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- Hacker News
- Perhaps model size and reasoning length trade off to some extent, similar to CPU vs. RAM. A smaller model with a longer reasoning trace has more intermediate structure to latch onto and build on.by sp1982
- Makes sense to me.
We will see, since if true then it is likely the other makers of small, dense models will copy it and include high reasoning by default.
If that also makes the other dense open source models better, then you are probably correct.
by deflator - Why are hosting providers charging to much to host it, compared to much larger models? https://openrouter.ai/compare/qwen/qwen3.8-27b/deepseek/deep...by josephcooney
- 2 things, 1st: Alibaba's official endpoint pricing. they don't want to undercut too much as there is profit to be made to be close to it but not too low
2nd, and maybe more importantly: KV is not as efficient (vram usage-wise) as something like deepseek v4 flash. for 256k, fp8 kv is 9.3gb (full precision ~17.3gb). deepseek v4 flash is ~2.5b for the same size at full precision (which is fp4/8, if you are interested in it, read the paper, its pretty cool).
Doing the math, hosting 27B at NVFP4 (~23gb) with 2.3M total ctx (9 agents) matches the vram usage of ds v4 flash for the same 2.3M ctx (2.3 agents). the break point is 1.5M (6 27B agents) if you use full precision 27B.
To be clear, the qwen3.5 architecture (what 3.8 uses) is still considered decent in terms of KV efficiency, its just that dsv4f's architecture is SOTA in that space, and with the lower active params, you get better max kv scaling and higher speed serving that, if you have a lot of gpus.
- It's 7th (!!!) overall on the agentic index, above Terra.by colingauvin
- Strangely Qwen 3.8 Max isn't on their list, at all.by hadlock
- That is actually insane. In my opinion, the future is local AI: for most daily tasks, you absolutely don't need Fable level intelligence - you need Fable level agentic capabilities. And this model has (almost) just that. If we get a similarly capable MoE model in a few months (yes we will), it's going to be an utterly wild ride.by euazOn
- Thank you for writing this; I have delayed 3.8:27b because my ollama needs to be updated (and I'm too lazy); but if I can save thousands of dollars not buying GPU hardware – for Top-10 performance – yes I will unlazy myself and update local ollama software.
- China cleaned house these past few months. Kudos to them.
I would really like to see some open source US companies out there.
- Meta's Glimmer is cracked for local inference!by chr15m
- Why is it so small, but expensive?
Open Router
Input /M $0.45
Output /M $3.20
Cache read /M $0.05
Throughput 27 tps
It would be a very nice model at 200-300 tps and if it was dirt cheap. What's the limiting factor of optimizing speed and price for inference providers?
by f311a - more profits i guess, https://news.ycombinator.com/item?id=49323381by thenews
- That's my questions as well. DeepSeek v4 0731 is served dirt cheap and it needs 10 times more RAM.
- I read it somewhere recently that it's architecture does not allow serving as many concurrent requests as the deepseek models allow. Maybe that's why.by freakynit
- It's a dense model, so 27B active parameters to compute. Compare that to DeepSeek V4 Flash, which has only 13B active parameters (MoE).by AgentLemon
- For more context, this puts it on par with models like GLM 5.2 and GPT 5.6 Luna, which are far largerby anana_
- Which should tell you how useful these benchmarks are.by catigula
- We don't actually know how large they are, actually.
- Also with Qwen 3.8 being more token hungry than Luna, using around 2.3x tokens. Which hurts for local deployment.by nsingh2
- And more context:
Same score as the latest DeepSeek Flash 0731 which has 284B parameters! (13B active)
Its also the second best Qwen model, much better than Qwen 3.7 Max, but significantly below Qwen 3.8 Max.
by bertili - And to read the tea leaves a little:
3.8 actually performs slightly worse than 3.6 on AA-Omniscience Accuracy, which could imply that they traded out world knowledge for capability in other areas.
It also produces nearly twice as many tokens per task as 3.6 (and by extension, time), which may be a tradeoff required to achieve correctness at this parameter size.
by anana_ - The smaller these frontier-nearing models get, the more I'm reminded of https://en.wikipedia.org/wiki/Lottery_ticket_hypothesisby padolsey
- i think there definitely is some truth to this in terms of embeddings spaces, which is why i believe they are implemented by OpenAI/Anthropic in roughly highest import => least import bit order-- an overwhelming majority of the variance is in the first few hundred vector bits. i haven't actually tested this myself by manually truncating vectors, but it is my understanding that they generally speaking have this property.by keeganpoppen
- I wonder whether it’s possible to test the seed on a small model and then size it up on whatever worksby Havoc
- I have an internal automated benchmark, which roughly follows my workflow, and I've been testing various models on it, local and cloud. Qwen 3.8 27B did awesome. Its understanding is correct, research is better than e.g. glm's (and I like glm), and implementation is good and careful.
Qwen 3.8 27B doesn't look benchmaxxed. These "52 AA score" numbers feel real, which is surprising. I've been using it locally for a few days for other tasks as well. If not the speed, I'd be totally happy to use it as a daily driver instead of cloud models, it is that good.
--- (benchmark, to get an idea):
1. First, initial prompt which is not super precise - similar to how I'd write a task when talking e.g. to Opus. I'm describing an idea, and asking model to come up with some plan, and also to criticize the approach. Task is about implementing a particular pi extension. I'm checking if a model actually understands what I'm asking.
2. Then, as a follow-up, I ask to research alternative implementations, research UX of similar extensions, etc. It needs to do web searches, inspect open source codebases, read articles and papers, etc. I don't prompt to do this exactly, but I expect good models to figure out they need to do it.
3. Then, implementation.
Also, one finding: Q4 and Q8 seem to have very different behavior in this benchmark. Q4 produces 2-3x thinking in the end, and makes more turns - it seems it makes more mistakes, and needs effort to recover from them, while Q8 gets more things right in a first try. In the end, quality is roughly similar, but Q8 gets there much faster, especially the implementation (tried it several times). Could be a difference between concrete artifacts, or between runtimes, I don't know, but be careful - it seems the real-world experience with qwen 3.8 27B can be vastly different, depending on how it's set up.
Regarding DeepSeek 0731 vs Qwen 3.8 27B. On this benchmark, Qwen understand my intent better, it's better at research, and I also liked its implementation more. But: if you're more precise in what you ask, 0731 is also very good, and it's quite a lot faster on mac; raw speed is better, and it needs less thinking to get there. So, I'd say it's a tie in practice, both are awesome :)
by kmike84 - > I have an internal automated benchmark ... I've been testing various models on it, local and cloud
Once you send your benchmark to "cloud", I don't think you can rely on it being secret/private any longer.
by ignoramous - > So, I'd say it's a tie in practice, both are awesome :)
Which harness for the benchmark ?
You have previously commented on using OC/GLM. R u going to stock with it?
by algo_trader - I used Qwen 3.6 27B extensively (>1B tokens) and DeepSeek V4 Flash (the older one also 2B+ tokens).
And I just can't fathom that the new 3.8 beats the new DeepSeek V4 Flash (which, in my eyes, is one of the best everyday coding models).
What an insane release, and convenient size to use every day/locally.
but i will test this model extensivly.
by K0IN - It has double the active params.by JacobAsmuth
- > I used Qwen 3.6 27B extensively (>1B tokens) and DeepSeek V4 Flash
Were your opinions effected by the harness ?
DS is an amazing combo. It probably could only happen in China, not in current USA or EU (for different reasons)
by algo_trader