Overview
GLM-5 is Z.ai's flagship open-source model designed for complex systems engineering and long-horizon agentic tasks. Released in February 2026, it scales from GLM-4.5's 355B to 744B total parameters with 40B active parameters, integrating DeepSeek Sparse Attention for improved efficiency. The model targets professional developers working on multi-file codebases, document generation workflows, and autonomous agent systems requiring sustained reasoning across hundreds of turns. According to third-party benchmarks, GLM-5 achieves an Intelligence Index score of 50 (up 8 points from GLM-4.7) and demonstrates significantly reduced hallucination rates, delivering enhanced reliability for enterprise applications.
What's New
Scaled Architecture with Sparse Attention
Compared to GLM-4.5 (355B total with 32B active), GLM-5 scales to 744B parameters while maintaining efficient inference through 40B active parameters. The integration of DeepSeek Sparse Attention reduces deployment costs while preserving long-context capacity up to 200K tokens. On tasks requiring deep context aggregation (1,200+ tokens), GLM-5 runs 150ms faster than GLM-4.7 despite the larger architecture. The model uses ~110M output tokens on the Intelligence Index benchmark compared to GLM-4.7's ~170M—a 35% reduction demonstrating improved efficiency.
Enhanced Reasoning and Agentic Performance
According to Artificial Analysis (February 2026), the model achieves an Intelligence Index score of 50, an 8-point improvement over GLM-4.7's 42. On the Agentic Index, GLM-5 scores 63 with a GDPval-AA ELO of 1,412, ranking third overall among evaluated models. Key benchmark improvements include gains on SWE-bench (code understanding), AIME (multi-step reasoning), and BrowseComp (web browsing with chained queries). The model excels at long-horizon tasks requiring sustained planning and tool use, outperforming GLM-4.7 by significant margins on CC-Bench-V2 across frontend, backend, and agentic coding scenarios.
Record-Low Hallucination Rate
GLM-5 achieves a score of -1 on the AA-Omniscience Index, a 35-point improvement over GLM-4.7. This represents the lowest hallucination rate among tested models, driven by improved abstention behavior—the model refuses to answer when lacking sufficient information rather than fabricating responses. The improvement stems from enhanced RL infrastructure (slime) enabling more fine-grained post-training iterations and better alignment with factual accuracy requirements.
Built-in Document Generation
GLM-5 introduces native document creation capabilities, generating production-ready .docx, .pdf, and .xlsx files directly from text prompts. The official Z.ai application now includes an Agent mode with built-in skills for PDF/Word/Excel creation, supporting multi-turn collaboration to refine outputs. Users can generate PRDs, lesson plans, financial reports, spreadsheets, and presentation materials without switching between tools. This functionality leverages GLM-5's improved long-context understanding and structured output generation.
Asynchronous RL Infrastructure (slime)
The model's post-training phase utilizes slime, a novel asynchronous RL infrastructure that substantially improves training throughput and efficiency. Unlike traditional synchronous RL, slime decouples rollout engines from training engines, transforming data generation into a parallelized, non-blocking process. This architecture enables more fine-grained iterations on complex agentic tasks where data generation can be slow, with accelerated rollouts using mixed-precision inference (FP8 for generation, BF16 for training).
Pricing & Plans
GLM-5 is available through multiple access methods with flexible pricing:
Free Tier (Z.ai Chat)
- Access via Z.ai web interface with model selection
- Chat mode for instant responses
- Agent mode with document generation skills
- Rate limits apply to free usage
GLM Coding Plan
- Subscription starting from $3/month (pricing subject to change; check official subscription page for current rates)
- Integration with Claude Code, Kilo Code, Roo Code, Cline, and other coding agents
- Higher usage quotas compared to free tier
- Gradual rollout: Max plan users have immediate access, other tiers added progressively
- GLM-5 requests consume more quota than GLM-4.7
- Note: Z.ai has announced pricing adjustments due to increased demand
API Access (Z.ai API & BigModel.cn)
- OpenAI-compatible API endpoints
- Pay-per-token pricing (contact for enterprise rates)
- Compatible with Claude Code and OpenClaw frameworks
- Supports local deployment via vLLM and SGLang
Open-Source Deployment
- Model weights available on HuggingFace and ModelScope
- MIT License for commercial use
- Supports non-NVIDIA chips (Huawei Ascend, Moore Threads, Cambricon, Kunlun Chip, MetaX, Enflame, Hygon)
- Hardware cost depends on deployment scale (minimum 40B active parameters)
Pros & Cons
Pros
- Massive scale: 744B parameters with efficient 40B active inference deliver superior reasoning on complex tasks
- Record accuracy: 35-point hallucination reduction and -1 AA-Omniscience score make it most reliable for factual generation
- Agentic excellence: Third-ranked on Agentic Index (63, per Artificial Analysis) with proven performance on long-horizon workflows and tool use
- Document automation: Native .docx/.pdf/.xlsx generation eliminates external conversion tools
- Open-source: MIT License with full weights available for commercial deployment
- Hardware flexibility: Runs on non-NVIDIA chips with kernel optimization and quantization support
Cons
- Latency overhead: Adds 30ms on shorter requests (50-300 tokens) compared to GLM-4.7
- Compute requirements: 40B active parameters demand substantial VRAM for local deployment
- Quota consumption: GLM-5 requests use more Coding Plan quota than previous versions
- Gradual rollout: Limited availability during initial launch period (Max plan priority)
- Learning curve: Advanced features like Agent mode and document skills require workflow adaptation
Best For
- Systems engineers building multi-component applications requiring cross-file refactoring and integration testing
- AI researchers developing autonomous agents with long-horizon planning and tool use capabilities
- Enterprise developers needing factual accuracy and low hallucination rates for production deployments
- Document automation teams generating reports, proposals, and spreadsheets from structured data
- Open-source advocates seeking Claude Opus 4.5
- level performance with MIT licensing
- Infrastructure teams deploying on non-NVIDIA hardware (Huawei Ascend, domestic chips)
FAQ
How does GLM-5 performance compare to GLM-4.7 on short vs long contexts?
GLM-5 adds modest latency on shorter requests (30ms overhead at 50-300 tokens) but becomes faster on longer contexts (150ms faster at 1,200 tokens). The most dramatic improvements appear on complex tasks requiring deep context aggregation, while short, well-formed prompts show minimal differences. For typical coding workflows with multi-file context, GLM-5's efficiency gains outweigh the short-prompt overhead.
Can I use GLM-5 with existing coding agent frameworks?
Yes, GLM-5 works with Claude Code, Kilo Code, Roo Code, Cline, and other frameworks supporting OpenAI-compatible APIs. Update your config file (e.g., ~/.claude/settings.json) to set the model name to "GLM-5". GLM Coding Plan subscribers can use it immediately (Max plan) or during gradual rollout (other tiers). The model supports preserved thinking mode and turn-level thinking control for complex tasks.
What are the hardware requirements for local deployment?
GLM-5 requires sufficient VRAM to load 40B active parameters. Hardware requirements vary based on precision, parallelization strategy, and quantization—typically requiring multi-GPU setups or high-memory servers. The model supports vLLM and SGLang inference frameworks with optimizations for non-NVIDIA chips. Quantization options (INT4/INT8) can reduce memory requirements but may impact performance. Consult the official GitHub repository and deployment documentation for detailed hardware specifications.
How does the document generation feature work?
GLM-5's document generation operates through Z.ai's Agent mode, which includes built-in skills for PDF, Word, and Excel creation. Provide a text prompt describing the desired document (e.g., "Create a project proposal for a mobile app with budget breakdown"), and the model generates a production-ready file. Multi-turn collaboration allows refinement—request layout changes, add sections, or adjust formatting. The feature leverages GLM-5's long-context understanding and structured output capabilities.
Is GLM-5 suitable for production enterprise applications?
Yes, with qualifications. The record-low hallucination rate (-1 AA-Omniscience score) and 35-point improvement over GLM-4.7 make it highly reliable for factual generation. Open-source MIT licensing eliminates vendor lock-in concerns. However, evaluate latency requirements—short-prompt overhead may impact real-time chat applications. The model excels at complex reasoning, code generation, and document automation where accuracy and context understanding outweigh sub-second response times. Run domain-specific benchmarks before production deployment.
