Hy4 Preview vs GLM 5.3 Flash: A Practical Comparison

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Choosing between Hy4 Preview vs GLM 5.3 Flash gets confusing fast, because the specs pull in opposite directions and the pricing pages disagree with each other. One model is small-active and cheap. The other is huge and sold as raw weights. This guide lines them up on the things that actually change your decision: cost, benchmarks, context, licensing, and the workload each one was built for. By the end you will know which model to reach for and why.

Both models are recent. Z.ai launched GLM 5.3 Flash on August 26, 2026, and Tencent released Hy4 Preview on August 28, 2026. That recency matters, because independent benchmark coverage is still filling in, and some figures below are vendor-reported rather than third-party verified.

Hy4 Preview and GLM 5.3 Flash solve different problems

These two models are not natural rivals, and treating them as interchangeable will lead you to the wrong pick. GLM 5.3 Flash is an efficiency model. Its whole design goal is frontier-class answers at a fraction of the usual cost, with multimodal input as a bonus. Hy4 Preview is a capacity model. It is large, text-only, and aimed at sustained engineering and agent workflows that reward deep reasoning.

The parameter counts tell the story. GLM 5.3 Flash runs 320B total parameters but activates only 18B per token, which is the trick that keeps it fast and cheap to serve. Hy4 Preview carries 770B total parameters and activates 49B per token across 78 layers, a far heavier compute footprint aimed at harder tasks.

So the honest framing is a question of fit. GLM 5.3 Flash is the natural starting point when your workload is high volume and price-sensitive, or involves images and video. Hy4 Preview earns a look when you need a self-hostable coding and agentic model and can afford the serving cost.

Table 1: Hy4 Preview vs GLM 5.3 Flash at a glance

GLM 5.3 Flash is far cheaper, and Hy4 Preview has no clear hosted rate

GLM 5.3 Flash wins clearly on raw price, but the comparison comes with an asterisk you need to understand before you budget. GLM 5.3 Flash has published API pricing. Hy4 Preview, as of September 2026, mostly does not.

GLM 5.3 Flash lists at $0.075 per million input tokens and $0.25 per million output tokens, with cached input at $0.015. That input rate is a launch promotion. Per Z.ai's own pricing docs, the promo runs through September 9, 2026, after which input reverts to a list price of $0.15 per million, and output to $0.50. Even at list price, it is the cheapest paid model in the GLM 5 series, and far cheaper than the flagship GLM 5.3, a separate text-only coding model.

Hy4 Preview is where the sources conflict. One provider on OpenRouter lists it at roughly $0.83 per million input and $2.50 per million output, though OpenRouter's weighted average input runs lower, near $0.10, because provider routing varies. Other trackers, including LLM Stats and BenchLM, report no first-party hosted token rate at all and treat it as self-hosted, where your real cost is infrastructure rather than a per-token bill. The practical takeaway: Hy4 Preview's cost depends entirely on how you access it, and any single quoted rate should be verified against the provider you plan to use.

Table 2: Pricing comparison (as of September 2026)

One more caveat on GLM 5.3 Flash speed, since cheap does not always mean fast. On Z.ai's own API it reportedly runs around 49 tokens per second, which is slow, while third-party hosts are far quicker. If throughput matters, choose your provider deliberately.

GLM 5.3 Flash is multimodal; Hy4 Preview is text-only

GLM 5.3 Flash is the only real option of the two for work involving images, video, documents, or interface screenshots. It is the first natively multimodal model in the GLM 5 series, accepting text, image, and video input and returning text. Z.ai trained it on a 30-trillion-token multimodal corpus so vision is baked in rather than bolted on.

That capability shows up in practical places. GLM 5.3 Flash posts strong chart-and-document reasoning scores, including 89.4 on CharXiv Reasoning and 78.0 on Chartography, both with tools, in Z.ai's own testing. For frontend coding, it can render its output, inspect what a user would actually see, and revise the result. On pure video benchmarks it trails Gemini 3.7 Flash, so heavy video analysis deserves its own test.

Hy4 Preview is text-only. That is not a flaw so much as a scope choice, since Tencent aimed it at code and agent workflows rather than visual tasks. Never feed a model an image and you lose nothing here. The moment you do, GLM 5.3 Flash is your pick.

On coding and agentic benchmarks, the two trade blows

Neither model is a clean benchmark winner, and the results depend heavily on which suite you trust. Both vendors published their own numbers, and independent aggregators land them close on composite scores. Read all of these as directional, and note that most Hy4 Preview figures are vendor-reported by Tencent, while GLM 5.3 Flash carries an Artificial Analysis Intelligence Index score of 57 (v4.1.1, per Z.ai citing Artificial Analysis).

Where GLM 5.3 Flash looks strong: in Z.ai's testing it scores 63.4 on DeepSWE v1.1, 48.8 on AutomationBench, and 78.4 on Toolathlon Verified, often beating the previous GLM generation by wide margins and approaching Claude Opus 4.8 on several agentic tasks. Its headline claim is frontier-class intelligence at roughly a tenth of the usual cost.

Where Hy4 Preview looks strong: in Tencent's testing it scores 82.9 on SWE-bench Multilingual and 65.7 on SWE-bench Pro, edging out several open-weight rivals on real-world software engineering. Tencent also reports a blind human evaluation in which 163 internal experts rated outputs on 203 engineering tasks, with Hy4 Preview preferred slightly more often than not against comparable models. Its known weak spots are the longest coding tasks and the hardest competition-math benchmarks.

Composite scores put them nearly level, with LLM Stats reporting 51.1 for GLM 5.3 Flash and 51.5 for Hy4 Preview. Across the seven benchmarks reported for both, that tracker gives GLM 5.3 Flash a slight edge, winning four. The signal is close, so let your specific workload break the tie.

Table 3: Selected benchmark scores. Hy4 Preview figures are Tencent-reported and GLM 5.3 Flash figures are Z.ai-reported, run on different evaluation harnesses, so per-benchmark rows are indicative rather than directly comparable. The LLM Stats composite normalizes across both.

Context, size, and licensing compared

A 1-million-token context window and open weights are common to both models, so the deciding details are size, license, and modality. A 1M-token window puts both in the top tier for long-document and long-codebase work, though real-world usable context always depends on your serving setup.

On licensing, both are permissive, with a small difference worth noting. GLM 5.3 Flash ships under the MIT license, and Hy4 Preview under Apache 2.0. Both allow commercial use and self-hosting. Apache 2.0 adds an explicit patent grant, which some legal teams prefer for commercial deployments.

Size and serving are where the gap turns real. Hy4 Preview is 770B parameters and, even in its FP8-quantized form, needs serious multi-GPU hardware to run. GLM 5.3 Flash, with far fewer active parameters, is cheaper to serve at scale and easier to host. Both publish weights on Hugging Face and support inference frameworks including SGLang and vLLM.

Which model should you choose

Choose GLM 5.3 Flash if cost, throughput, or multimodal input drives your decision. It is the better default for high-volume workloads, document and chart understanding, vision tasks, and any project where price per token is the deciding factor. You get near-flagship intelligence at a fraction of the cost, with the trade-off that first-party speed is slow and thinking mode cannot be turned off.

Choose Hy4 Preview if you want a self-hosted coding and agentic model and you have the infrastructure to serve it. Its strengths are real-world software engineering, tool use, and long-horizon agent tasks, backed by a production-ready serving stack and a favorable Apache 2.0 license. The cost is operational: you supply the hardware, and the model is heavy.

For most builders weighing the two, the split is simple. If you want something cheap and flexible to call through an API, GLM 5.3 Flash. If you want a large open model to own and run yourself for engineering work, Hy4 Preview.

If GLM 5.3's text-only limit or subscription-only access is the blocker, our GLM 5.3 alternatives guide sorts six models by which specific gap you are trying to close.

Build production apps without picking a raw model

Choosing between Hy4 Preview vs GLM 5.3 Flash is really a choice between two engineering trade-offs: a cheap, flexible multimodal model versus a heavy, self-hosted coding specialist. Match the model to the workload, verify the pricing against the provider you will actually use, and treat the newest benchmark figures as provisional until third-party coverage catches up.

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