Hy 4 Preview

Confidence: medium — The sample contains two large, highly opinionated threads (270 source comments, 127 after the 40/post limit) with hard benchmark data cited by the users themselves; but there is no data on API pricing, writing/creativity, or multimodality, and many opinions concern compression or comparisons with Hy3 — hence 2 null categories and a medium rating.

Category scores

Intelligence

80

Threads cite a blind side-by-side of 163 experts across 203 engineering tasks: Hy4 preview slightly ahead of GLM 5.3 (2.99 vs 2.92) and Kimi K3 (2.99 vs 2.94); Horizon Math described as the best among open models (behind GPT-5.6 Sol); users stress 'this is only a PREVIEW' and incomplete post-training (Hy3 gained 50-100% after full training), so the intelligence potential is rated highly, with the caveat of undertraining.

Speed

40

The official statement quoted in the comments admits 'spending longer than necessary reasoning' and a tendency toward over-verification; jokes about '1TPM' and 'done thinking in a week', a note that NVMe offload means 'very slow tps', and the 770B size being 'too big for most people' — the community does not praise the model's working pace.

Coding quality

80

The cited engineering eval: 46.8% wins vs GLM 5.3 and 51.2% wins vs Kimi K3; SWE-Bench multi 82.9 (BF16) -> 81.3 (MIX-STQ1_0 quant) points to a strong coding position; comment on hy3 Q1: 'still very good, won't make syntax errors, just won't think ahead as far'; joking hope for '128% on DeepSWE'.

Instruction following

65

Officially admitted 'tendency to over-verify its own work' (doing more than asked); IFBench 73.5 -> 72.5 after quantization (good retention); the negative anecdote about hy3 as an agent (the model depopulating a field) concerns Hy3, not Hy4.

Long context

70

In the blog TLDR: 'Features 1M context'; MRCR 81.3 -> 81.1 after quantization (very good retention at 1M); no direct user experience with long context — the rating relies on benchmark data cited in the threads.

Price / value

60

Open weights shared on Day 0 on HuggingFace/GitHub/OpenRouter ('Kudos to the Tencent team to release weights on Day 0') — free local use; but the question 'any word on pricing 👀' went unanswered (no data on API pricing) and there is widespread complaining about hardware costs (200GB+ RAM, 'need 00001 quant for 32gb VRAM').

Reliability & uptime

55

Officially: 'we are shipping with known issues' given the preview status; no reported 429s/outages in the sample; no long-term user experience with Hy4 (the model is fresh).

Creativity & writing

no data

No data in the sample — none of the 127 comments rates Hy4's writing/creativity.

Multimodal & vision

no data

No data in the sample — the only mention of multimodality is a teaser from Tencent's quarterly report ('further enhancing model performance and multimodal capabilities'), with no user ratings.

Agentic & tool use

68

Hy4 in the Yuanbao app is labeled 'Use Tools to Solve Problems'; a comment about the viral workbuddy harness and 'massive agentic dataset for the post-train. Looking forward to it.'; the caveat that aggressive quantization breaks long agentic runs ('long agentic runs fall apart... around turn ten').

Summary

Pros and cons

Top quotes

• “WTF! That's insane. Someone test this lol.”

• “Hy4 preview came out slightly ahead of both GLM 5.3 (2.99 vs. 2.92 average, 46.8% wins / 12.8% ties / 40.4% losses) and Kimi K3 (2.99 vs. 2.94, 51.2% wins)”

• “The future of local models is brighter than expected. We can still compress them further. Hy4 has not yet been sufficiently (post-)trained.”

• “Awesome. Only 190 GB more to go and then I will run it on my RTX 3080.”

• “This is an early version of Hy4... among them, spending longer than necessary reasoning through complex tasks, and a tendency to over-verify its own work.”

• “Aggressive quants don't degrade uniformly. Short single-turn questions barely move; long agentic runs fall apart... that gap open up around turn ten, never at turn one.”

• “Features 1M context; excels at real-world productivity tasks. Beats GLM-5.3 and Kimi K3 in blind expert tests.”

• “The only saving grace from an onslaught of "iT oVerTHinKs" posts is the fact that it's a huge-ass chonker of a model, too big for most people.”

• “And this is a PREVIEW? The actual release must actually be fable level or higher then?”

• “Their Horizon Math score is the best among all open models, and second only to GPT-5.6 Sol.”

• “people legit get excited when somebody gets one of these monsters running at 1TPM, even if practical use is ask a question and it may be done thinking in a week or so.”

• “Glad it was measured on more than just KL divergence, 98% on KL can be very misleading.”

Top sample posts

Top 5 of 6 posts by score

• Tencent compressed Hy4-preview from 1.5TB to about 200GB GGUF and kept about 98% performance. ↗

  RedditUsr2 · 902 points · 147 comments

• Tencent/Hy4-preview 770B-A49B weight dropped ↗

  Beamsters · 559 points · 140 comments

• Tencent begins testing its new flagship model Hunyuan Hy4 ↗

  Nunki08 · 190 points · 25 comments

• An official 1-bit quant for Hy4??? 👀 ↗

  lakySK · 68 points · 10 comments

• Model: add Tencent Hy 4 (hy_v4) preview architecture support by Little0o0 · Pull Request #28127 · ggml-org/llama.cpp ↗

  pmttyji · 35 points · 0 comments

FAQ

What is Hy 4 Preview's overall community score?

66 out of 100, with medium confidence: The sample contains two large, highly opinionated threads (270 source comments, 127 after the 40/post limit) with hard benchmark data cited by the users themselves; but there is no data on API pricing, writing/creativity, or multimodality, and many opinions concern compression or comparisons with Hy3 — hence 2 null categories and a medium rating.

How was the Hy 4 Preview benchmark collected?

From 6 posts and 127 comments on r/LocalLLaMA sampled over the all time window.

Which categories does the Hy 4 Preview report score?

Intelligence, Speed, Coding quality, Instruction following, Long context, Price / value, Reliability & uptime, Creativity & writing, Multimodal & vision, Agentic & tool use.

What are the main community pros and cons for Hy 4 Preview?

Top pros: Spectacular compression: 1.5TB -> ~200GB GGUF with ~98% benchmark retention (MCP Atlas 83.7->83.2, SWE-Bench multi 82.9->81.3); Frontier-level quality: blind test wins over GLM 5.3 and Kimi K3; Horizon Math the best among open models. Main cons: Overthinking: officially admitted 'spending longer than necessary reasoning' and 'over-verify its own work'; 770B/A49B size — ~200GB+ of memory, out of reach locally for most users.

When was the Hy 4 Preview data collected?

Collected on September 26, 2026; the report was generated on September 26, 2026.