GLM-5 is the latest flagship large language model from Zhipu AI, a prominent Chinese AI startup, released on February 11, 2026. The model represents a significant step forward for China's AI ecosystem and is positioned as a direct competitor to frontier models from Western labs like OpenAI.
Architecture & Scale
At its core, GLM-5 is a Mixture-of-Experts (MoE) model that scales from the 355 billion parameters of its predecessor GLM-4.5 to 744 billion total parameters, with 40 billion active per token. This architecture allows the model to be extremely capable while keeping inference costs manageable, since only a fraction of the model's parameters are activated for any given input. Pre-training data was also expanded from 23 trillion to 28.5 trillion tokens, and the model integrates DeepSeek Sparse Attention (DSA) to reduce deployment costs while preserving long-context capabilities.
Training Innovation: Slime RL
A key innovation behind GLM-5 is its post-training approach. Zhipu developed "slime," a novel asynchronous reinforcement learning infrastructure that substantially improves training throughput and efficiency, enabling more fine-grained post-training iterations. This RL technique is credited with helping the model achieve strong performance on reasoning, coding, and agentic tasks. The model has also been reported to achieve a record-low hallucination rate, a notable improvement for practical reliability.
From Vibe Coding to Agentic Engineering
Zhipu frames GLM-5's philosophy as a shift from "vibe coding" to "agentic engineering," meaning the model is designed not just for conversational or one-shot code generation, but for complex, multi-step systems engineering and long-horizon autonomous tasks. It claims best-in-class performance among all open-source models on reasoning, coding, and agentic benchmarks, closing the gap with proprietary frontier models.
Open Source & Deployment
GLM-5 is released under the MIT license, making it fully open source. It can be deployed locally using frameworks like vLLM, SGLang, and xLLM. Zhipu also offers API access through its Z.ai platform.
Broader Significance
The release is significant in the broader context of China's AI sector reaching frontier-level capabilities despite ongoing U.S. semiconductor export restrictions. Chinese GPU maker Moore Threads achieved day-zero compatibility with GLM-5 on its domestic MTT S5000 GPU, underscoring efforts to build an independent AI hardware-software stack.
GLM-5 joins a crowded field of capable open-source models, but its combination of scale, open licensing, strong agentic performance, and novel RL training techniques makes it one of the most ambitious open-source releases to date from any Chinese AI lab.
GLM-5 vs Claude Code vs OpenAI Codex: Comparison
With GLM-5 entering the arena as a frontier-class open-source model with strong coding capabilities, it's worth comparing it against two of the most prominent AI-powered coding tools: Claude Code (by Anthropic) and OpenAI Codex. While GLM-5 is fundamentally a foundation model, Claude Code and OpenAI Codex are integrated product experiences built on top of their respective proprietary models. This comparison looks at how they stack up across key dimensions.
Overview Comparison
| Feature | GLM-5 | Claude Code | OpenAI Codex |
|---|---|---|---|
| Developer | Zhipu AI (Z.ai) | Anthropic | OpenAI |
| Release Date | February 11, 2026 | Early 2025 (continuously updated) | May 2025 (continuously updated) |
| Underlying Model | GLM-5 (744B MoE, 40B active) | Claude Opus 4.6 / Sonnet 4.5 | GPT-5.3-Codex (latest) |
| Type | Foundation model (API + self-host) | Agentic coding product (CLI, IDE, web) | Agentic coding product (app, CLI, IDE, web) |
| License | MIT (fully open source) | Proprietary (subscription-based) | Proprietary (subscription-based) |
| Context Window | 128K+ tokens | Up to 1M tokens (Opus 4.6) | 400K tokens |
| Self-Hosting | ✅ Yes (vLLM, SGLang, xLLM) | ❌ No | ❌ No |
Coding & Agentic Benchmarks
| Benchmark | GLM-5 | Claude Opus 4.6 (powers Claude Code) | GPT-5.2-Codex |
|---|---|---|---|
| SWE-bench Verified | 77.8% | 80.9% | 56.4% |
| Terminal-Bench 2.0 | 56.2 | Highest reported (est. ~60+) | 64.0% |
| AIME 2026 I | 92.7% | — | 100% (GPT-5.2 Pro) |
| GPQA-Diamond | 86.0% | — | 93.2% (GPT-5.2 Pro) |
| BrowseComp | 62.0 | 86.8% (multi-agent) | — |
| Hallucination (AA Index v4) | Record-low (Score: -1) | — | — |
Note: Benchmarks are sourced from each vendor's published results and may use different evaluation harnesses, making direct comparisons approximate. GPT-5.3-Codex benchmarks were not yet fully published at time of writing.
Product & Workflow Integration
| Capability | GLM-5 | Claude Code | OpenAI Codex |
|---|---|---|---|
| Terminal / CLI | Via third-party tools (e.g., Ollama) | ✅ Native CLI | ✅ Native CLI (open-source) |
| IDE Integration | Community-driven (CodeGeeX) | ✅ VS Code, JetBrains, Cursor, Windsurf | ✅ VS Code, Cursor, JetBrains |
| Web Interface | Z.ai platform | ✅ claude.ai/code | ✅ Codex app (ChatGPT) |
| Mobile | ❌ | ✅ iOS app | ✅ iOS app |
| Git Integration | Manual | ✅ Native (commits, branches, PRs) | ✅ Native (commits, PRs) |
| Multi-Agent Support | ❌ | ✅ Agent Teams (research preview) | ✅ Multi-agent parallel workflows |
| Cloud Sandboxes | ❌ | ✅ Web-based sessions | ✅ Cloud sandbox per task |
| MCP (Model Context Protocol) | ❌ | ✅ Full support | ✅ Supported |
| Automations / CI-CD | ❌ | ✅ GitHub Actions, GitLab CI/CD | ✅ Background automations |
| Skills System | ❌ | ✅ .claude/skills/ | ✅ Built-in Skills library |
Pricing
| Plan / Tier | GLM-5 | Claude Code | OpenAI Codex |
|---|---|---|---|
| API Input (per 1M tokens) | ~$0.80–$1.00 | $5.00 (Opus 4.6) | $1.75+ (GPT-5.2) |
| API Output (per 1M tokens) | ~$2.56–$3.20 | $25.00 (Opus 4.6) | $14.00+ (GPT-5.2) |
| Consumer Plan | Free tier on Z.ai | $20/mo (Pro), $100–$200/mo (Max) | Included with ChatGPT Plus ($20/mo), Pro ($200/mo) |
| Self-Hosted Cost | GPU infrastructure only | N/A | N/A |
Key Takeaways
GLM-5 is ideal for teams that want full control over their infrastructure, need an open-source model they can self-host, fine-tune, and integrate into custom pipelines — all at a fraction of the cost of proprietary APIs. Its coding benchmarks approach frontier proprietary models, and its MIT license makes it uniquely flexible. However, it lacks the polished product experience and deep IDE/Git/CI integrations that Claude Code and Codex offer out of the box.
Claude Code excels as a terminal-native, agentic coding assistant with deep codebase understanding. Powered by Claude Opus 4.6 (the highest-scoring model on SWE-bench Verified), it's designed for developers who want a reliable AI partner embedded in their existing workflow. Its strength lies in sustained multi-file reasoning, git-aware operations, and the emerging Agent Teams feature for parallel work. The trade-off is cost — Opus 4.6 is the most expensive option per token.
OpenAI Codex offers the most product-rich ecosystem, with a dedicated macOS app, cloud sandboxes, built-in Skills, and Automations for background tasks. GPT-5.3-Codex is the latest model, designed for long-horizon agentic work with mid-turn steering. Its integration across ChatGPT, CLI, IDE, GitHub, and mobile gives it the broadest surface area. It strikes a middle ground on pricing and is included in existing ChatGPT subscription plans, making it the most accessible for developers already in the OpenAI ecosystem.