GLM-5: China's 744B Open-Source Model That Rivals GPT-5.2

GLM-5 Scores 50 on the Intelligence Index — A First for Open-Source

Zhipu AI released GLM-5 on February 11, 2026, and the numbers speak for themselves: a score of 50 on the Artificial Analysis Intelligence Index v4.0, making it the first open-weight model to reach that threshold. It ranks #1 among open models on both the LMArena Text Arena and Code Arena, placing it on par with Claude Opus 4.5 and GPT-5.2 (xhigh) across agentic, reasoning, and coding benchmarks.

For builders who have been waiting for an open-source model capable of handling real software engineering tasks — not just benchmark puzzles — GLM-5 is the most credible candidate to date.

Architecture: 744B Parameters, Only 40B Active

GLM-5 uses a Mixture-of-Experts (MoE) architecture with 744 billion total parameters but only 40 billion active per token. This is a significant efficiency play: you get frontier-level capability at roughly one-fifth the compute cost of a dense model of equivalent quality.

The key architectural innovation is Dynamic Sparse Attention (DSA), which replaced GLM-4.5's standard MoE approach. DSA dynamically allocates attention resources based on token importance, reducing computational overhead without sacrificing long-context understanding. The model supports a 200K context window with 128K max output — numbers that put it firmly in the same league as GPT-5.2's 400K context.

Training at Scale

• Pre-training corpus: 28.5 trillion tokens (up from GLM-4.5's 15T)
• Architecture: MoE with DSA, 744B total / 40B active parameters
• Context: 200K input, 128K output
• License: MIT (fully open)
• Mid-training phase: Progressive context extension from 4K to 200K using long-context agentic data

The Secret Sauce: Asynchronous Agent Reinforcement Learning

GLM-5's post-training pipeline is where it gets interesting. Zhipu AI implemented a three-stage sequential reinforcement learning process:

1. Reasoning RL — sharpening logical and mathematical capabilities
2. Agentic RL — training the model to handle complex, multi-step workflows
3. General RL — broadening performance across diverse tasks

The breakthrough is the asynchronous RL infrastructure that decouples generation from training. Traditional RL for LLMs forces the model to generate responses and learn from them synchronously, creating a bottleneck. Zhipu's approach runs generation and training in parallel, dramatically improving post-training throughput.

They also introduced On-Policy Cross-Stage Distillation to prevent catastrophic forgetting — ensuring the model retains its reasoning edge while becoming a better generalist.

Benchmark Results: Where GLM-5 Stands

GLM-5 was evaluated on 8 key benchmarks alongside DeepSeek-V3.2, Claude Opus 4.5, Gemini 3 Pro, and GPT-5.2:

On average, GLM-5 shows a 20% improvement over its predecessor GLM-4.7 and is comparable to Claude Opus 4.5 and GPT-5.2 (xhigh), while outperforming Gemini 3 Pro.

Practical Builder Assessment: Can You Actually Use GLM-5?

This is where most coverage falls short. Benchmarks are one thing — can you actually deploy and use GLM-5 in production?

What Works Well

• Agentic coding tasks: GLM-5 excels at end-to-end software engineering. It handles multi-file changes, understands codebases holistically, and can work through complex debugging sessions.
• Long-horizon tasks: The Vending Bench 2 results (#1 among open models) demonstrate genuine long-term planning capability, not just pattern matching.
• Cost efficiency: With only 40B active parameters, inference costs are approximately 6x lower than proprietary alternatives for comparable quality.
• MIT license: No usage restrictions, no revenue caps, no phone-home requirements.

The Caveats

• Hosting requirements: 744B total parameters means you need significant infrastructure. Even with MoE, you're looking at multi-GPU setups for self-hosting.
• API access: Zhipu offers API access through their platform, but latency from outside China can vary.
• Ecosystem maturity: The tooling ecosystem around GLM models is growing but still behind OpenAI and Anthropic's developer experience.
• Benchmark vs. real-world gap: While the benchmarks are impressive, independent verification of real-world coding performance is still emerging.

Open Source Implications: What This Means for the Industry

GLM-5 scoring 50 on the Intelligence Index matters beyond the number itself. It demonstrates that open-source models can now compete at the frontier — not just on narrow benchmarks, but on the agentic, multi-step tasks that actually matter for production software engineering.

This has several implications:

1. Proprietary moat is shrinking: If an MIT-licensed model can match GPT-5.2 on coding tasks, the value proposition of closed models shifts from capability to ecosystem and reliability.
2. China's AI competitiveness is real: Despite export controls on advanced chips, Zhipu AI (backed by Tsinghua University) continues to push the frontier. The DSA architecture is a genuine innovation, not just scale.
3. Self-hosting becomes viable for serious workloads: Companies with privacy requirements or specific compliance needs now have a frontier-class option they can run on their own infrastructure.
4. Agent frameworks benefit most: Open-weight models that excel at agentic tasks lower the barrier for building autonomous coding agents, CI/CD integrators, and developer tools.

GLM-5 vs. GPT-5.2 vs. Claude Opus 4.5: How They Compare

Who's Behind GLM-5: Zhipu AI

Zhipu AI (Z.ai) is a Beijing-based AI company spun out of Tsinghua University's Knowledge Engineering Group. Founded in 2019, they've raised over $400 million and were one of the first Chinese companies to release competitive open-source LLMs with the GLM series.

Their approach has been notably different from DeepSeek: while DeepSeek focused on training efficiency and distillation, Zhipu has invested heavily in agentic capabilities and novel architectures like DSA. The result is a model specifically optimized for the tasks that matter most to developers building AI-powered tools.

FAQ

Is GLM-5 truly open source?

Yes. GLM-5 is released under the MIT license, which allows unrestricted commercial use, modification, and distribution. Model weights, code, and documentation are available on GitHub at github.com/zai-org/GLM-5.

How does GLM-5 compare to DeepSeek-V3.2?

GLM-5 outperforms DeepSeek-V3.2 on most agentic and coding benchmarks. Both are Chinese open-source models, but GLM-5's DSA architecture and three-stage RL training give it an edge on long-horizon tasks.

Can I run GLM-5 locally?

Running the full 744B model requires significant hardware — multiple high-end GPUs with substantial VRAM. However, the 40B active parameters mean inference is more efficient than a dense model of similar capability. Quantized versions and smaller distilled variants are expected from the community.

What is the Intelligence Index v4.0?

The Artificial Analysis Intelligence Index v4.0 is a composite benchmark incorporating 10 evaluations including τ²-Bench, Terminal-Bench Hard, SciCode, Humanity's Last Exam, and GPQA Diamond. GLM-5's score of 50 makes it the highest-scoring open-weight model.

How much does it cost to use GLM-5 via API?

Zhipu AI offers API access through their platform at approximately 6x lower cost than GPT-5.2 for comparable tasks. Exact pricing varies by usage tier and region.

Is GLM-5 suitable for production use?

For coding and agentic tasks, GLM-5 shows production-grade performance. However, as with any new model release, thorough testing against your specific use cases is recommended before full production deployment.