Sonnet 5.5

https://tools.simonwillison.net/markdown-svg-renderer?url=ht...

Here's how the thinking effort levels compare:

  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)

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.

And my job won’t even pay for Claude now because it’s so ruinously expensive.

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

Sounds like at least for Anthropic models we reached peak cyber capabilities with Opus 4.8. Everything after that falls back to worse models

This is bollocks. Their safeguards are shit.

Is there a good use case? This isn't like Luna where it's much cheaper/effective just to use Luna in certain situations.

- Fable 5.1 for planning/adversarial reviewer

- Opus 5.5 for well-scoped tasks break down

- Sonnet 5.5 for these well-scoped tasks implementation

I think the blocker might be how efficient the context is compacted and sending around between these agents

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.

From looking at their Terminal-Bench graph, anything you would use level "high" or above for Sonnet it seems like you should consider using Opus instead.

OpenAI Luna is a lot cheaper. But DeepSeek seems smarter and the cost seems similar.

https://bench.killswitch-lang.org/

    Claude Sonnet 5    17.8%
    Claude Sonnet 5.5  7.4%

> 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.

On low and medium it seems competitive, maybe slightly cheaper than opus, in terms of intelligence per task.

If the time per task is lower (Artificial Analysis don’t have the date up at time of posting) then I have a clear use case for this model all other things being equal.

At this point it's almost comical how angry Anthropic makes HN. It's like the opposite of Apple's reality distortion field.

In xhigh effort it is a lot cheaper and possibly lot less impressive?

I bounce between ”fuck you, give me an AGI-approximate robot god” or ”how dare you charge me more than $0.04/million tokens”.

Give me the frontier, or give me the cheapest form of good enough.