Can anyone give me the compelling reason for Terra and Sonnet to exist?
Virtually every task I give to either is better served by going up a tier and down a reasoning level, or down a tier and up a reasoning level. I simply don't understand their use case. No testing I've done has shown them to have a niche where there isn't a cheaper-with-equal-results or more-performant-at-same-cost option.
They are the biggest models supported in Github Copilot Pro
https://docs.github.com/en/copilot/reference/ai-models/suppo...
Idk… I’ve written 100k sloc in ~2 weeks on the Claude £20 subscription with sonnet medium alone. Seems pretty good to me.
You should look at model reasoning level curves on the cost/performance chart on artificial analysis. If you compare the curves for haiku/sonnet/opus I would agree with you that sonnet serves no purpose because it is priced too high. If you look at luna/terra/sol you will see that terra is priced such that it successfully fills a gap between luna and sol. And when you add speed into the equation it adds another dimension where terra makes sense. terra low falls between luna xhigh and max on both cost and intelligence, but it is way faster because it uses way fewer reasoning tokens. So I would say there are workloads where terra makes sense. But from my analysis I don't see any workloads where sonnet makes sense.
On the API we use a lot of Luna (cost efficient) and sometimes Terra level models. I think we never use a larger model. These are models that our app runtimes depend on.
On subscription for coding and stuff I am basically on Astra Ultra all day long.
Sonnet is way faster than Opus when doing everyday tasks, I think on the subscription plans it's a better option when you need something done faster than Opus' long thinking sessions.
There is a chasm of performance difference between haiku and sonnet ime. Sonnet is cheaper and faster than Opus, so if you're providing a service via api where cost and speed is important, it's a big difference.
For coding, no.
I have some business automation process for some customers that I maintain, where Terra with low reasoning ended up with the best price/performance & latency. With an equivalent Luna with high reasoning, price was usually better, but quality had a higher variance (very much depends on where the thinking takes the model) and latency for high reasoning was worse.
If you trust these benchmarks, I'd argue that there's almost never a reason to use anything other than Terra 6.1 xhigh.
I've been refused to do cybersecurity (read-only investigative access against the recent brotli cve) with Opus 5.5 when I had full SSH access, so I guess you've got to have a fallback?
I cannot figure it out either. I use Astra Ultra to plan and 6.1Sol High or X-High to build and even on huge projects with 10s of 1000s of pages it is fast and competent
If you use API in agent or your own integration with software, such models make a lot of sense. We use Terra 5.6 for "question to SQL" translation for example. It's good enough and affordable enough. Luna will probably not handle complex queries well while Sol is too expensive.
i use sonnet to generate answers to help-desk tickets, which it does for significantly cheaper and insignificantly worse than opus.
if you're just talking about use in codex or claude code, then sure, leave it on opus all the time. but for running routine api requests, sonnet is great.
Sometimes this tier is faster, and getting a good enough result in a few seconds instead of a better result in ~15-30 seconds is a meaningful result.
I think it's also a fake lever to give to finops / it teams to try and keep costs down.
> Can anyone give me the compelling reason for Terra and Sonnet to exist?
To justify the pricing of Sol and Astra. Old business trick.
I'm on Claude's default plan for personal use, and Sonnet is plenty enough for small projects while not hitting my usage limits too hard.
Cheap and affordable. Dispatching proper tasks to these models can save you a huge % of your quota. Especially if you _know_ the task is simple (ie add something here or there, or archive a bunch of files and reformat kinda thing)
I think for the majority of the audience here who use these models inside of coding agent harnesses w/ subsidized monthly subscriptions, there likely isn't one. Depending on how Terra contributes to the rate limits, it may make sense to use it for some subset of a repeated workflow to get more mileage out of sub.
If you pay API prices for inference in your product stack & have specific use-cases w/ custom evals, these smaller models provide an additional cost/speed lever to adjust. Ultimately, I think the point is to compete on cost for use-cases where Sol on the lowest reasoning is overkill.
If you have a fairly straightforward usecase where Terra performs just as well as Sol, but is faster and cheaper, why not use it?