GLM-5.3
Z.ai's ~753B parameter flagship, rebuilt through post-training on the GLM-5.2 base, and GLM-5.3-Flash, a 320B MIT-licensed sibling that fits a two-unit Faraday Growth cluster.
Model Story
GLM-5.3 is the latest flagship from Z.ai (formerly Zhipu), the team behind the General Language Model family that started at Tsinghua University. Z.ai launched it on its API on August 14, 2026 and published the weights on Hugging Face on August 28, 2026. It follows GLM-5.2 (June 16, 2026) and GLM-5.1, and it keeps the same ~753B total / ~40B active Mixture-of-Experts base as GLM-5.2. All of the gains come from post-training. The context window is 1M tokens.
Those gains are large on Z.ai's own numbers. GLM-5.3 scores 66.9 on DeepSWE v1.1, up from GLM-5.2's 46.2, and 28.3 on Terminal-Bench 3.0, up from 4.6. Independently, Artificial Analysis scores GLM-5.3 at 45 on its Intelligence Index v4.3, second among open-weight models behind MiMo-V2.6-Pro (46).
One thing did get worse for buyers: the license. GLM-5.2 was MIT. GLM-5.3 uses a bespoke "glm-5.3" license whose main restriction is that model-as-a-service businesses above $10B in annual revenue must pass Z.ai's security review. Few organizations come near that threshold, but it is no longer the "no review needed" MIT license GLM was known for.
Alongside the flagship, Z.ai released GLM-5.3-Flash: a smaller 320B model under a plain MIT license. GLM-5.3-Flash is the GLM model Faraday Machines ships on standard tiers, and it has its own section below.
Key Specifications
| Developer | Z.ai (formerly Zhipu) |
|---|---|
| Release Date | August 14, 2026 (API); weights published August 28, 2026 |
| Architecture | Sparse Mixture-of-Experts (MoE), same base as GLM-5.2 |
| Total Parameters | ~753 billion |
| Active Parameters | ~40 billion per token |
| Context Window | 1M tokens |
| License | Bespoke "glm-5.3" license (not MIT) |
| Weights | Hugging Face |
| Size at 4-bit | Roughly 410 GB |
| Faraday Fit | Cluster of 4+ Mac Studios |
API Pricing
Z.ai list price for GLM-5.3 as reported by API aggregators, October 2026. On-premises deployment on Faraday Machines eliminates per-token costs entirely; you pay only for hardware amortization and electricity.
Benchmarks
The top row is vendor-reported by Z.ai. Z.ai also reports 31.4% on its own Z.ai Code Bench, ahead of Claude Opus 4.8 (29.5%) while using under half the output tokens but behind Fable 5 (39.5%), and lists GPT-5.6 Sol at 34.6 on Terminal-Bench 3.0. The bottom row is independent, from the Artificial Analysis Intelligence Index v4.3. Under the earlier v4.1.1 index, GLM-5.3 (max) scored 60 at $0.68 per task; those older scores are not comparable with v4.3. GLM-5.3 had no standardized SWE-bench Pro entry as of late September 2026.
GLM-5.3-Flash: The Version Faraday Ships
GLM-5.3-Flash is the practical GLM for on-premises work. It is a 320B total / 18B active Mixture-of-Experts model with a 1M-token context, natively multimodal, and released under a plain MIT license, so it carries none of the flagship's license conditions.
Artificial Analysis scores GLM-5.3-Flash at 42 on its Intelligence Index v4.3 at $0.25 per task. That is the same score as GPT-5.6 Terra (42 at $1.40 per task) and Claude Opus 5.5 at low effort (42 at $0.55 per task), from a model you can run on two Mac Studios.
| Total Parameters | 320 billion |
|---|---|
| Active Parameters | 18 billion per token |
| Context Window | 1M tokens |
| Multimodal | Native multimodal input |
| License | MIT (full commercial freedom) |
| Size at 4-bit | Roughly 180 GB |
| Faraday Fit | Growth (2 Mac Studios, 256GB) |
On-Premises Deployment
GLM-5.3-Flash is the GLM model included on Faraday's standard tiers. At 4-bit quantization it is roughly 180 GB, which fits a Growth cluster (two Mac Studios, 256GB of unified memory) with room left for working memory. Faraday recommends it for general business intelligence, document analysis and multimodal input.
The full GLM-5.3 is available on larger clusters. At 4-bit quantization its weights are roughly 410 GB, more than the 384GB in our three-unit Scale tier, so it needs a cluster of four or more Mac Studios. Organizations that want the flagship's extra coding and agentic performance, and are comfortable with the bespoke license, can size a larger cluster with us.
Frequently Asked Questions
Can I run GLM-5.3 on-premises?
Yes. GLM-5.3-Flash, the 320B MIT-licensed sibling, runs on Faraday Machines' two-unit Growth tier: at 4-bit quantization it needs roughly 180 GB of the 256GB of unified memory. The full GLM-5.3 needs roughly 410 GB at 4-bit, more than the 384GB in the three-unit Scale tier, so it runs on a cluster of four or more Mac Studios, which Faraday Machines sizes on request.
Is GLM-5.3 still MIT licensed?
No. GLM-5.3 uses a bespoke glm-5.3 license instead of MIT. Its main restriction is that model-as-a-service businesses above $10B in annual revenue must pass Z.ai's security review. GLM-5.2 was MIT, and GLM-5.3-Flash, the smaller 320B model Z.ai released alongside GLM-5.3, still ships under a plain MIT license with full commercial freedom.
How much does GLM-5.3 cost?
Z.ai's list price for GLM-5.3 is $1.40 per 1M input tokens and $4.40 per 1M output tokens, as reported by API aggregators in October 2026. Artificial Analysis measures GLM-5.3-Flash at $0.25 per Intelligence Index task. Running GLM-5.3-Flash on-premises on a Faraday Machines Growth tier ($19,999 US one-time, including 12 months of support) removes per-token costs entirely.
How does GLM-5.3-Flash compare to GPT-5.6 Terra and Claude Opus 5.5?
GLM-5.3-Flash matches both on the Artificial Analysis Intelligence Index v4.3 at a lower cost per task. It scores 42 at $0.25 per task, the same score as GPT-5.6 Terra (42 at $1.40 per task) and Claude Opus 5.5 at low effort (42 at $0.55 per task). The full GLM-5.3 scores 45, second among open-weight models behind MiMo-V2.6-Pro at 46.
References
[1] AI News. (2026). "Zhipu GLM-5.3 benchmarks explained." August 2026. Available at: artificialintelligence-news.com
[2] Digital Applied. (2026). "GLM-5.3 weights ship under a bespoke license, not MIT." August 2026. Available at: digitalapplied.com
[3] Artificial Analysis. (2026). "GLM-5.3" model page. Available at: artificialanalysis.ai
[4] Artificial Analysis. (2026). "GLM-5.3-Flash" model page. Available at: artificialanalysis.ai
[5] Artificial Analysis. (2026). "Artificial Analysis Intelligence Index v4.3." September 2026. Available at: artificialanalysis.ai
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Run GLM-5.3-Flash on Hardware You Own
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