GLM / Z.ai (Zhipu AI)
A Tier · 8.0/10
Zhipu AI's open-weights flagship -- GLM-5.2 (launched 2026-06-13) is a ~753B-parameter MoE with a 1M-token context and the new IndexShare sparse-attention architecture (~2.9x lower per-token FLOPs at 1M context), MIT licensed. Vendor benchmarks put SWE-Bench Pro at 62.1 (up from GLM-5.1's 58.4) and it tops the Artificial Analysis open-weights Intelligence Index; VentureBeat reports it beats GPT-5.5 on several long-horizon coding benchmarks at roughly 1/6 the cost. Drop-in for Claude Code / Cline / OpenCode. Still trained outside the Nvidia stack on Huawei Ascend silicon
Score Breakdown
Benchmark Scores
Benchmarks for GLM-5.2 (~753B MoE, launched 2026-06-13) -- vendor-published; third-party verification still settling
| Benchmark | Description | Score | |
|---|---|---|---|
| SWE-Bench Pro | 62.1% | ||
| GPQA Diamond | Graduate-level science questions | 91.2% | |
| AIME 2026 | 99.2% | ||
| Humanity's Last Exam (reasoning) | 40.5% | ||
| MMLU-Pro | Harder multi-subject reasoning | 81.2% | |
| HumanEval | Python code generation | 89.1% |
Last updated: 2026-06-18
Personality & Tone
The Z.ai research model
Tone: Academic and structured. GLM-4.6's instruction-tuned chat tends toward outlined, bullet-heavy responses and leans on established phrasing rather than casual voice.
Quirks: Strong on multilingual and tool use, weaker at playful conversation. Smaller community fine-tuning ecosystem than Llama or Qwen, so fewer 'flavored' checkpoints to pick from -- most deployments run the base instruction-tune.
The Good and the Bad
What we like
- +GLM-5.1 (2026-04-07) topped SWE-Bench Pro at 58.4 -- beating GPT-5.4, Claude Opus 4.6, and every other open-weight model on that benchmark. The result is externally verified and is the strongest agentic-coding signal from any Chinese open-weight model in 2026
- +First frontier model trained entirely on 100,000 Huawei Ascend 910B chips with zero Nvidia in the training stack -- a genuine proof point that non-Nvidia training pipelines can reach frontier quality, with big implications for US-China compute strategy
- +True MIT license -- one of the few frontier-tier open-weights models with zero commercial restrictions
- +GLM-5.1 is SOTA among open models for agentic tool-use and function calling; GLM-4.6V is #1 open-source on MMBench, MathVista, OCRBench among multimodal models
- +200K context window handles long documents reliably. Strong Chinese + English performance (unlike DeepSeek which is English-biased)
What could be better
- −Smaller Western community than Qwen or DeepSeek -- fewer tutorials, quants, fine-tunes
- −English tone is noticeably more stilted than Claude or Mistral for creative writing
- −PRC content filters apply to politically sensitive topics
- −Ollama support lags behind Qwen/Llama/Mistral release cycles
Pricing
Self-hosted (Free)
- ✓MIT license -- truly open, no MAU clauses
- ✓Full weights on Hugging Face
- ✓Commercial use fully permitted
API (Z.ai / OpenRouter, GLM-5.2)
- ✓GLM-5.2 (launched 2026-06-13): ~753B MoE, 1M context, IndexShare architecture
- ✓GLM-5.1 (2026-04-07): 744B MoE / 40B active, $0.60 in / $2.20 out (still available)
- ✓GLM-4.6V (vision): tiered
- ✓VentureBeat: ~1/6 the cost of GPT-5.5 on long-horizon coding
System Requirements
Hardware needed to self-host. Min = smallest viable setup (usually heavy quantization). Max = full-precision / production-grade.
| Model variant | Min | Max |
|---|---|---|
| GLM-5.2 (~753B MoE, launched 2026-06-13, 1M context)MIT license. IndexShare sparse attention lowers per-token FLOPs ~2.9x at 1M context. Active-parameter count not published in the model card | 256 GB RAM + 48 GB GPU (Q2 offload; community quants landing post-launch) | 8x H100 FP8 or 4x H200 |
| GLM-5.1 (744B MoE / 40B active, launched 2026-04-07)MIT license. Trained entirely on Huawei Ascend 910B, not Nvidia | 256 GB RAM + 48 GB GPU (Q2 offload, production builds still landing) | 8× H100 FP8 or 4× H200 |
| GLM-4.6 (355B MoE, legacy)MIT license -- zero commercial restrictions | 128 GB RAM + 24 GB GPU (Q3 offload) | 4× H100 FP8 |
| GLM-4.6V (multimodal)Vision tower adds ~4 GB on top of base footprint | 128 GB RAM + 28 GB GPU (Q3 + vision tower) | 4× H100 FP8 |
| GLM-4-9B (small) | 6 GB VRAM (Q4) | 24 GB VRAM FP16 |
Known Issues
- ZCODE HARNESS LAUNCHED (2026-07-02, press-verified): Zhipu released **ZCode**, an agentic-coding harness for GLM-5.2 that lets developers build autonomous coding assistants on the model -- an explicit shot at Anthropic's Claude Code, leaning on developer frustration with Anthropic's recent access restrictions and Zhipu's open-weights positioning. Launch incentives: **5 million free tokens for new ZCode users** and a 50% data-quota boost for existing subscribers. Open-source status implied but not explicitly confirmed -- verify the repo/license before building on it. Strategically this completes the GLM-5.2 story: MIT-licensed frontier weights + a first-party harness = a full Claude Code substitute stack from one Chinese labSource: SCMP (scmp.com/tech/tech-trends/article/3359170, 2026-07-02) · 2026-07-02
- INDEPENDENT BENCHMARK PUBLISHED (verified 2026-07-04): Artificial Analysis now has a citable independent eval for GLM-5.2 -- **Intelligence Index = 51, ranked #1 of 93 open-weight models** (artificialanalysis.ai/models/glm-5-2), corroborating the vendor's 'tops the open-weights index' claim with a third-party number. Keep the Zhipu-reported figures (SWE-Bench Pro 62.1%, Terminal-Bench 2.1 81.0%) labeled as vendor benchmarks -- the AA-51 is the independently-measured one to lead with.Source: Artificial Analysis (artificialanalysis.ai/models/glm-5-2), Crypto Briefing · 2026-07-04
- MODEL LAUNCH (2026-06-13): **GLM-5.2** -- Z.ai's new open-weights flagship 'for long-horizon tasks', a substantial leap over GLM-5.1. ~753B-parameter MoE, MIT licensed, with a 1M-token context that sustains long-horizon work and the new **IndexShare** sparse-attention architecture that cuts per-token FLOPs ~2.9x at 1M-token context. Vendor-published benchmarks (third-party verification still settling): SWE-Bench Pro 62.1, GPQA-Diamond ~91.2, AIME 2026 ~99.2, HLE reasoning ~40.5. It tops the Artificial Analysis open-weights Intelligence Index, and VentureBeat reports it beats GPT-5.5 on several long-horizon coding benchmarks at roughly 1/6 the cost. MIT weights shipped to Hugging Face (zai-org/GLM-5.2) and ModelScope; works as a drop-in coding backend for Claude Code, Cline, and OpenCode. Note: exact active-parameter count for 5.2 was not published in the model card -- treat the MoE-active figure as unconfirmed pending a vendor spec sheet.Source: Hugging Face zai-org/GLM-5.2 model card, VentureBeat (z-ais-open-weights-glm-5-2-beats-gpt-5-5...), z.ai/blog/glm-5.2 · 2026-06-13
- GLM-4.6 requires specific tokenizer and chat template -- several community llama.cpp quants initially had broken tool-use until fixes landedSource: Hugging Face discussions, GitHub issues · 2026-03
- Refuses discussion of Tiananmen, Taiwan, Xi Jinping -- same PRC content filters as DeepSeek and QwenSource: Reddit r/LocalLLaMA · 2026-02
Best for
Teams that need genuine MIT-licensed frontier open weights with no commercial strings. Especially strong for agentic workflows and vision (GLM-4.6V).
Not for
Consumer-facing English content generation (Mistral or Claude write better), or ultra-low-resource deployment (use Gemma 4 or Phi-4 instead).
Our Verdict
With GLM-5.2 (June 13, 2026), Z.ai has the strongest MIT-licensed open-weights model of 2026 -- a ~753B MoE with a 1M context, SWE-Bench Pro 62.1, and the top spot on Artificial Analysis's open-weights index, which VentureBeat says edges GPT-5.5 on long-horizon coding at a fraction of the cost. The true MIT license puts it ahead of Llama 4 on licensing, and the agentic tool-use performance beats most of its open-weight peers. GLM-4.6V is legitimately the best open multimodal model on several benchmarks. The weakness is purely ecosystem: fewer Western fine-tunes and less Ollama coverage. If you're building an agent or multimodal product and want clean licensing, GLM is the pick.
Sources
- Artificial Analysis: GLM-5.2 (Intelligence Index 51, #1 open-weight) (accessed 2026-07-04)
- Hugging Face: zai-org/GLM-5.2 model card (specs, license, benchmarks) (accessed 2026-06-18)
- VentureBeat: Z.ai's open-weights GLM-5.2 beats GPT-5.5 on long-horizon coding for ~1/6 the cost (accessed 2026-06-18)
- Winbuzzer: Z.ai releases GLM-5.1 754B tops SWE-Bench Pro (accessed 2026-04-17)
- TestingCatalog: Zhipu AI launches GLM-5.1 open-source model for coding (accessed 2026-04-17)
- Z.ai blog: GLM-4.6 and GLM-4.6V (accessed 2026-04-17)
- Hugging Face THUDM collection (accessed 2026-04-17)
- Artificial Analysis open-weights leaderboard (accessed 2026-04-17)
- OpenRouter pricing (accessed 2026-04-17)
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