una combinazione eccellente di precisione, velocità ed efficienza dei costi API.
Best for: Automazione enterprise, sviluppo software, agenti IA autonomi e pipeline di produzione Zhipu AI
Compare GLM-4.7-Flash
Frequently Asked Questions about GLM-4.7-Flash
How much does the GLM-4.7-Flash API cost per 1M tokens?+
GLM-4.7-Flash by Zhipu AI is priced at $0.015 per 1M input tokens and $0.03 per 1M output tokens. With prompt caching enabled, cached input tokens cost $0.002 per 1M tokens, and asynchronous Batch API requests receive a 50% discount.
Should I use a $20/month subscription or the GLM-4.7-Flash 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-4.7-Flash's context window?+
GLM-4.7-Flash features a 200K tokens context window, which equals approximately 150,000 English words or ~400 standard single-spaced pages in a single prompt. Its maximum output generation per turn is 8,192 tokens.
Can I run GLM-4.7-Flash locally on my own GPU (Open Weights)?+
Yes, GLM-4.7-Flash (Confidential) 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-4.7-Flash 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-4.7-Flash perform on coding and reasoning benchmarks?+
GLM-4.7-Flash achieves a composite Intelligence Index of 74/100, scoring N/A% on SWE-bench Verified, N/A% on GPQA Diamond, 72% on MMLU-Pro, and 1270 pts on Chatbot Arena Elo.
What is GLM-4.7-Flash best used for?+
GLM-4.7-Flash is best suited for: Automazione enterprise, sviluppo software, agenti IA autonomi e pipeline di produzione Zhipu AI. Assessment: GLM-4.7-Flash di Zhipu AI offre una combinazione eccellente di precisione, velocità ed efficienza dei costi API.
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-4.7-Flash:
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.
Fonti ufficiali: Zhipu AI API Platform Documentation↗