On August 28, 2026, Tencent released Hy4 preview under Apache 2.0 on the same day Z.ai’s GLM-5.3 weights appeared under a custom license with a $10 billion Model-as-a-Service revenue gate. If you’re looking to build on open weights, this is one of the clearest examples yet of why model licensing now matters almost as much as benchmark performance.
The open-source AI community has been debating a superficially innocuous question for the past couple of years: when a company makes its model weights open source, does “open source” really have any meaning, or is it just a branding term? This week produced one of the clearest answers yet.
Z.ai announced GLM-5.3 on August 14 and said the weights would follow after safety evaluation and hardening. When the weights arrived on August 28, they came with a custom GLM-5.3 license. The license says that if a licensee or affiliate operates a Model as a Service business and has more than $10 billion in aggregate revenue over any consecutive 12 months, it must pass Z.ai’s security review before using the software commercially. On that same date, Tencent’s Hy Team released Hy4 preview, a 770-billion-parameter mixture-of-experts system, under standard Apache 2.0. Same week. Similar frontier scale. Similar capability tier. Very different rules.
Tencent suddenly put a new twist on the open-source AI license war.
What Tencent Just Dropped: A Frontier Model on Apache 2.0
Hy4 preview is a sparse mixture-of-experts transformer with 770 billion total parameters and 49 billion parameters activated per token, about 6.4 percent of the backbone. It uses 256 routed experts plus one shared expert, activates eight routed experts per token, and supports a one-million-token context window. Tencent has published BF16 and FP8 weights through Hugging Face, ModelScope, GitCode and CNB. It also provides official vLLM and SGLang deployment recipes and prebuilt Docker images, which reduces the setup work for teams that want to self-host it.
The headline figures are enough to attract attention: Tencent reports 92.3 on GPQA Diamond and 65.7 on SWE-bench Pro. Tencent also lists API pricing at $0.834 per million input tokens, $2.501 per million output tokens, and $0.042 per million cache-hit tokens. These are vendor-reported figures, so they should be treated as launch data rather than independent validation. Tencent is unusually direct about the fact that this is an early preview, acknowledging known issues such as reasoning for longer than necessary on complex tasks and a tendency to over-verify its own work.
| Feature | Hy4 preview (Tencent) | GLM-5.3 (Z.ai) |
|---|---|---|
| Total / active params | 770B / 49B | 743B / about 40B |
| Context window | 1M tokens | Up to 1M in published evaluation settings |
| License | Apache 2.0 | Custom GLM-5.3 license |
| Large MaaS revenue gate | None in Apache 2.0 | Security review above $10B aggregate 12-month revenue for Model as a Service businesses |
| Blind expert evaluation | 2.99 / 4.00 | 2.92 / 4.00 in Tencent’s internal comparison |
| Self-hosting | BF16, FP8, vLLM and SGLang recipes | Open weights on Hugging Face with its own license terms |
The Open-Source AI License War Was Already Heating Up
Hy4 undercuts the model-specific agreement attached to GLM-5.3. The GLM-5.3 license is not MIT and it is not Apache 2.0. According to Z.ai’s published license, the revenue threshold applies specifically when the licensee or an affiliate operates a Model as a Service business. If aggregate revenue exceeds $10 billion over any consecutive 12 months, that business must pass Z.ai’s security review before commercial use. For individual developers, startups and most companies below that threshold, the license still grants broad rights to run, deploy, fine-tune and modify the model.
That distinction matters for hyperscalers and very large AI infrastructure providers. A company such as AWS, Google Cloud or Microsoft offering GLM-5.3 as a managed model service would need to read the clause carefully and, if it falls within the license’s Model as a Service definition and revenue threshold, obtain the required security review. That is more precise than saying every large company is simply banned from commercial use.
Z.ai releases GLM-5.3’s weights under a new license requiring companies with $10B+ in revenue over 12 months to pass Z.ai’s security review.
Why Hy4 Is a Wrench in the Open-Source AI License War
On this part it depends. Value protection is only obtainable with a license where there is no alternative on comparable terms. The GLM-5.3 revenue gate may be strategically useful for Z.ai, but Tencent released a model in the same broad scale class under Apache 2.0, a standard permissive license without a revenue-threshold review clause. That gives infrastructure companies and developers a simpler licensing path if the model itself meets their technical requirements.
Among several recent flagship Chinese open-weight models, license design has become a real differentiator. Kimi K3, MiniMax M3 and Qwen3.8-Max use their own model-specific license terms, while Hy4 preview uses plain Apache 2.0. That difference is what makes Hy4 interesting in the current licensing contest: the technical comparison is no longer enough. Builders now have to compare model quality, serving economics and legal terms side by side.
Tencent released Hy4 Preview, a new open weight model under Apache License 2.0! 770B parameters, 49B active, 1M context window.
What the Benchmarks Actually Say
Perhaps the most notable number in Tencent’s release is the human evaluation. Tencent says 163 internal experts scored outputs across 203 engineering tasks without knowing which model produced each answer. Hy4 preview averaged 2.99 out of 4, compared with 2.92 for GLM-5.3 and 2.94 for Kimi K3. Tencent reports a 46.8 percent win rate against GLM-5.3 and a 51.2 percent win rate against Kimi K3.
But that means near-parity more than a decisive victory. The average-score differences are only 0.07 points against GLM-5.3 and 0.05 points against Kimi K3 on a four-point scale. The evaluation is still useful as a signal that Hy4’s outputs are competitive on engineering work, but independent replication would be valuable because Tencent designed and ran the evaluation itself.
The important preview caveat is also clear. Tencent calls this an early version and says there is still headroom in both pre-training and post-training. The currently released Hy4 preview weights are licensed under Apache 2.0. A future stable model could change technically, but that does not erase the Apache 2.0 grant already attached to the released preview weights.
The Real Story Is the Sparsity Race
There is a quieter technical shift underneath the license debate: the activation ratio. Hy4 activates 49B of 770B parameters, about 6.4 percent. GLM-5.3 is commonly described as roughly 40B active out of 743B total, around 5.4 percent. Z.ai’s newer GLM-5.3-Flash uses 18B active out of 320B, about 5.6 percent. Qwen3.8-Max uses about 95B active out of 2.4T, roughly 4 percent, while Ant Group’s Ling-3.0-flash uses 5.1B active out of 124B, about 4.1 percent.
The direction is clear: large mixture-of-experts systems are trying to keep only a small share of their total parameters active for each token. That can reduce compute per token, but it does not make the full model cheap to host. The complete weight set still has to live somewhere, and memory capacity, interconnects, quantization, batching and context length still shape real serving cost. This tension, lower active compute but a huge memory footprint, is one reason licensing matters so much at the hosting layer.
AITechLedger has also covered the efficiency side of this race in our GLM 5.3 Flash review, including its benchmarks, pricing and deployment trade-offs.
The community is already experimenting with aggressive Hy4 quantization. A community AngelSlim GGUF release includes a 213.66 GiB mixed 1-to-2-bit build and a 435.20 GiB Q4_K_M build. That is materially smaller than the original checkpoint, but the model card also warns that these files require patched llama.cpp support. The widely repeated claim that a roughly 200GB build retains 98 percent of performance is not established by that model card, so it should be treated as an unverified community claim rather than a Tencent benchmark.
What Reddit and X Are Saying Right Now
The open-weight community is treating this as a genuine fork in the road. Across r/LocalLLaMA, r/LLMDevs and r/singularity, the recurring theme is that licensing is becoming a first-class competitive weapon, not just a legal footnote. Early reaction on X, meanwhile, has focused on the model’s scale, active-parameter count and Apache 2.0 license.
BenchmarkList Hy4 page is now tracking 40+ benchmark results. It seems comparable to GLM 5.3.
A separate r/singularity discussion highlighted another distinction that matters: open licensing and hosted API pricing are separate levers. Tencent’s own pricing confirms that Hy4 is substantially more expensive per token than Hy3’s earlier launch pricing, even though the Hy4 weights themselves are available under Apache 2.0.
What This Means for the Open-Source AI License War
It’s not that Tencent is automatically more generous than Z.ai. In the open-weight race, license design is a real strategy, and a permissively licensed rival can change the commercial calculation quickly. Z.ai’s GLM-5.3 terms preserve broad rights for most users while adding a security-review gate for very large Model as a Service businesses. Tencent took the simpler route for Hy4 preview by using Apache 2.0.
The practical advice remains valuable for builders: hosting cost, behavior benchmarking and license terms are three different factors, and “open weights” does not always mean “free to use however you like.” Read the license, not only the model card. If your deployment depends on reselling or hosting a frontier-scale model, the open-source AI license war is now part of your architecture decision.
Before You Deploy Hy4 or GLM-5.3: A 5-Step License Checklist
- Read the actual license file. Do not rely only on the model card, a social post or a news headline.
- Identify your business model. Internal use, a customer-facing product and Model as a Service can trigger different obligations.
- Check revenue and user thresholds. GLM-5.3, Qwen3.8-Max and MiniMax M3 use different threshold-based conditions, while Hy4 preview is under Apache 2.0.
- Benchmark your own workload. Vendor benchmark tables are useful for discovery, but coding, agent and retrieval workloads can behave differently in production.
- Price self-hosting separately from API use. A permissive license does not make a 770B model inexpensive to keep in memory or serve at scale.
If you are comparing other models before deployment, AITechLedger’s AI Models coverage tracks model releases, benchmarks and practical differences.
Frequently Asked Questions
Is Tencent Hy4 preview really Apache 2.0?
Yes. Tencent’s official repository and Hugging Face model page state that the currently released Hy4 preview weights use the Apache License 2.0.
Does the GLM-5.3 $10B clause apply to every company?
No. The published clause is tied to a licensee or affiliate operating a Model as a Service business and exceeding $10 billion in aggregate revenue over any consecutive 12 months.
Does Apache 2.0 make Hy4 cheaper to run?
No. Apache 2.0 changes licensing freedom, not hardware requirements. Hy4 still has 770B total parameters, so self-hosting cost depends heavily on precision, quantization, memory, interconnects and serving architecture.
Key Takeaways
- Tencent released Hy4 preview with 770B total parameters, 49B active parameters and a 1M context window under Apache 2.0 on August 28, 2026.
- Z.ai’s GLM-5.3 weights also arrived on August 28 under a custom license. The $10B threshold applies to Model as a Service businesses and affiliates meeting the aggregate revenue condition, not every commercial user.
- In Tencent’s internal blind evaluation of 163 experts and 203 engineering tasks, Hy4 averaged 2.99, versus 2.92 for GLM-5.3 and 2.94 for Kimi K3. These results are vendor-reported.
- The deeper technical trend is MoE sparsity. Recent models activate roughly 4 to 6 percent of their total parameters per token, although the full memory footprint remains large.
- For teams comparing frontier open weights, the licensing landscape means model quality, infrastructure cost and license conditions now need to be evaluated together.
For more releases and analysis, browse AITechLedger’s Open-Source AI coverage.
Sources and Further Reading
- Tencent official Hy4 preview announcement
- Tencent Hy4 preview GitHub repository
- Tencent Hy4 preview on Hugging Face
- Hy4 Apache 2.0 license
- Z.ai official GLM-5.3 announcement
- GLM-5.3 license
- TestingCatalog on X
- Techmeme on X
- r/LLMDevs Hy4 discussion
- r/LocalLLaMA Hy4 quantization discussion
Editorial note: Benchmark figures and win rates attributed to Tencent are self-reported unless otherwise noted and have not been independently reproduced by AITechLedger. Model names, pricing and license terms can change for future releases. Verify the current model card and license before making a deployment decision. This article is editorial analysis for informational purposes and is not legal or investment advice.
