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- Hacker News
- I don't understand why I would really use this over using just a lower or even similar effort level on Opus, given that in many of the benchmarks it's basically the same cost, if not more, at any effort higher than medium.
Sure maybe it costs 30% less than Sonnet 5 but now it's basically neck and neck in most of the benchmarks it seems and in some of them it actually outcosts Opus.
Maybe I'm missing something but the announcement doesn't really seem to give much reason for the average person to even think about using this.
by Jcampuzano2 - The cost / performance chart shows that in almost all configurations, it looks worse than Opus. Why would you use Sonnet 5.5 on xhigh if you would get better results (higher score, cheaper cost) on Opus 5.5 high?
Is there a good use case? This isn't like Luna where it's much cheaper/effective just to use Luna in certain situations.
by wkcheng - It costs 20x more than the Chinese models I use. I just don’t need them anymore. Sure I’d use them if forced to for a job, but I don’t pay them outside of that anymore.
And my job won’t even pay for Claude now because it’s so ruinously expensive.
- Pelicans. Sonnet 5.5 has the same problem as Opus 5.5: on "max" thinking effort it burned through 128,000 thinking tokens (taking 15 minutes to do that) and ran out before it had produced the final SVG.
https://tools.simonwillison.net/markdown-svg-renderer?url=ht...
Here's how the thinking effort levels compare:
Low and medium both used 0 thinking tokens.low 27 input, 1,623 output, thinking_tokens: 0 1.6284 Duration: 10138ms (10s) medium 27 input, 1,796 output, thinking_tokens: 0 1.7914 cents Duration: 11266ms (11s) high 27 input, 2,334 output, thinking_tokens: 745 2.3394 cents Duration: 17376ms (17s) xhigh 27 input, 5,730 output, thinking_tokens: 2535 5.7354 cents Duration: 41882ms (41s) max (failed to return response) 27 input, 128,000 output, thinking_tokens: 128000 $1.28 Duration: 940617ms (15m 40s)by simonw - "Sonnet 5.5’s cyber capabilities are a large improvement over Sonnet 5’s, so we’re deploying it with safeguards similar to those on Opus 5.5. Users can still find and fix bugs in their code as part of routine software development, but higher-risk cybersecurity tasks will visibly fall back to Sonnet 5
Sounds like at least for Anthropic models we reached peak cyber capabilities with Opus 4.8. Everything after that falls back to worse models
by wongarsu - Sonnet 5.5 scoring higher (70.6) than Opus 5.5 (66.4) in Terminal-Bench is interesting. I looked into this, because it felt strange.
Turns out that Opus had 10% of its trials answered by a fallback model due to safeguards; versus only 1.5% fallbacks for Sonnet. [1] So I would not read too much into this, just the difference in fall backs could probably explain the gap.
[1] Section 8.5 of the Sonnet 5.5 System Card
by abejora - Unless you’re using frontier models like Astra, Sol, Fable, or Opus, I think you’re often better off using Chinese models for a fraction of the price. I’m not sure people realises just how competitive they’ve become.
GLM and DeepSeek are great examples. They’re a bit like Linux or Android in that there isn’t necessarily one best provider. You need to do some research, try a few, and pick whatever works best for your use case.
I think that’s partly why Anthropic has been pushing its most expensive models so heavily for a while now. Sonnet and Haiku were great, but at that level of intelligence it’s becoming much harder for them to compete on price with Chinese models that have largely caught up.
The main reason to use frontier models from Anthropic or OpenAI now is the combination of intelligence and speed. Chinese frontier models still struggle to match that, possibly in part because of hardware constraints. But judging by the recent GLM releases, they seem to be moving in the right direction.
by azuanrb - Probably a first world problem, but with Opus 5.5's efficiency, the limits on the 5x plan are simply sufficient for my everyday work, even when running 2-3 sessions at a time. So I wonder when I would use Sonnet 5.5.
More concurrency than that isn't really practical for me if I want to retain some semblance of understanding. Perhaps it's different for purely web app or frontend tasks, where the outcome is more relevant than the process, I don't have much experience there (and also don't want to belittle these domains, I might be underestimating their complexity).
So surprisingly, my own work is at least for the time being almost saturated by the model capabilities. I am not sure how I'd scale from here. Sure I could run all requests at max effort to burn tokens for the sake of it, but that can't be it. And for many tasks, I am not really able to define so clear cut success criteria or self-verification loops that I could benefit from letting an agent (or a fleet thereof) autonomously run for a day.
So I realize it's a skill issue on my side, but I can't be the only one. I wonder if there is a limit to token demand, at least short term. Feels like either they accelerate to AGI and RSI, where the AI can find uses for token, or things might plateau at some point.
Note I don't think this because I'm an AGI skeptic or think there's a ceiling to intelligence, but there might simply be a valley of economic hardship for the companies where the supply of tokens outpaces the demand, due to a lack of ideas of what to do with them. And this might slow down the funding enough that they never reach escape velocity with the training run scaling. But we'll see.
by Sol-