Technical specifications of GLM-5
| Item | GLM-5 (reported) |
|---|---|
| Model family | GLM (Z.ai / Zhipu AI) — flagship generation |
| Architecture | Mixture-of-Experts (MoE) + sparse attention (DeepSeek/DSA optimizations). |
| Total parameters | ≈744–745B (MoE pool). |
| Active / routed params (per token) | ~40–44B active (depends on routing/experts). |
| Pre-training tokens | ~28.5T tokens (reported). |
| Context window (input) | Up to 200,000 tokens (long-context mode). |
| Max output tokens | 128,000 tokens (max generation per call reported). |
| Input modalities | Text only (primary); engineered for rich text → outputs (doc/xlsx generation via tools). |
What is GLM-5
GLM-5 is Zhipu AI’s next-generation foundation model that scales the GLM line with an MoE routing design and sparse attention optimizations to deliver long-context reasoning and agentic workflows (multi-step planning, code & system orchestration). It’s explicitly positioned to be an open-weights contender for agentic and engineering tasks, with enterprise accessibility via APIs and self-hosting.
🚀 Main Features of GLM-5
1. Agentic Intelligence & Reasoning
GLM-5 is optimized for workflows where the model breaks down long, complex tasks into ordered steps with reduced hallucination — a major improvement over prior GLM versions. It leads certain open weights model benchmarks on knowledge reliability and task productivity.
2. Long Context Support
With a 200K token context window, GLM-5 can sustain very long conversations, large documents, and extended reasoning chains without losing coherence — an increasingly critical capability for real-world professional applications.
3. DeepSeek Sparse Attention
By integrating a sparse attention mechanism, GLM-5 efficiently scales its memory footprint, allowing longer sequences without linear cost increases.
4. Tool Integration & Output Formats
Native support for structured outputs and external tool integrations (JSON, API calls, dynamic tool use) makes GLM-5 practical for enterprise applications like spreadsheets, reports, and automated coding assistants.
5. Cost Efficiency
GLM-5 is positioned as cost-competitive compared to proprietary counterparts, with input/output pricing substantially lower than major offerings, making it attractive for large-scale deployment.
Benchmark Performance of GLM-5
Multiple independent evaluations and early industry benchmarks show GLM-5 performing strongly among open-weight models:
- It achieved record-low hallucination rates on the Artificial Analysis Intelligence Index — a measure of reliability and truthfulness — outperforming prior models by a wide margin.
- Agent-centric benchmarks indicate substantial gains in complex task execution compared to GLM-4.7 and other open models.
- Cost-to-performance metrics position GLM-5 as 4th quartile for speed but top tier (best) on intelligence and price among open-weight models.
Quantitative Scores (Example from ranking platform):
- Intelligence Index: #1 among open weights models.
- Pricing Efficiency: High ratings for low input/output costs.
How to access and use GLM-5 API
Step 1: Sign Up for API Key
Log in to cometapi.com. If you are not our user yet, please register first. Sign into your CometAPI console. Get the access credential API key of the interface. Click “Add Token” at the API token in the personal center, get the token key: sk-xxxxx and submit.
Step 2: Send Requests to glm-5 API
Select the “glm-5” endpoint to send the API request and set the request body. The request method and request body are obtained from our website API doc. Our website also provides Apifox test for your convenience. Replace <YOUR_API_KEY> with your actual CometAPI key from your account. Where to call it: Chat format.
Insert your question or request into the content field—this is what the model will respond to . Process the API response to get the generated answer.
Step 3: Retrieve and Verify Results
Process the API response to get the generated answer. After processing, the API responds with the task status and output data.