GLM-5 Series Models
Open-source flagship models for long-horizon agentic engineering, with solid 1M context and flexible reasoning effort.
- Source repo
- zai-org/GLM-5
- Stars
- ★ 7.4k
- Last updated
- 4d ago
- License
- Apache-2.0
- FA score
- 26/100 · Major gaps
At a glance
- How it runs
- Works with
- Platform-specific
- Cost
- Free, no paid service needed
- Setup effort
- High · needs real infrastructure
- You'll need
- Typical use
- A software engineer generating or fixing code in a large repository using GLM-5.2's 1M context to maintain context over many files.
- Not a fit if
- Individual developers without large GPU clusters to self-host a 744B model
- Users wanting a turnkey experience without configuring SGLang or vLLM
- Teams that cannot download weights from Hugging Face or ModelScope
- Source review
- 26/100 · Major gaps 8 safety controls not found
What does this agent do, and when should you use it?
The GLM-5 series from Z.ai includes GLM-5.2, GLM-5.1, and GLM-5, all built on a 744B-parameter MoE architecture (40B active) with sparse attention and multi-token prediction, available in BF16 and FP8. The series targets long-horizon tasks such as software engineering, terminal operations, and business simulation, with GLM-5.2 delivering a 1M-token context for stable extended work. Models are downloadable from Hugging Face and ModelScope, and supported by inference frameworks like SGLang, vLLM, Transformers, KTransformers, and Unsloth, plus Ascend NPU. Fine-tuning is supported via Slime (RL) and ms-swift (SFT/PPO/GRPO). Reasoning effort can be adjusted via the reasoning_effort parameter.
GLM-5 models process natural language and code, generating code, handling repo-level tasks (e.g., NL2Repo), executing terminal commands (Terminal-Bench), and simulating long-term operations (Vending Bench). They use advanced RL training (via Slime) and support fine-tuning with ms-swift. At inference, users deploy with SGLang/vLLM and control thinking via reasoning_effort (max/high) or disable it with enable_thinking=false. The models output text through standard APIs, with BF16 and FP8 precision options.
- A software engineer generating or fixing code in a large repository using GLM-5.2's 1M context to maintain context over many files.
- A DevOps engineer using GLM-5.1 to automate multi-step terminal operations, as in Terminal-Bench tasks.
- A researcher benchmarking open-source models on coding tasks like SWE-bench Pro, relying on GLM-5.2's strong performance.
- An entrepreneur simulating a year-long vending machine business in Vending Bench 2 with GLM-5, testing planning and resource management.
- A team deploying GLM-5.2 with vLLM for internal agentic applications, leveraging the documented recipes.
How do you install or deploy this agent?
Download model weights from Hugging Face (e.g., zai-org/GLM-5.2) or ModelScope (ZhipuAI/GLM-5.2), choosing BF16 or FP8. Install a supported inference framework: SGLang (≥0.5.13.post1), vLLM (≥0.23.0), or Transformers (≥0.5.12), and follow the framework-specific setup guides (e.g., SGLang cookbook). For Ascend NPU, see the example/ascend.md file.
How do you use this agent?
Load the model with SGLang or vLLM and send requests via API or CLI. Set reasoning_effort to 'max' (default) or 'high' to adjust thinking budget, or enable_thinking=false to disable thinking. Refer to the framework documentation for code examples, such as the vLLM recipes page.
What are this agent's strengths and limitations?
- State-of-the-art open-source performance on SWE-bench Pro and Terminal-Bench 2.1, close to Claude Opus 4.8.
- Solid 1M-token context supports long-horizon tasks like large-scale code repair.
- Multiple inference framework options and precision types (BF16/FP8) provide deployment flexibility.
- Huge model size (744B) requires substantial GPU memory for local deployment, increasing infrastructure costs.
- Dependency on specific framework versions (e.g., SGLang v0.5.13.post1, vLLM v0.23.0) may require upgrades and compatibility checks.
- Reasoning effort control is limited to two levels, offering less granularity than other systems.
How does this agent compare with similar options?
Outperforms Gemini 3.1 Pro on Terminal-Bench 2.1 (81.0 vs. 80.0) and nearly matches Claude Opus 4.8 (85.0). On Vending Bench 2, it ranks #1 among open-source models, approaching Claude Opus 4.5.
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| GLM-5 Series Models This agent | 26 · Major gaps | Self-hosted serviceFree | ★ 7.4k | 4d ago | — | — |
| Autoprompt Skill | 69 · Some gaps | CLIFree + model costs | ★ 1.2k | 15d ago | JavaScript | Codex · Claude Code |
| little-coder: Lightweight Local Coding Agent | 58 · Major gaps | CLIFree + model costs | ★ 2.7k | 1mo ago | TypeScript | — |
| Meta-Harness | 44 · Major gaps | CLIFree + model costs | ★ 1.7k | 17d ago | Python | Claude Code |
How does FollowAgents rate this agent?
Why each dimension lost points
The repository contains only model weights and references to inference/fine-tuning frameworks, with no documentation on least privilege, user confirmation, data flow transparency, sensitive data handling, dependency security, external effects, rollback, or source attribution. Hence all trust criteria score 0.
Self-consistency: README descriptions of model versions and capabilities are mostly consistent, but lack code and tests to verify, so score 1. Dependency availability: lists external frameworks like SGLang and vLLM, but no version pinning or verification, score 1. Failure messages: no documentation of error handling or failure messages, score 0.
Audience and scenarios: clearly targets developers and researchers, describes multiple use cases, score 2. Capability boundaries: mentions reasoning effort control and deployment frameworks, but lacks detailed limitations, score 1. Trigger precision: no trigger mechanisms or parameter details, score 0. Environment fit: lists multiple deployment frameworks and platforms, score 2.
Information architecture: README is well-structured with sections for introduction, download, deployment, fine-tuning, etc., score 2. Install notes: provides dependency list and framework versions, but lacks detailed installation steps, score 1. Naming stability: model naming is consistent, but no API stability mentioned, score 1. Examples and FAQ: provides links but no concrete examples or FAQ, score 1. Known limitations: none mentioned, score 0. License: Apache-2.0 license is complete, score 2. Versioning and changelog: no version history or changelog, score 0. Maintenance responsibility: no clear maintainer or update policy, score 1.
Output usability: provides model download and deployment guides, but no output format or usage examples, score 1. Marginal value: claims significant performance improvements, but no independent verification, score 2. Cost-benefit: no cost or resource requirements information, score 1.
Claim traceability: provides technical report and blog links, but no specific data or reproduction steps, score 1. Cross-source corroboration: cites external benchmarks, but no independent verification, score 1. Fact-inference separation: README mixes facts and inferences without clear distinction, score 1.
- Not found in source: least-privilege scopingGrant only what the task needs: a dedicated account or read-only token, scoped to specific directories and repos.
- Not found in source: confirmation before actingTurn on (or add) a confirmation step before it acts, and try it in a sandbox or test environment before real data.
- Not found in source: data-flow disclosureWatch which external services it contacts (proxy or firewall logs) and keep sensitive data out until you know where it goes.
- Not found in source: sensitive-data handlingUse dedicated, low-privilege, revocable API keys — never production credentials — and keep secrets out of logs.
- Not found in source: dependency securityPin versions and run a dependency audit (npm audit, pip-audit) before installing; prefer running it in a container.
- Not found in source: disclosed external effectsEstablish which external systems it writes to, sends to or changes, and verify with test accounts or repos before production.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- Not found in source: verifiable attributionInstall from the official repo or registry and check the publisher and URL to avoid look-alike packages.
- The repository contains only model weights and documentation, with no code or tests, making it impossible to verify security and reliability.
- No information on permissions, data flow, sensitive data handling, or dependency security, posing potential risks.
- No version history or changelog, making it difficult to track updates and compatibility.
- No known limitations mentioned, potentially hiding issues.