Tencent’s Hy4 Preview: Apache 2.0, 5% cache pricing, mid-pack SWE-Marathon

TL;DR

• Tencent’s Hy4 Preview ships as a 770B/49B-active MoE with a 1M-token context.
• License flips to Apache 2.0, unblocking EU, UK, and South Korean users the prior terms banned.
• SWE-Marathon score lands at 31.9, trailing GLM 5.3 (42.5) and Kimi K3 (42.0).
• OpenRouter cache pricing at ~5% of input cost undercuts a 10–20% norm, driving day-one volume.
• “Recursive self-improvement loop” claim credited with a 31.8% throughput gain resolves to AI-assisted systems work.

Only one developer drop lands today, but it’s a telling one. Tencent’s Hy4 Preview ships as a 770B-parameter MoE with a 1M-token context, an Apache 2.0 license that unblocks EU, UK, and South Korean users the prior terms shut out, and OpenRouter cache pricing at roughly 5% of input against a 10–20% industry norm. What it does not ship is a benchmark win: on SWE-Marathon it posts 31.9 against GLM 5.3’s 42.5 and Kimi K3’s 42.0.

Read together, the release reads as a distribution play rather than a capability one. The license flip and the aggressive cache pricing are moves aimed at day-one volume and hosted-inference reach; the “early-stage recursive self-improvement loop” Tencent credits with a 31.8% throughput gain turns out, on inspection, to be AI-assisted systems work. The flagship framing is the part that doesn’t quite hold.

Tencent’s Hy4 trades benchmark wins for reach

Source: simon-willison · published 2026-08-29

TL;DR

• Tencent’s Hy4 Preview ships as a 770B/49B-active MoE with a 1M-token context and a 1.56TB Hugging Face footprint.
• License flips to plain Apache 2.0, dropping the prior ban on EU, UK, and South Korea users.
• On SWE-Marathon, Hy4 scores 31.9 vs. 42.5 (GLM 5.3) and 42.0 (Kimi K3), undercutting the “flagship” framing.
• OpenRouter cache pricing lands at ~5% of input cost against a 10–20% norm, driving fast day-one volume.
• Tencent claims an “early-stage recursive self-improvement loop” drove a 31.8% throughput gain — really AI-assisted systems work.

The strategic package, not the capability leap

Simon Willison’s post frames Hy4 as a straight scaling step over July’s Hy3: 770B total parameters versus 295B, 49B active versus 21B, 1M context versus 256K, 1.56TB on disk versus 598GB. That’s the surface. The more interesting story is what Tencent changed around the weights.

The license flipped from a bespoke Hunyuan community agreement to plain Apache 2.0, with the previous “Territory” clause barring users in the EU, UK, and South Korea removed ¹. TechNode reads the broader Chinese open-weight cadence (Hy4, GLM 5.3, Kimi K3, Qwen 3.8) as a deliberate response to US export controls: seed the ecosystem with permissive frontier weights, then migrate serving onto Huawei Ascend ². Hy4’s license shift fits that thesis cleanly.

On hosted inference, Hy4’s cache pricing on OpenRouter came in at roughly 5% of input cost, versus the 10–20% most vendors charge, and commenters flagged that as the reason token volume spiked in the first days ³. Cheap cache is a distribution weapon.

Benchmarks tell a messier story

The “beats GLM 5.3, Qwen 3.8, Kimi K3” headline is thinner than the parameter count suggests. On SWE-Marathon — the long-horizon coding benchmark that matters for agentic work — Hy4 lands at 31.9, well behind GLM 5.3 (42.5) and Kimi K3 (42.0), with DeepSeek V4 Pro still the domestic coding leader ⁴. Tencent’s own blind eval of 163 experts across 203 engineering tasks scored Hy4 at 2.99/4.00, only fractionally ahead of GLM 5.3 (2.92) and Kimi K3 (2.94) ⁵. Fractional wins on a self-run panel are not what “flagship” usually means.

Willison’s observation that the reasoning trace uses truncated “caveman” English got the same read on Hacker News: deliberate token-efficiency, not sloppy training ³. The reasoning_effort template exposes only high and no_think — no middle setting — which is a stronger opinion about when reasoning is worth paying for than most vendors ship.

1.56TB, or 200GB if you squint

The 1.56TB Hugging Face footprint understates local-hosting reality in both directions. Tencent shipped an official MIX-STQ1_0 1-bit GGUF that r/LocalLLaMA users clocked at ~200GB with a claimed 98% performance retention — an unusually aggressive first-party quantization ⁶. That’s still “SSD crying” territory for consumer hardware, and the thread carried visible benchmark fatigue at the rate of 700B+ MoE drops.

The RSI claim worth flagging

The one line Willison didn’t touch: Tencent’s release notes describe an “early-stage recursive self-improvement loop” in which Hy4 identified bottlenecks in its own inference stack and drove operator-fusion and communication changes worth a 31.8% end-to-end throughput gain ⁵. In practice this is AI-assisted systems engineering, not the loaded RSI meaning — but it’s the framing Tencent chose, and it’s the kind of marketing sentence that will get quoted out of context. Worth noting now, before someone else does.

Footnotes

1. Tencent Cloud techpedia (license notes) — https://www.tencentcloud.com/techpedia/148044?lang=en

   Hy4 Preview is released under the standard Apache 2.0 license… previous ‘Territory’ clause that barred users in the European Union, United Kingdom, and South Korea is removed.

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2. TechNode — geopolitical framing — https://technode.com/2026/08/28/tencent-open-sources-hy4-preview-with-770b-parameters-and-a-1m-token-context/

   Chinese labs have responded [to US export controls] by ‘opening’ weights to establish de facto industry standards… allowing migration onto domestic Huawei Ascend chips.

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3. Hacker News discussion of Hy4-preview — https://news.ycombinator.com/item?id=49492632

   Hy4 offers a 5% cache cost compared to the 10-20% industry standard, leading to massive token volume on OpenRouter within days of its release… reasoning trace uses truncated ‘caveman’ English to save tokens.

   ↩ ↩²

4. Medium (Data Science in Your Pocket) — comparative benchmark writeup — https://medium.com/data-science-in-your-pocket/tencent-hy4-preview-beats-glm-5-3-qwen-3-8-kimi-k3-8ff85d3dbe5b

   Hy4 scored 31.9 on SWE-Marathon, significantly behind GLM 5.3 (42.5) and Kimi K3 (42.0)… Hy4 struggles to maintain state over marathon-length coding sessions where DeepSeek V4 Pro remains the domestic leader.

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5. Tencent official release announcement — https://www.tencent.com/tencent-releases-and-open-sources-tencent-hy4-preview/

   Model autonomously identified bottlenecks in its own inference system, conducting operator fusion and communication optimization that resulted in a 31.8% increase in end-to-end throughput — an ‘early-stage recursive self-improvement loop.’

   ↩ ↩²

6. r/LocalLLaMA thread on Hy4-preview weight drop — https://www.reddit.com/r/LocalLLaMA/comments/1w0igxk/tencenthy4preview_770ba49b_weight_dropped/

   Official ‘MIX-STQ1_0’ 1-bit GGUF compressed the model from 1.5TB down to ~200GB while reportedly retaining 98% of performance… still ‘SSD crying’ territory for consumer hardware.

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