Key Takeaways
- #1 open-weights, 4th overall. GLM-5.2 scores 51 on the Artificial Analysis Intelligence Index v4.1 — the top open-weights model, 4th overall, +11 over GLM-5.1 (40), and ~7 points clear of the next open models, MiniMax-M3 (44) and DeepSeek V4 Pro (44).
- It leads open weights, not the whole field. It sits roughly 5 points below Claude Fable 5 and ranks behind the closed frontier overall. "Closes the gap" — not "beats the frontier."
- Strong on coding, second to Opus 4.8. Independent coding results put GLM-5.2 ahead of GPT-5.5 yet behind Claude Opus 4.8 in most head-to-head comparisons.
- ~744B parameters is not home-runnable. It is Mixture-of-Experts (~40B active per token), but the full model needs multi-GPU or a rented GPU; only heavily quantized 1-bit GGUF builds fit consumer hardware.
- Self-hosted weights keep your data; the Z.ai API does not necessarily. MIT-licensed weights run inside your boundary; the first-party Z.ai API carries China data-residency considerations.
- Treat Z.ai's own benchmarks as company-reported. Reproducibility is contested — lead with the independent Artificial Analysis numbers.
What Is GLM-5.2?
GLM-5.2 is an open-weights large language model released June 13, 2026 by Z.ai (formerly Zhipu AI), under the MIT license with no regional usage limits. It was publicly benchmarked from June 16, 2026.
- ~744B total parameters (sources cite 743B–753B), using a Mixture-of-Experts architecture with ~40B active parameters per token.
- 1M-token context window with a 131,072-token maximum output.
- ~43,000 output tokens per task on average — up from GLM-5.1's ~26,000 — which raises local inference time and cost.
- MIT license: free to download, self-host, and modify, with no regional restrictions.
How Good Is GLM-5.2? Independent Benchmarks First
On the one independent, cross-vendor ranking — the Artificial Analysis Intelligence Index v4.1 — GLM-5.2 is the highest open-weights model at 51 points, 4th overall (Artificial Analysis, June 2026).
| Model | Index v4.1 | Tier |
|---|---|---|
| Claude Fable 5 | ~56 | Closed frontier |
| GLM-5.2 | 51 | #1 open weights / 4th overall |
| MiniMax-M3 | 44 | Open weights |
| DeepSeek V4 Pro | 44 | Open weights |
| GLM-5.1 (previous) | 40 | Open weights |
Independent coding results: Terminal-Bench 2.1 — GLM-5.2 scores 81.0 vs Claude Opus 4.8 at 85.0. SWE-bench Pro — GLM-5.2 at 62.1 (Z.ai-reported point value) lands ahead of GPT-5.5's 58.6; independent coverage corroborates that ordering. FrontierSWE — GLM-5.2 at 74.4 (Z.ai-reported point value) beats GPT-5.5 (72.6) and trails Opus 4.8 (75.1) by about one point, an ordering independent reporting confirms. Net independent verdict: GLM-5.2 is the strongest open-source coding model available as of June 2026, but it still trails Claude Opus 4.8 in most head-to-head comparisons (VentureBeat; letsdatascience, June 2026).
Z.ai's Own Numbers vs Independent Results: Read With Care
Several headline figures come from Z.ai's own evaluations and should be read as company-reported, not independently verified.
- Company-reported coding figures — for example MCP-Atlas 77.0 (Z.ai-reported), against GPT-5.5 at 75.3 and Opus 4.8 at 77.8 — are run by Z.ai itself and should be treated as claims pending independent replication.
- The Artificial Analysis writeup notes Z.ai's internal evaluations were reported weaker than its published benchmarks, and reproducibility is contested.
- Reproducibility is an open question. At least one prominent commentator characterizes the model as "bench-maxxed," and GLM-5.1 reportedly scored 0% on at least one benchmark that GLM-5.2 now does well on. The independent Artificial Analysis Index — not Z.ai's own suite — is what currently supports the #1-open-weights claim.
Can You Run GLM-5.2 at Home? The ~744B Reality Check
No — not the full model. "Open weights" and "self-hostable" do not mean "runs on a typical home PC."
- Full GLM-5.2 needs serious infrastructure: multi-GPU servers or a rented cloud GPU.
- On consumer hardware, only heavily quantized 1-bit GGUF builds are feasible, with quality and speed trade-offs.
- The high ~43,000-tokens-per-task output further raises local time and cost.
- For the hardware reality of large local models, see Running 70B Models on Consumer Hardware, Used GPUs for Local LLMs, the Local LLM Hardware Guide 2026, and Apple Silicon M5 for Local LLMs.
Self-Hosted Weights vs the Z.ai API: Where Your Data Goes
The license and the API are two different data-governance stories. Self-hosted MIT weights keep your data inside your boundary; the first-party Z.ai API does not.
- Self-hosted (MIT weights): data stays local and yours — no third-party transmission.
- Z.ai first-party API: independent coverage explicitly flags China data-residency considerations ("China data risk") on the API path (TechTimes, June 17, 2026).
- Decision framing: if data sensitivity matters, self-host the weights; if you use the hosted API, treat it as you would any third-party cloud endpoint subject to its jurisdiction.
GLM-5.2 Pricing and Cost
Via the hosted API, GLM-5.2 runs at roughly one-sixth the cost of closed-frontier models (VentureBeat, June 2026). Reported pricing is approximately $1.4 per 1M input tokens and $4.4 per 1M output tokens (as of June 2026). Factor in the high per-task output (~43,000 tokens) when estimating real workload cost.
Should You Use GLM-5.2?
GLM-5.2 decision guide
Use a local LLM if:
- •You want the strongest open-weights model available right now
- •You need self-hosting and data control inside your own boundary
- •You run long-horizon coding tasks
- •You want frontier-adjacent quality at roughly one-sixth the cost
Use a cloud model if:
- •You need the top score in head-to-head coding or reasoning
- •You do not require open weights and prefer a closed frontier model such as Claude Opus 4.8
- •You cannot provision multi-GPU or rented GPU infrastructure
Quick decision:
- →Best open-weights option today — but verify the contested benchmarks against your own tasks before committing.
GLM-5.2: Regional Context
EU / GDPR: Self-hosting GLM-5.2 under the MIT license keeps all inference data inside your own infrastructure, which satisfies data-residency expectations under the GDPR. The compliance difference between models is in supplier documentation, not data handling, when inference runs locally.
Japan (METI): For production deployments, document the model version (GLM-5.2), license (MIT), and whether inference runs on self-hosted weights or the Z.ai API, in line with METI AI governance guidance.
China / data path: GLM-5.2 is built by a Chinese lab. The key compliance lever is the deployment path, not the model: self-hosted MIT weights keep data in your boundary, while the first-party Z.ai API is subject to its home jurisdiction. Choose the path that matches your data-residency requirements.
Common Mistakes When Evaluating GLM-5.2
- Assuming "open weights" means "runs at home." The ~744B size requires multi-GPU or rented infrastructure; only 1-bit GGUF builds fit consumer hardware.
- Treating Z.ai's first-party benchmarks as verified. Lead with the independent Artificial Analysis Index; treat company-run coding numbers as claims.
- Conflating the MIT weights with the hosted API for data governance. Self-hosting keeps data local; the API is subject to its home jurisdiction.
- Reading "#1 open weights" as "beats the frontier." GLM-5.2 is 4th overall and trails Claude Opus 4.8 in most head-to-heads.
- Ignoring the ~43,000-token-per-task output when budgeting inference time and cost.
Frequently Asked Questions
Is GLM-5.2 the best open-weights model right now?
By the independent Artificial Analysis Intelligence Index v4.1 (June 2026), yes — GLM-5.2 scores 51, the top open-weights result and 4th overall. It leads the next open models, MiniMax-M3 and DeepSeek V4 Pro (both 44), by about 7 points. It does not, however, beat the closed frontier overall.
Can I run GLM-5.2 on a normal PC or Mac?
Not the full model. At ~744B parameters it needs multi-GPU servers or a rented cloud GPU. On consumer hardware you are limited to heavily quantized 1-bit GGUF builds, which trade quality and speed. See our hardware guides for what large local models actually require.
Does GLM-5.2 beat GPT-5.5 and Claude Opus 4.8?
On coding, independent results put GLM-5.2 ahead of GPT-5.5 (for example SWE-bench Pro and FrontierSWE orderings). Against Claude Opus 4.8 it trails in most head-to-head comparisons — for example Terminal-Bench 2.1 (81.0 vs 85.0) and FrontierSWE (about one point behind). The accurate summary is "leads open weights, closes the gap to the frontier," not "beats the frontier."
Is GLM-5.2 really free? What is the license?
GLM-5.2 is released under the MIT license with no regional usage limits, so you can download, self-host, and modify it for free. Running the full model still costs real infrastructure (multi-GPU or rented GPU), and the hosted Z.ai API is a paid service.
Is my data safe with GLM-5.2?
It depends on the deployment path. Self-hosted MIT weights keep all data inside your own boundary. The first-party Z.ai API carries China data-residency considerations flagged by independent coverage, so treat it as you would any third-party cloud endpoint subject to its jurisdiction.
Are GLM-5.2's benchmark numbers trustworthy?
The independent Artificial Analysis Index corroborates the #1-open-weights ranking. Z.ai's own coding numbers are company-reported, and reproducibility is contested — the Artificial Analysis writeup notes internal evaluations were reported weaker than published benchmarks. Lead with the independent numbers and treat first-party figures as claims.
How much does GLM-5.2 cost to run via API?
Roughly one-sixth the cost of closed-frontier models. Reported pricing is approximately $1.4 per 1M input tokens and $4.4 per 1M output tokens (June 2026). Because GLM-5.2 averages ~43,000 output tokens per task, estimate real cost on your own workload rather than per-token rates alone.
What hardware do I need to self-host GLM-5.2 properly?
For the full model, multi-GPU servers or a rented cloud GPU. Consumer hardware can only run heavily quantized 1-bit GGUF builds. See the Local LLM Hardware Guide 2026, Used GPUs for Local LLMs, and Running 70B Models on Consumer Hardware to size your setup.
Sources
- Artificial Analysis. (2026). "GLM-5.2 is the new leading open-weights model on the Artificial Analysis Intelligence Index." https://artificialanalysis.ai/articles/glm-5-2-is-the-new-leading-open-weights-model-on-the-artificial-analysis-intelligence-index — Independent Intelligence Index v4.1 ranking (51, #1 open / 4th overall).
- TechTimes. (2026, June 17). "GLM-5.2 open weights live, tops coding benchmark; API use carries China data risk." https://www.techtimes.com/articles/318543/20260617/glm-52-open-weights-live-top-coding-benchmark-api-use-carries-china-data-risk.htm — Data-residency considerations on the Z.ai API path.
- VentureBeat. (2026). "Z.ai's open-weights GLM-5.2 beats GPT-5.5 on multiple long-horizon coding benchmarks for 1/6th the cost." https://venturebeat.com/technology/z-ais-open-weights-glm-5-2-beats-gpt-5-5-on-multiple-long-horizon-coding-benchmarks-for-1-6th-the-cost — Independent coding comparison and cost framing.
- LetsDataScience. (2026). "GLM-5.2 open weights beats GPT-5.5 coding." https://letsdatascience.com/blog/glm-5-2-open-weights-beats-gpt-5-5-coding — Independent coverage of coding results.