Only thing I can think of is for when someone is out of opus credits. Of course there are API billing use cases but I'd probably still just use opus on low.

From the system card: "On CyberGym vulnerability discovery, Claude Sonnet 5 is less capable than Sonnet 4.6, and far less capable than Opus 4.8 and Mythos 5

As with the other evaluations in this section, these results were achieved with all safeguards turned off. When run with our default mitigations, Sonnet 5 scored a 0 on CyberGym"

I have been using Sonnet 4.6 more than Opus, because I'm mostly doing agent-assisted development and not fully agent-driven development. This announcement does not make me positive, I have found that the more models are optimized for fully agentic development, the worse they get at assisted development and often start doing too much despite very strict/specific instructions.

I have been moving more and more to K2.7 Code and GLM-5.2 the last few weeks. They are often good enough for assistance, very fast, and cheap.

Weak spots (categories it fails):

    - Trivia — 0/3 - basically not much built-in knowledge
    - Combined tool-calling tasks — score 45/100, sometimes makes invalid tool calls
    - Puzzle Solving — score 77, flubs carwash-like tests

> Illustration of a white goose riding a bicycle, with one wing extended forward to grip the handlebar, set against a plain white background with a brown ground line.

https://simonwillison.net/2026/Jun/30/claude-sonnet-5/

I've been using Sonnet instead of Opus for almost all coding tasks for a while now. A little elbow grease to break down tasks and you can spend a lot less money for just about the same output quality.

They changed the Sonnet 5 'Agentic search' benchmark graph overnight

They released Sonnet 5 with a temporary price reduction until August. Everyone was excited, but in reality, they increased the tokenizer size by 50%. As a result, the actual cost went up by 50%, they shifted everyone's attention to decrease.

Thus, Anthropic is raising prices but not telling anyone about it. Nobody is really aware of it. You go to the pricing page, the price looks the same. Yet people are actually paying 50% more.

Very shady marketing.

And of course they lie about 35% again. In reality with coding it is 50%.

UPD: I run playcode.io, so it’s my job test all models, their pricing, quality in order to provide best price/quality/speedy/reliability to non-techy.

Why would they brag about something like this? It's like they know people want to use models to perform cybersecurity tasks yet knowingly deny them the ability.

And Opus 4.8 is still cheaper for a higher pass rate (much less open weight models like GLM 5.2) so not sure why I'd use Sonnet except on the low effort level for I suppose trivial tasks where I want it to work only 50% of the time judging by the graph. The pricing doesn't really make any sense.

This line as a selling point is also pretty funny:

> Evaluations also show that it has a much lower ability to perform cybersecurity tasks than our current Opus models.

In effect, high reasoning only makes sense when you're using the frontier model and need extra performance (higher levels of reasoning are never pareto optimal unless you're at the largest model size).

> Sonnet 5 (medium): None of these past chats mention a sushi place — I don't have anything on record about that. Do you remember the name, or roughly which part of Columbus (neighborhood/street) it was near? That'll help me search and check its current status.

Not impressed. It got the name right on high effort one shot but hallucinated the date relativity (Jan 2026 is not last month...). Worked okay on extra. Sonnet 4.6 worked fine on medium, high, and extra one shot.

Edit: as the replies point out, the prompt is definitely ambiguous however Sonnet 5 didn't even extract the semantic meaning "looking for <place> near <compound place>" which all the human replies seem to understand. Even Haiku 4.5 identifies the semantic meaning although it fails to retrieve the correct results

> Haiku 4.5 (reasoning off) I'll help you find information about that sushi place. Let me search for sushi restaurants that were near latitude 41 in Columbus and check their current status.

>_Searched the web_

> I see that "Latitude 41" is a restaurant in Columbus, but it's actually a modern American restaurant, not specifically a sushi place. However, based on your mention of latitude 41, that's what came up. Let me search more specifically for sushi restaurants that may have closed in Columbus around that area.

It actually costs more per task than every other model. It's only cheaper than Claude Fable 5.

Source: https://artificialanalysis.ai/?cost=cost-per-task#price-and-..., as of writing this comment (the results are frequently changing)

I also like that the difference between low, medium, high, xhigh seems more spread, which is actually a good thing for people trying to tune applications. Running Sonnet 5 on low with the launch pricing makes this potentially a better fit than Haiku or open source models for some tasks. I don't think it will make sense at full price.

I struggle to understand where this model fits in. If I need a cheap model for simple stuff (like, summarizing an email); I'd go Haiku (actually, I'd go Deepseek v4 Flash, but you catch my drift). I just can't think of many tasks where I'm like "yeah let me reach for Sonnet Low Reasoning so I can save a dollar but also seriously run the risk of it failing"; I'd just reach for Opus Low.

Sonnet 5's performance is comparable to GLM 5.2 in both one-shot coding and agentic ability. However, it's about ~20% less verbose than GLM 5.2 in average code submission sizes, and uses fewer reasoning tokens, which reduces the cost gap and suggests it writes cleaner code. In practice, Sonnet 5 ends up being 40% more expensive and ~2x faster than GLM 5.2 in our evaluations (not 300% more expensive as the per-token pricing would suggest). Granted, GLM 5.2 is an extremely reasoning heavy model.

Overall, it's a solid release that gives Anthropic some standing in the price-conscious inference market.

Data at https://gertlabs.com/rankings

In my early tests tonight, Sonnet 5 is a LOT better out of the box. It's one-shotting complex instructions. It also recovered independently from bad instructions that led to an uninformative 400 error by using its schema-fetching tool to figure out there were was too much input.

If I have to gripe about something: it interpreted another impossible instruction by quietly discarding the input in question. But, the way it did it is... kinda exactly what anybody else would do, if they weren't in a position to change the implementation.

This is, obviously, early days but I'm impressed.

In other words, for certain tasks, Opus 4.8 is cheaper than Sonnet 5, and does better than Sonnet 5.

I've noticed this pattern on a lot of benchmarks. You can try to emulate a bigger model by ramping up the test time compute (max reasoning, more turns, model fusion etc.), but you can't reach the same quality level, and you often exceed the cost you would have paid by just using a bigger model.

tldr: if you're doing something hard, just use a bigger model.

or

The Dodge Charger is built to be the most Charger like car yet.

Today sonnet 5's med level effort is equivalent to sonnet 4.6 low level effort :/

Based upon the "Agentic Computer usage", Sonnet 5 Max was going to be off "Agentic Search results" chart. lol ...

In short, Sonnet 5 Low/Medium is more cost efficient, if its a task below Opus 4.8 Medium. For the rest its expensive and your better off using Opus 4.8.

Why even release this model?

Unfortunately that means I won't be using it at work for now.

cool to see, still waiting for models to get better at computer use.

It seems being incompetent is a feature now...

[0] https://github.com/dginovker/BFME-Source-Code/

Okay.

And yet, the $2-$5 section is the widest, even though it only contains a single point.

I can't even say if this is making the product look better or not, but it sure is weird. Maybe Claude just hallucinated those splits xD

- Do the ever increasing scores on the mean we will soon have models that approach 100%? And what would that even mean? That there is no more room for improvement?

- Would Anthropic (or any other model vendor for that matter) ever release a newer model that scores lower? If not, does that mean they keep tweaking a new model they want to release until it shows an improvement of the prior model?

- Would it be more useful to move toward a comparative rather than absolute ranking?

None of the other labs are doing this kind of long lived two model series.

Oh Claude you master of software engineering does it ever end? DO you have no bounds?

How may we further assist you oh Claude?

loads of trust me bro benchmarks

financially incentivized comments and upvote/downvoting patterns

it's all slop