Technical Specifications of GLM-5.1
| Specification | Details |
|---|---|
| Developer | Z.ai (Zhipu AI) |
| Model Version | GLM-5.1 (post-training refinement of GLM-5) |
| Architecture | Mixture-of-Experts (MoE); ~744–754 billion total parameters, ~40 billion active per token; incorporates Multi-head Latent Attention and DeepSeek Sparse Attention for long-context efficiency |
| Context Length | 200K–203K tokens (up to 202,752–204.8K in some configurations) |
| Maximum Output Tokens | 128K tokens |
| Modalities | Text-only (input/output); no native vision or audio support |
| Key Capabilities | Thinking modes, streaming output, function calling/tool use (MCP integration), context caching, structured JSON output |
| License | MIT (fully open-source weights) |
| Deployment Options | Official API, local inference (vLLM, SGLang), Hugging Face / ModelScope |
| Training Hardware | Huawei Ascend chips (no Nvidia dependency) |
What is GLM-5.1
GLM-5.1 is Z.ai’s frontier-class language model optimized for long-horizon autonomous tasks. Unlike traditional LLMs that excel at short, single-turn interactions, it is engineered for sustained execution loops—planning, coding, testing, benchmarking, debugging, and iterative optimization—over extended periods without human intervention.
Key Features of GLM-5.1
1. Long-Horizon Autonomous Work
8-Hour Sustained Execution: GLM-5.1 is Z.AI’s latest flagship model for long-horizon tasks, and the official docs say it can work continuously and autonomously on a single task for up to 8 hours. It is positioned to handle the full loop from planning and execution to iterative optimization and final delivery.
Closed-Loop Optimization: A core feature of GLM-5.1 is its ability to keep iterating through an “experiment → analyze → optimize” cycle, rather than stopping at one-shot output. Z.AI describes this as a major step toward autonomous engineering and long-horizon coding agents.
2. Strong Coding and Reasoning Ability
Broad Capability Balance: GLM-5.1 is broadly aligned with Claude Opus 4.6 in general capability and coding performance, and that it shows a balanced profile across reasoning, coding, agents, tool use, and browsing benchmarks.
Advanced Engineering Workflows: GLM-5.1 is designed for real-world development workflows, including complex engineering optimization, debugging, and production-grade delivery. Z.AI positions it as a foundation for autonomous agents and long-horizon coding agents.
3. Better Support for Complex Tasks
Larger Context and Output: The migration guide lists GLM-5.1’s maximum context length as 200K and maximum output as 128K, which makes it more suitable for large tasks and extended sessions.
Deep Thinking and Tool Streaming: GLM-5.1 supports deep thinking mode, and Z.AI also adds streaming output during tool calls with tool_stream=true, which helps expose tool-call parameters in real time.
4. Built for Agentic Engineering
From Code Generation to Autonomous Delivery: Z.AI’s positioning for GLM-5.1 is not just “generate code,” but “deliver engineering work.” The docs describe it as a new-generation flagship model for “Agentic Engineering,” emphasizing planning, execution, optimization, and delivery in one workflow.
Stronger Stability Over Long Tasks: The release notes say GLM-5.1 improves stability, consistency, and tool use over extended tasks, supported by multi-turn SFT, RL, and process-quality evaluation.
GLM-5.1 vs Other Models
GLM-5.1 stands out as one of the strongest open-source options and a direct competitor to closed frontier models in coding and agentic scenarios:
- vs. Claude Opus 4.6: ~94–100% of coding performance on SWE-Bench Pro (58.4 vs. 57.3); superior long-horizon autonomy and lower cost via open weights/aggregators.
- vs. GPT-5.4: Outperforms on SWE-Bench Pro (58.4 vs. 57.7); competitive or slightly behind in some pure reasoning tasks.
- vs. GLM-5 (predecessor): 28% coding uplift and dramatically better sustained execution.
- vs. Llama 3.1 / Qwen / DeepSeek: Stronger agentic and long-horizon results; open MIT license provides greater customization freedom than many alternatives.
Its primary advantages are open-source accessibility, cost efficiency at scale, and specialized optimization for real-world engineering agents.
Use Cases
GLM-5.1 excels wherever long-running, iterative intelligence is required:
- Autonomous Software Engineering: Full-stack feature development, code migration, large-scale refactoring, and end-to-end testing with minimal oversight.
- Performance Optimization: Kernel-level improvements, database tuning, and multi-iteration benchmarking (e.g., 6.9× vector query speedup).
- Agentic Workflows: Integration into coding agents (Claude Code, OpenClaw) for repository-scale tasks or complex system building.
- Enterprise Productivity: Long-document analysis, report generation, and structured office artifacts.
- Research & Prototyping: Rapid iteration on ambiguous problems requiring hundreds of self-correcting steps.
How to Access GLM-5.1 via CometAPI
CometAPI, a unified AI model aggregator, provides immediate, OpenAI-compatible access to GLM-5.1 (and GLM-5) alongside 500+ other models. Developers simply sign up at cometapi.com, obtain an API key, and route requests to the GLM-5.1 endpoint(glm-5.1) using standard OpenAI SDKs or Chat Completions. No infrastructure setup is required—CometAPI handles inference routing, load balancing, and failover.
Current CometAPI Pricing (approximate, as of mid-April 2026):
- Input: $0.8 per million tokens
- Output: $3.2 per million tokens
This is significantly lower than Z.ai’s direct rates (~$1.4 / $4.4) and a fraction of equivalent Western frontier models.
