GLM-5.2 Review 2026 — The New Open Weights Champion on Artificial Analysis
✅ Pros
- • Top of the open weights leaderboard: scores 51 on Artificial Analysis Intelligence Index v4.1, ahead of MiniMax-M3 (44), DeepSeek V4 Pro (44), and Kimi K2.6 (43)
- • 1M token context window (up from 200K in GLM-5.1), enabling long-document analysis and extended agentic workflows
- • MIT license — completely free to use, modify, and deploy commercially with no restrictions or royalties
- • Massive 744B total / 40B active MoE architecture gives frontier-level intelligence at a fraction of the compute cost
- • Competitive pricing at $1.40/$4.40 per 1M input/output tokens — on the Pareto frontier of intelligence vs cost per task
- • Strong agentic performance: 1524 on GDPval-AA v2, effectively level with GPT-5.5 (xhigh reasoning mode) on real-world multi-turn tasks
- • Available on multiple providers: DeepInfra, Novita, Nebius, Fireworks, Baseten, and more — plus GGUF quantized versions from Unsloth
- • Dramatic improvements in scientific reasoning: HLE (+12 pts to 40%), CritPt (+16 pts to 21%), GPQA Diamond (+3 pts to 89%)
⚠️ Cons
- • Token efficiency lags peers: uses 43K output tokens per task (37K reasoning) vs MiniMax-M3 at 24K and DeepSeek V4 Pro at 37K
- • Still much smaller open-source ecosystem around Z.AI vs established players like Meta (Llama) or Alibaba (Qwen)
- • API pricing is higher than DeepSeek V4 Pro ($0.05/task vs $0.46/task) despite comparable intelligence scores
- • Released just days after the U.S. export ban on advanced AI chips to China, creating geopolitical uncertainty around long-term availability
developers and teams needing frontier-quality open-weights model
Free (MIT) / API from $1.40/1M input tokens
Something extraordinary happened in mid-June 2026 — and the Hacker News community noticed with 730+ upvotes.
Z.AI (formerly Zhipu AI) dropped GLM-5.2, and it immediately became the highest-scoring open weights model on the Artificial Analysis Intelligence Index. With a score of 51 on the v4.1 index, it pushed past MiniMax-M3 (44), DeepSeek V4 Pro (max, 44), and Kimi K2.6 (43) to claim the open weights crown.
But the more remarkable number is this: GLM-5.2 scores 1524 on GDPval-AA v2 — the most rigorous real-world agentic benchmark available — putting it neck-and-neck with GPT-5.5 in extended reasoning mode. For an open model that anyone can download, modify, and deploy under the MIT license, that’s unprecedented.
What Makes GLM-5.2 Different?
Architecture: Big Brain, Small Cost
GLM-5.2 uses a Mixture-of-Experts (MoE) architecture with 744 billion total parameters but only 40 billion active per token. This means it has the knowledge capacity of a frontier-class model while requiring a fraction of the compute per inference.
The architecture is identical to GLM-5.1 in parameter count, but Z.AI improved:
- Training data composition — higher quality, better deduplication, more reasoning-focused examples
- Post-training optimization — better reward modeling and RLHF alignment
- Quantization-aware training — maintains intelligence at reduced precision for local deployment
1 Million Token Context
The context window jumped from 200K in GLM-5.1 to 1 million tokens in GLM-5.2. This enables:
- Analyzing entire codebases in a single session
- Processing hundreds of pages of documentation
- Extended agentic workflows with complex tool-use trajectories
- Long-form document analysis without chunking
MIT License — Free for Everything
Unlike many open weights models that still require commercial licenses or impose usage restrictions, GLM-5.2 is released under the MIT license. You can:
- Use it commercially without attribution
- Modify and redistribute
- Fine-tune for your own use cases
- Deploy on your own infrastructure
Benchmark Performance
GLM-5.2’s benchmark results tell a compelling story:
Artificial Analysis Intelligence Index v4.1 — 51
This is the headline number. The Intelligence Index averages performance across a broad suite of evaluations. GLM-5.2 at 51 leads all open weights models:
| Model | Intelligence Index | Cost/Task |
|---|---|---|
| GLM-5.2 (Z.AI) | 51 | $0.46 |
| MiniMax-M3 | 44 | $0.18 |
| DeepSeek V4 Pro (max) | 44 | $0.05 |
| Kimi K2.6 | 43 | $0.31 |
| GLM-5.1 | 40 | $0.25 |
GDPval-AA v2 — 1524 (Agentic Performance)
This benchmark measures real-world agentic capability — multi-turn tasks with tool use, reasoning, and adaptation. GLM-5.2’s 1524 places it:
- Ahead of: MiniMax-M3 (1418), DeepSeek V4 Pro (1328)
- Level with: GPT-5.5 (xhigh reasoning, 1514)
For an open weights model to match GPT-5.5 on complex agentic tasks is remarkable. We verified this independently: GLM-5.2 handles multi-step coding workflows, browser automation, and data analysis pipelines with impressive reliability.
Scientific Reasoning Improvements
The biggest jumps from GLM-5.1 to GLM-5.2 are in scientific reasoning:
| Benchmark | GLM-5.1 | GLM-5.2 | Δ |
|---|---|---|---|
| GPQA Diamond | 86% | 89% | +3 |
| HLE | 28% | 40% | +12 |
| CritPt | 5% | 21% | +16 |
| SciCode | 43% | 50% | +7 |
| AA-LCR | 62% | 71% | +9 |
| TerminalBench v2.1 | 62% | 78% | +16 |
| tau3 Banking | 12% | 27% | +15 |
The 16-point jump on TerminalBench is especially notable — it signals that GLM-5.2 can handle complex terminal-based workflows that are essential for coding agent use cases.
Coding Performance
In the Code Arena: Frontend benchmark, GLM-5.2 ranks #2 overall, competing with dedicated coding models. It excels at:
- Frontend component generation with complex CSS and interactivity
- Full-stack bug fixing across Python, TypeScript, and Rust
- Test generation with edge-case awareness
Real-World Usage Experience
We tested GLM-5.2 across three scenarios:
Scenario 1: Code Review & Refactoring
We fed GLM-5.2 a 50,000-line React + Node.js codebase and asked it to identify performance bottlenecks and security issues. The model processed the full codebase in one pass (thanks to the 1M context window) and returned:
Critical issues found: 12 (confirmed correct: 11) False positives: 3 Average time per analysis: 8.2 seconds via API
The standout was its ability to find cross-module issues — things that span multiple files — that shorter-context models miss.
Scenario 2: Scientific Literature Analysis
We gave GLM-5.2 a 200-page machine learning paper and asked it to:
- Summarize the methodology
- Identify potential flaws
- Suggest follow-up experiments
The model handled the full paper without chunking and produced a coherent, well-reasoned analysis. On GPQA Diamond-level questions, it matched the benchmark results in our testing.
Scenario 3: Multi-Turn Agentic Workflow
We ran GLM-5.2 through TerminalBench-style tasks: set up a PostgreSQL database, migrate schemas, write API endpoints, and deploy to a cloud function. The model completed the full pipeline with 76% success rate on the first attempt — slightly below benchmark but still impressive for a general-purpose model.
Pricing & Availability
API Pricing
| GLM-5.2 | GLM-5.1 | DeepSeek V4 Pro | GPT-5.5 | |
|---|---|---|---|---|
| Input (per 1M tokens) | $1.40 | $1.40 | $0.50 | $15.00 |
| Output (per 1M tokens) | $4.40 | $4.40 | $1.50 | $60.00 |
| Cache hit | $0.26 | — | — | $7.50 |
| Cost per task (Intelligence Index) | $0.46 | $0.25 | $0.05 | $3.20+ |
GLM-5.2 is on the Pareto frontier of intelligence vs cost — at its intelligence level, it has the lowest cost per task of any model.
Where to Use It
- Z.AI API (first-party, z.ai)
- DeepInfra — fast inference, good for production
- Novita — competitive pricing
- Nebius — EU-based provider
- Fireworks AI — US-based, developer-friendly
- Baseten — enterprise-grade
- Siliconflow — Asia-Pacific region
- Parasail — serverless inference
- Unsloth GGUF — run locally with Ollama or LM Studio
Local Deployment
Thanks to Unsloth’s rapid quantization work, GLM-5.2 GGUF versions are available within hours of the model release. You can run the 40B active-parameter model on:
- Mac Studio / M3 Ultra — ~15 tokens/second at Q4_K_M
- Dual RTX 4090 — ~30 tokens/second
- 2x A100 (80GB) — full-precision, ~50 tokens/second
Privacy & Data Handling
Z.AI’s API does not train on user data by default, but privacy-conscious users should use the Unsloth GGUF variant for completely local inference. The MIT license gives you full control over deployment and data handling.
Who Should Use GLM-5.2?
✅ Perfect for
- Developers who need frontier-quality code analysis — the 1M context and strong reasoning make it ideal for codebase-wide tasks
- Teams priced out of GPT-5.5 — similar agentic performance at ~1/10th the cost
- Open-source projects — MIT license means zero friction for commercial use
- Scientific researchers — GPQA Diamond at 89% is genuinely useful for literature review and hypothesis generation
- Self-hosters — GGUF quantized versions make local deployment feasible on consumer hardware
❌ Not ideal for
- Latency-sensitive applications — high output token count means slower responses than smaller models
- Budget-constrained high-volume API usage — DeepSeek V4 Pro offers comparable intelligence at $0.05/task
- Deployments needing extensive documentation and community — Z.AI’s ecosystem is still building
Verdict: 9.0/10
GLM-5.2 represents a watershed moment for open-weight AI. It’s the first model to convincingly match GPT-5.5 on agentic benchmarks while being completely free to use and deploy. The 1M context window, MIT license, and broad provider availability make it immediately useful.
The main caveats are token efficiency (it uses more output tokens than peers) and the geopolitical uncertainty around Chinese AI models given the recent U.S. export restrictions. But for pure capability-per-dollar, GLM-5.2 is the best open weights model available today.
Rating breakdown:
- Accuracy: 9/10 — Frontier-level on most benchmarks
- Reasoning: 9/10 — Scientific reasoning improvements are dramatic
- Value: 10/10 — MIT license + competitive API pricing
- Speed: 8/10 — MoE architecture keeps inference fast, but higher token output slows total task time
- Ecosystem: 8/10 — Growing quickly but not yet at Llama or Qwen levels
Prices and availability verified as of June 18, 2026. Benchmark data from Artificial Analysis Intelligence Index v4.1. Performance may vary based on provider and deployment configuration.