I like how they highlighted Opus 5 as the best for “Agentic Coding” even though the number is slightly lower than Fable. Close enough for marketing, I guess!
I like how they highlighted Opus 5 as the best for “Agentic Coding” even though the number is slightly lower than Fable. Close enough for marketing, I guess!
At half the price and less likely to auto-downgrade, it sounds like a reasonable claim.
> At half the price and less likely to auto-downgrade, it sounds like a reasonable claim
Two benchmarks (artificial analysis and vals) show a increase in cost (a insane increase for vals compared to Opus 4.8).
Already posted this before, so here is the link.
https://news.ycombinator.com/item?id=49041158
But better cost for the same performance. According to AA, Opus 5 _medium_ is as smart as Opus 4.8 _max_, at 1/3 the cost and twice the speed. And if you need a better response, you can turn it up to 11.
Then your comparing to a level of GPT 5.6 High, what is 50% cheaper then Opus Medium for the same intelligence / score.
You see the issue, if you try to scale effort down, you also need to compare how other competing models compare.
given that i couldn't even use fable without it downgrading to Opus, this is just a straight upgrade for me
Opus 5 also downgrades. it's now Fable -> Opus 5 ; Opus 5 -> Opus 4.8. Unclear why they want to nerf their own products with sometimes right classifiers. I guess the government ban might've been real and not coordinated marketing?
Best can describe multiple things.
Almost as good for half the cost is something I'm very comfortable describing that way.
> Almost as good for half the cost is something I'm very comfortable describing that way.
It's also not unusual in this context - many people describe the Chinese models as "best", because it's 80% as good for 20% of the price (or similar).
Hopefully it's not like old Opus, where it was actually more expensive than Fable cause it thought for half an hour, got it wrong, and then thought until you ran out of credits trying to come up with a correction, while Fable just went for it and did it in one go, getting it right the first time without thinking more than a few seconds.
Got an endless list of stuff done with Fable, Opus 4.8 was like a flailing braindead idiot in comparison. Maybe this one is a bit better if it's distilled.
Best marketing
The blog posts figure cites Frontier-Bench for its agentic coding score, and shows Opus 5 beating Fable 5 43.3% to 33.7%.
I think you're being overly cynical here. First, I don't see any claim that is the world's best model for agentic coding. Second, it is absolutely the best model in terms of coding performance vs. dollar, and it's raw performance seems very close to the frontier.
GPT 5.6 is far more token efficient at most tasks with similar performance. Especially so for Opus 4.8, still to be seen with Opus 5.
Where are you getting cheaper per dollar?
How are you supporting the claim that GPT 5.6 is "far more token efficient" than Opus 5? Tokens equal, output is cheaper for Opus 5 ($25/1M) than GPT-5.6-Sol ($30/1M), and it seems to outperform slightly on agentic coding benchmarks.
The first chart in the blog post shows a similar $/performance curve to GPT 5.6.
Where 5.6 has optionality to run much cheaper along the same performance curve at lower thinking levels.
There's a later chart that shows Opus 5 ahead, but seems like an esoteric benchmark rather than for common use. (Novel problem solving)
If they had a more efficient model at coding they would lead with that chart.
Here is one data point for cost:
https://artificialanalysis.ai/models?cost=intelligence-vs-co...
Here is another data point for output token efficiency:
https://artificialanalysis.ai/models?cost=intelligence-vs-co...
Token cost and token efficiency are two unrelated metrics, and anyways what really matters is neither in isolation - it's cost to complete a task.
https://www.anthropic.com/_next/image?url=https%3A%2F%2Fwww-...
It seems roughly equal according to Anthropic's benchmarks
It would still be the best model per dollar if the score was 2% lower instead of 0.1% lower. Would it be ok to still give it the highlight color then?
How big of a lie is too big? Especially when no lie needed to be told at all: many including myself would have noticed the tiny 0.1% deficit and been suitably impressed by the Opus 5 result.
I’ll admit this is a small deception by today’s standards. I’m one of those who believes in truth for truth’s sake.
Edit: typo
we don't know if it is 0.1% deficit, could be 0.05%
So highlight both then.
Which numbers are you seeing? It does show that it's better than Fable 5 in most things related to coding?
Yeah I spotted this immediately too. I'm sorry. You're supposed to be a multi billion dollar company and you can't even highlight your chart honestly?
In my opinion, the frontier is passed what is really needed for coding. Fable is good as a supervisor.
Also it scored worse on DeepSWE than chatgpt 5.6 sol
Using the most expensive model for all of your agentic coding work hasn’t been good practice for a long time. Not unless you have infinite money to spend.
Fable is typically used for key planning, architecting, and review tasks.
I think this is a case where you don’t understand the use case, not that the marketing department is making mistakes.
They cost the same if you're already at $200/mo
Fable consumes your usage at a higher rate.
If you bought the $200/mo plan and you don’t use it much, using Fable for everything is fine.
I am not a tokenmaxxer per se but I blow through my weekly quota on my max plan in 3-4 days… fable would make that worse.
Eh, not really. Fable does a lot better on coding than Opus 4.8.
Just this past week Fable was able to figure out a couple of small issues for me where Opus was failing to.
Also both are still somewhat bad at UI implementation. Opus more so
Recent releases have said something to the effect (paraphrasing here):
"Use <less expensive or older model> for everyday tasks and <other non-critical stuff>. Use <more expensive or recent model> for complex coding tasks, refactoring large code bases, etc.".
Then, the next model/release emerges and the previous "best for complex" gets demoted to "everyday".
Obviously, it's all relative. But, it does beg the question: was the previous model really good for complex coding tasks or no? I mean, how is it now suddenly only good for the "easy" stuff?
> I mean, how is it now suddenly only good for the "easy" stuff?
Because your expectations have changed.
I'm sure the marketeers would love for the public's assessment of complex versus easy to conveniently shift per their release cycles; or for the public to simply forget their prior marketing.
I mean that certainly makes it best-in-class