> In our testing, it costs up to 30% less per task than its predecessor.
> Sonnet 5.5 generates outputs 30%+ faster than Sonnet 5, making it our fastest Sonnet model to date.
This isn't enough. Sonnet 5 was arguably the most cost ineffective model ever released at the time of a release.
They need something competitive on speed and cost with Luna or Gemini Flash 3.8 (certainly they aren't getting to DeepSeek v4.1 Flash) - this is literally a year behind.
Anthropic continues to be a Fable/Opus only company. They're going to get left behind as workloads shift more and more to more cost-effective good-enough models. They're 10-100x behind in terms of speed and cost.
I've almost exclusively been using Anthropic for design and review, as it almost never makes sense to use any of their models for implementation (90%+ token usage) - except in the rare cases it's something too complex for a number of 10-100x cheaper models (and more importantly for me 5-10x faster, too).
For me, it's less about cost. I'm not doing anything that can't be done with a $200 subscription and minimal intelligence on what models to use. It's primarily about speed. I don't have an entire work day to give Opus / Sonnet a task that Flash can get done 95% as good in 30m.
This is YET AGAIN another Sonnet model that is just a FAR worse version of Opus at every part of the cost AND speed curve.
Hopefully they release a Haiku that actually has a reason for existing.
I always tell coworkers if they're gonna use Claude to just stick to only Opus and Fable. Sonnet is a waste of time that does a bad job at a bad price.
DeepSeek V4.1 Flash may be chatty but it's cheap, fast, and reliable. I'm not sure what the upside of Sonnet is supposed to be. Right now it feels like a trap.
Theoretically but I've used DeepSeek V4.1 Flash for several hundred millions of tokens already and it chews through tokens but it is surprisingly good at making it to the end.
MiMo V2.6 Pro I want to love, but I've hit three deathloops in a row. Either my luck is catastrophically bad, or someone needs to patch vLLM or something.
I am sure DeepSeek V4.1 Flash can deathloop, too, but so far it feels less prone to it than other models I've tried like GLM 5.3 Flash so, I'm impressed so far.
I always wonder what the deal with these failure modes are. Google, OpenAI and Anthropic seem to have found good enough workarounds, and I am surprised I don't hear more people talking about them. I thought maybe it was shitty broken providers on OpenRouter, but then I started making presets just for using only the upstream provider and found that no, really, the models do fail that way.
Which is a shame because on paper MiMo V2.6 Pro seems strong, but I haven't gotten through a hard task with it yet.
I did like GLM 5.3 Flash but it's just way too often I'd run it on some long running task and come back to it repeating the same tokens or tool calls endlessly, just doing nothing. It wasn't unusable, but I couldn't trust it. That's really frustrating and I think new models have to do better not just on benchmark scores but general reliability and user experience as well.
At some point Anthropic and OpenAI models definitely could fall into similar traps so I do think it is a solvable problem and likely not a reflection of the models themselves being bad. In this case it may indeed be a training bug of some kind, but I also suspect mitigations on the inference side are possibly lacking or not effective enough for the open models and their runtimes.
onlyrealcuzzo · · focus · HN ↗
> Sonnet 5.5 generates outputs 30%+ faster than Sonnet 5, making it our fastest Sonnet model to date.
This isn't enough. Sonnet 5 was arguably the most cost ineffective model ever released at the time of a release.
They need something competitive on speed and cost with Luna or Gemini Flash 3.8 (certainly they aren't getting to DeepSeek v4.1 Flash) - this is literally a year behind.
Anthropic continues to be a Fable/Opus only company. They're going to get left behind as workloads shift more and more to more cost-effective good-enough models. They're 10-100x behind in terms of speed and cost.
I've almost exclusively been using Anthropic for design and review, as it almost never makes sense to use any of their models for implementation (90%+ token usage) - except in the rare cases it's something too complex for a number of 10-100x cheaper models (and more importantly for me 5-10x faster, too).
For me, it's less about cost. I'm not doing anything that can't be done with a $200 subscription and minimal intelligence on what models to use. It's primarily about speed. I don't have an entire work day to give Opus / Sonnet a task that Flash can get done 95% as good in 30m.
This is YET AGAIN another Sonnet model that is just a FAR worse version of Opus at every part of the cost AND speed curve.
Hopefully they release a Haiku that actually has a reason for existing.
jchw · · focus · HN ↗
DeepSeek V4.1 Flash may be chatty but it's cheap, fast, and reliable. I'm not sure what the upside of Sonnet is supposed to be. Right now it feels like a trap.
eli · · focus · HN ↗
jchw · · focus · HN ↗
MiMo V2.6 Pro I want to love, but I've hit three deathloops in a row. Either my luck is catastrophically bad, or someone needs to patch vLLM or something.
I am sure DeepSeek V4.1 Flash can deathloop, too, but so far it feels less prone to it than other models I've tried like GLM 5.3 Flash so, I'm impressed so far.
I always wonder what the deal with these failure modes are. Google, OpenAI and Anthropic seem to have found good enough workarounds, and I am surprised I don't hear more people talking about them. I thought maybe it was shitty broken providers on OpenRouter, but then I started making presets just for using only the upstream provider and found that no, really, the models do fail that way.
Which is a shame because on paper MiMo V2.6 Pro seems strong, but I haven't gotten through a hard task with it yet.
eli · · focus · HN ↗
GLM 5.3 Flash is also very good. I think a little smarter and a little more expensive.
jchw · · focus · HN ↗
At some point Anthropic and OpenAI models definitely could fall into similar traps so I do think it is a solvable problem and likely not a reflection of the models themselves being bad. In this case it may indeed be a training bug of some kind, but I also suspect mitigations on the inference side are possibly lacking or not effective enough for the open models and their runtimes.