Pelican from a few days ago: <a href="https://simonwillison.net/2026/Jul/6/hy3/" rel="nofollow">https://simonwillison.net/2026/Jul/6/hy3/</a> - I was using the free tier on OpenRouter, which expires on July 21st.<p>I tried the preview model 41 days ago and got a pelican with a "change pelican color" button: <a href="https://static.simonwillison.net/static/2026/hy3-preview-pelican.html" rel="nofollow">https://static.simonwillison.net/static/2026/hy3-preview-pel...</a>
Recently tried the pelican test on GPT-OSS which was probably one of the best local models of 2025. So cool to see how models have improved in the SVG pelican!
Curious why TFA calls out "Tencent in China".<p><pre><code> tencent/Hy3. New Apache 2.0 licensed model from Tencent in China
</code></pre>
Is there a Tencent AI lab elsewhere (MiniMax have some association with Tencent, for example)?
I think it's just their version of "Designed by Apple in California"
Tencent seems to have subsidiaries: <a href="https://en.wikipedia.org/wiki/Tencent#Subsidiaries" rel="nofollow">https://en.wikipedia.org/wiki/Tencent#Subsidiaries</a><p>Also they have large European / South African shareholders.
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Some things are better left unsaid.
I will vouch for Simon. I do not know him personally. To me his posts are honest and inspirational.<p>> I have been overly critical and arguing in bad faith about your writing in the past<p>I think you are just critical without a stated valid reason. Arguing in bad faith seems to be a thing if HN history is the judge.<p>And this is coming from a critical thinker, who is a bit tired of people firing off "human slop" comments. The Internet is full of a lot of people, but even when a few are bad apples, it spoils the lot. Maybe that is the intent. Maybe you are just grumpy for your life's situation.<p>It is 100% possible to build software entirely with AI. If you don't do that, that's great! I still code by hand from time to time, and I'm reading a lot of Rust nowadays, and learning the ropes. I come from a strong Python and Javascript background, plus networking and operations, which I'm a whiz at. I don't do it anymore, but I know how to inform it is done properly.<p>With this power, I can build things nobody wants to build, but me. Doesn't mean one has to put it into production, or it has to pass some security test, although with me driving it probably will. It only need be what is important to the end user, the prompter, to matter.<p>I think Simon helps people with this mindset be better at what they love to do. And for that, we should all be grateful.
Tony the Tiger on a bicycle selling addictive items to children is not new.
A month ago I wrote a blog post about how Hy3 was topping the OpenRouter rankings despite no one talking about it: <a href="https://news.ycombinator.com/item?id=48317294">https://news.ycombinator.com/item?id=48317294</a><p>As of today, it has fallen to 8/9th on the rankings. I don't see a reason where you would use this model over competitors. However, price economics are bit confusing, as currently the effective input price of Hy3 via OpenRouter is now the same as DeepSeek-hosted DeepSeek Flash V4.<p><a href="https://openrouter.ai/tencent/hy3-preview" rel="nofollow">https://openrouter.ai/tencent/hy3-preview</a><p><a href="https://openrouter.ai/deepseek/deepseek-v4-flash" rel="nofollow">https://openrouter.ai/deepseek/deepseek-v4-flash</a>
I had to stop using it because I was getting rate limited like crazy. Probably why it has dropped. Seemed like they couldn't keep up with demand.
Because it was free with generous limits and high availability, until it wasn't.
That was the preview model right? This one appears to be significantly better.<p>I mean it's still a small model, but at least the benchmark scores (incl. on DeepSWE) went up significantly.<p>It costs as much as Flash, but the benchmarks are on par with Pro (or above in some cases).<p>Of course, benchmarks are mostly meaningless -- the only real benchmark is the actual work you give it :)
It is really slow on openrouter and I ran into many http errors.
Writes pretty engaging prose, finetunes well, now MIT licensed... what's not to like?<p>Oh and very good world knowledge for the size: better than than DS4 Flash
Interesting way to show off a model last on every benchmark. Not sure any other lab is doing this
Novita is offering free Hy3 on OpenRouter until July 21st<p><a href="https://openrouter.ai/tencent/hy3:free" rel="nofollow">https://openrouter.ai/tencent/hy3:free</a><p><a href="https://x.com/novita_labs/status/2074158304159510819" rel="nofollow">https://x.com/novita_labs/status/2074158304159510819</a>
This is admittedly an aside from the content of the post itself, but... why do so many mobile sites insist on preventing zooming in and seem to share the same incredibly buggy image zooming? It's quite frustrating.
Curious how people feel about this compared to DS4 Flash, given they are pretty close in size. Also curious how well it holds up to heavy quantization.<p>DS4 Flash can currently run reasonably well on systems with ~96gb+ RAM, I wonder if Hy3 can compete there.
> given they are pretty close in size<p>One thing that might not be obvious about about DSV4 is how much innovation the Deepseek team implemented in its architecture. When llama.cpp fully supports its lightning indexer, the full 1M context will only require about 6G of RAM. So even though they are similar in size, I believe Deepseek will be much more efficient in that regard.<p>> I wonder if Hy3 can compete there<p>Highly depends on how well Hy3 is resilient to quantization. DSV4 is useful even at 2-bit quants.
That's a 2-bit quant of DS4 flash. You're probably better off running Qwen3.6-27B at Q8.
Having heavily evaluated both antirez’s ds4 flash and Qwen 3.6 27B at FP8 and Q8: it depends. The quantised Flash is better in a number of tasks despite running much slower on my DGX Spark-alike.<p>27B is amazing for its size but has some surprising limits when used for longer agentic coding sessions, especially if you’re using tools that are outside the stock standard web tech stuff: it really isn’t good at Relay, for example.
I think its good advice to test both on your own evals for sure, but the MoE parameters are already natively FP4 in ds4. Dropping to 2bpw isn't as big of a loss as it seems (and as corroborated by antirez's work).<p>Its also only 13B active, so your decode speed would be nearly 2x that of Qwen3.6-27B. So there are other latent benefits as well.
z-lab has been dropping dflash addons for a lot of models<p><a href="https://huggingface.co/collections/z-lab/dflash" rel="nofollow">https://huggingface.co/collections/z-lab/dflash</a><p>I'm running the qwen3.6-27B + dflash on a spark and tgen is way up, but keep the draft count low, acceptance rate is terrible beyond half a dozen and it requires more memory
For most coding or agentic tasks, Qwen 3.6 27B likely outperforms, yes.<p>For 'general intelligence', DS4 Flash seems to be a noticeable step up still.
I suspect it would depend on the task. DS4-flash does, as previously mentioned, handle quantization very well. Even at 2-bit it's still very coherent.
qwen 27b at q8 is slower and worse than ds4 at q2 in my experience.
Isn't Q8 way overkill these days? I see many graphs showing Q4 or Q5 having less than %1 deviation. Nvidia's NVFP4 Qwen quantization should be even better due to its better training methods.
Q8 isn't overkill if you have sufficient RAM to fit the whole model, and you care about quality. There's a number of people who have enough hardware to fit exactly one 27B to 35B size Q8 model and not more than that, so if you can fit the whole thing in Q8, no reason to use Q4 or Q6.
Qwen3.6 below Q8 often can't exit a reasoning loop (until it hits max output token count), forgets to insert a tool call, often mistakenly inserts them inside the thinking block... It's still usable though.
When orgs/bencmarks claim 1% deviation, in most cases that means measuring perplexity loss on datasets like wikitext or c4. Even if the loss is calculated via KLD or similar, its not a good proxy for whats actually degradaing at the task level across an entire rollout.<p>And for MoEs, very small amounts of loss can mean you're flipped to entirely different experts (this is also a problem more broadly with numerical stability issues too).
It depends on model size I think, but yeah, from my understanding at ~30B and below Q6 or even Q4 will get you 95%+ of the way there
Careful with those graphs, they're usually evaluating the model on KLD on relatively short transcripts. When you're running with 100k token contexts and the model running close loop a difference that looks small in terms of KLD may be quite substantial.<p>I'm not aware of any great benchmarks that work by giving it a live agentic harness and a number of realistic tasks that take most of the context window to accomplish and evaluate success rate and tokens to completion... but that's what you'd really want to use to judge different quantization levels.
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I was playing with Hy3 via openrouter yesterday (and I've also been using DS4 Flash/Pro as a daily driver since I cancelled my Anthropic sub a week ago).<p>I've found DS4 Flash to be very temperental (via Claude Code). The speed is great, but it often builds a completely wrong mental model and charges off down the wrong path. I find myself needing to rein it in regularly (and also compact the history, which undercuts the whole cache price advantage).<p>Hy3 isn't as fast, but so far it seems to stay on track much more reliably than DS4 Flash. It also doesn't seem to degrade as much with longer context. I'm not sure what the real pricing is, but I feel like it's a very competitive model.<p>As an aside, I also nabbed a 50m token pack for LongCat 2.0 to give it a whirl. Not free, but it's so cheap they're basically giving it away. Very impressed too - seems roughly on par with Hy3. Not frontier-level intelligence, but a dependable workhorse that can navigate a codebase well and can reliably execute what you tell it to do.
Hy3 lacks the DSv4 architecture's KV Cache efficiency.<p>Whereas I can run DSv4 Flash on a pair of DGX Sparks and have enough memory left over for 3M tokens of KV cache, with Hy3 (quantized to FP4), there is only room for ~130K tokens of KV cache.
Lower context window notwithstanding, Hy3's coding benchmarks hold their own against <i>DeepSeek v4 Pro</i> & <i>MiMo v2.5 Pro</i>. That's quite something for a model priced like <i>DeepSeek v4 Flash</i> & <i>MiMo v2.5</i> (for non-cached tokens), which are 3x cheaper than their respective <i>Pro</i> variants.
DS4-Flash is not only "significantly" smaller, it will also benefit from a lot more speed thanks to DSpark
I don’t like DS4 in my experiences with it I still prefer qwen locally and glm on api
This model is shockingly small for how capable it is. its a little bit bigger than deepseekV4 flash but around as capable if not more on some benchmarks than V4 pro, i wouldnt be surprised if this becomes a popular local model.
I've been wondering about that. GLM-5.2 is also half the size of DeepSeek V4 Pro. (But costs roughly twice as much.)<p>I looked into DeepSeek's architecture a little bit and the main focus was how can we save as much money as possible. They did a lot of cost cutting with the attention mechanisms. This allowed them to offer an insanely cheap price even on massive contexts, but seems to have come at the cost of performance?<p>At least, that's my guess, when I see smaller models costing more and outperforming, I think, "they must have denser attention?"
hardly, its still quite big unless by "local" you mean people that spend many thousands on rigs :)
> Hy3 has 295B parameters in total. To serve it on 8 GPUs, we recommend using H20-3e or other GPUs with larger memory capacity.<p>I would.
I feel like I'm taking crazy pills with hy3, it's either benchmaxxed to hell and back or skill issue on my part but I'd rather use dense gemma. I don't think there's a single model that's wasted more of my time in recent memory.
Gemma 4 31B is underrated. It surprises me a lot.
The Hy3 preview has been a mediocre performer in my benchmarks of security auditing with models, and yes, it is outperformed by Gemma 4 (31b soundly beats it, the MoE does slightly better, even at 4-bit quantization when using the QAT version). Qwen 3.6 27b also beats it.<p>I'll try it again now that it's out of preview and has been updated with more post-training. It presumably can't be worse, so maybe it's better enough to compete with a 31b model.
What we really need is a breakthrough in inference or LLM architecture to allow running GLM-5.2-level models at the size of Qwen 3.6 27b or smaller on consumer devices like a 48GB Macbook Pro, and at least at 100 tokens/second. My hypothesis is that a smaller, less capable but faster model paired with a good harness can run for longer and brute force its way out to solve problems that the bigger models can one-shot.
That would be great during the winter months
im more expecting the harness to be a literal LLM, Like how you put vibration dampeners on all kinds of mechanical structures
I tried out the model it's pretty great, better than ~~gpt5.4~~ gpt-5.4-mini perhaps, atleast close enough to sonnet 5 in performance that I didn't notice much of a gap.<p>Not really at gpt 5.5 tier though, and probably below glm 5.2...<p>But most of all it just works for me for most things I tried and it's exceedingly cheap so there is no reason not to use it, if you need a foss model.<p>Edited: gpt-5.4-mini not the base gpt-5.4
Hy3 DeepSWE - 28%<p>GPT5.4 xhigh DeepSWE - 52%<p>A lot of contaminated benchmarks in the blog post about Hy3, needs real testing though I have a distinct feeling it's benchmaxxed like a lot of Chinese models.
I think you’ve got the models wrong…gpt-5.4? I doubt there is any open source mode matching it. Maybe in a year
Yeah I meant gpt-5.4-mini, but GLM 5.2 is pretty close to gpt-5.4 base, and much better than it when it comes to design stuff.
GLM 5.2 already matches GPT-5.4 easily.
Worse than GLM-5.2, much more expensive than deepseek v4. Tough bargain.
There are now 3 tiers of competition:<p>-Fable + Gpt 5.6 Sol<p>-Opus + Gpt 5.6 Terra + Grok 4.5 + Muse Spark 1.1<p>-Open Chinese models: GLM + et family<p>The economics is on the Fable tier people are willing to spend a lot on it and on the Open tier you have to give it away to drive usage. The bottom tiers are also getting more and more competitive.
This site can’t be reached
<a href="https://hy.tencent.com/research/hy3" rel="nofollow">https://hy.tencent.com/research/hy3</a> is unreachable.
Been using this and GLM 5.2 back and forth. I like the speed of Hy3. Also seems very happy to follow instructions. Still haven’t found any open models that follow instructions as good as Mimo v2 pro though
MiMo v2.5 Pro is very spiky, in my experience. Sometimes excellent, sometimes mediocre. Weirdly high non-deterministic behavior. Run the same task three times, get three different results. I mean, they're all rolling dice for the next word, but MiMo seems to run hot on the randomness dimension in my benchmarks.<p>But, it performs very well for its size. I just looked it up, and it's much smaller than I thought it was when I was testing it. 310B A15B is tiny for how well it performs. I guess that explains why it's so cheap.
Er, actually, Pro is a big 1T model (which makes more sense given how well it does, sometimes). Regular MiMo is small, and I haven't tested it.
Quite interesting to see them and Meta and others release before OpenAI supposedly is to release GPT 5.6 today, would it be better to release it before or after? Calm before the storm type of thing?
I was expecting a new release of the Hy language (<a href="https://hylang.org" rel="nofollow">https://hylang.org</a>).
It's a very good model for this size and price. I tried it with a couple of small tasks - just an year ago this would be the level of the leading models.
Congrats, the trial chat is QR locked. The AI companies are spoiled and get crazier every day (not only this one, US companies as well).
That UI demo page is… really quite janky.
I would never use any product that can't explain on its own front page what it is and why I should use it.
Very impressive model for its size
I'm sorry but what on earth is going on with that bar chart, the bars are not consistent. E.g., in the frontierscience-olympiad chart Hy3 preview scores the same as DeepSeek (70.0) but Hy3 preview's bar is visibly lower.
Got really excited for a minute that the long-standing [Hy](<a href="https://hylang.org" rel="nofollow">https://hylang.org</a>) project had had a release, but it's just some confusingly-named LLM. Shame.